What is DynamoDB?
Amazon DynamoDB is a database service provided by Amazon Web Services (AWS) that falls under the category of NoSQL, which stands for “not only SQL”. Unlike traditional SQL databases, NoSQL databases like DynamoDB are designed to scale horizontally and provide high flexibility and performance. DynamoDB is a platform as a service (PaaS) AWS product.
Key Features
Amazon DynamoDB is a fully-managed NoSQL database service provided by Amazon Web Service (AWS). Here are some of its key features:
Serverless Performance and Limitless Scalability: DynamoDB is a serverless NoSQL database service that supports key-value and document data models. It automatically scales to support tables of virtually any size with automated horizontal scaling. This means it can start small and scale globally to handle your application’s requirements without you having to manage any servers. It also provides consistent single-digit millisecond performance and up to 99.999% availability.
Security and Reliability: DynamoDB provides end-to-end security of its services and platforms and takes responsibility for securing the infrastructure that runs the DynamoDB services globally. It offers encryption at rest and in transit, built-in security, and authentication. It also provides high availability with its automatic scaling and replication. Moreover, DynamoDB complies with industry standards such as ISO 27001, ISO 27701, and PCI DSS to maintain security and secure operating standards.
Cost Effectiveness: DynamoDB offers two different capacity modes: On-Demand and Provisioned. With On-Demand capacity mode, DynamoDB charges you for the data reads and writes your application performs on your tables. With Provisioned capacity mode, you specify the number of reads and writes per second that you expect your application to require. You can use auto-scaling to automatically adjust your table’s capacity based on the specified utilization rate to ensure application performance while reducing costs.
Integration with AWS Services: DynamoDB integrates seamlessly with other AWS services, enhancing its capabilities. For instance, it can integrate with AWS Lambda to enable serverless architectures, allowing you to execute custom code in response to data modifications.
Fully Managed Service: As a fully managed service, DynamoDB eliminates the need for manual interventions in database administration. This includes hardware provisioning, setup and configuration, replication, software patching, and scaling.
NoSQL Data Model: In DynamoDB, data can be stored and retrieved as key-value pairs, documents, or wide column stores. This enables you to have a flexible schema, so each row can have any number of columns at any point in time.
Highly Available and Durable: DynamoDB is designed to be highly available and durable. Your data is replicated across multiple Availability Zones to ensure it is always accessible, even in hardware failures.
Flexible Querying: DynamoDB supports various ways to query your data, including key-value lookups, range queries, and more. This allows you to retrieve the data you need quickly and efficiently.
On-Demand and Provisioned Throughput: With DynamoDB, you can choose between on-demand and provisioned throughput. On-demand throughput is ideal for applications with unpredictable workloads, while provisioned throughput is a good option for applications with more predictable workloads.
Benefits
Amazon DynamoDB is a popular NoSQL database service with several benefits:
Scalability: DynamoDB is designed to provide virtually unlimited storage and automatically scales to meet your application’s data requirements. It uses auto-scaling to adjust capacity in response to traffic patterns, which helps optimize costs and maintain performance. DynamoDB also uses partitions to distribute data and traffic for tables over multiple servers to handle throughput and storage requirements.
Seamless Data Replication: DynamoDB replicates data across multiple Availability Zones in a single region to ensure high availability and data durability. It also supports global tables, which replicate your data across multiple regions. This allows for fast, localized read and write performance, and helps your applications stay highly available even in the unlikely event of isolation or degradation of an entire region.
Fully Managed (Serverless): As a fully managed service, DynamoDB eliminates the need for you to worry about hardware provisioning, setup, and configuration, replication, software patching, or cluster scaling. This allows you to focus on building your application without managing the underlying infrastructure.
Secure: DynamoDB provides robust security measures, including encryption at rest and in transit, identity, and access management (IAM), and compliance with globally recognized regulatory standards like PCI DSS, HIPAA, and NIST.
Fast Response Times: DynamoDB is designed to deliver fast, consistent performance at any scale. It offers single-digit millisecond response times, and with the DynamoDB Accelerator (DAX), it can even deliver microsecond response times for accessing eventually consistent data.
Flexible Schema: Unlike traditional relational databases that require a predefined schema, DynamoDB is schema-less. This means each item in a table can have a different set of attributes. This flexibility allows you to evolve your data model over time to meet changing application requirements.
ACID Transactions: DynamoDB supports ACID (Atomicity, Consistency, Isolation, Durability) transactions. You can group multiple actions and submit them as a single all-or-nothing operation, ensuring data integrity.
Active-active Replication with Global Tables: DynamoDB Global Tables provide automatic multi-active replication to AWS Regions worldwide. This means when you operate on a DynamoDB table in one region, it automatically propagates to all other regions where that table is present.
Availability and Fault Tolerance: Designed with high availability in mind, DynamoDB replicates your data across multiple zones within a region by default. This redundancy ensures that your data remains accessible in case of hardware failures or outages. Additionally, DynamoDB offers global tables, enabling data replication across geographically distinct regions for even greater disaster resilience.
Drawbacks
Amazon DynamoDB is a popular NoSQL database service, but it has its drawbacks. Here are some of the main disadvantages of DynamoDB:
Limited Querying Options: DynamoDB is a NoSQL database and it doesn’t support complex querying like SQL databases do. You can use the Query API operation in DynamoDB to find items based on primary key values. However, this can be limiting if your application requires more complex queries. For example, if you put a limit on a query, it doesn’t mean that the query will return the first few values. It just says that query for a few items on the table (in any order), so you may get fewer items than the limit.
Difficult to Predict Costs: DynamoDB’s pay-per-use model means you’re not paying for idle resources, which is perfect for applications with unpredictable workloads. However, estimating the cost can be tricky. Factors influencing DynamoDB cost include data storage, write and read units, deployment region, provisioned throughput, indexes, global tables, backups, and more. Write-heavy, latency-sensitive workloads are typically the main contributing factors to high bills.
Unable to Use Table Coins: DynamoDB does not support table joins. A join requires the DBMS to scan several tables and perform complex processing to aggregate the data to return a result set. However, DynamoDB allows you to mimic a “join” by modeling the data with the single table design principle.
Limited Storage Capacities for Items: DynamoDB only allows a maximum size of 400KB per item. This can be a limitation if you need to store larger items. If your application needs to store more data in an item than the DynamoDB size limit permits, you can try compressing one or more large attributes or breaking the item into multiple items.
On-Premise Deployments: DynamoDB is a cloud-based service, and it does not support on-premise deployments. Although DynamoDB does not offer an on-premise deployment for production environments, it offers an on-premise deployment for development or testing. However, this deployment does not have the same high speeds we expect from DynamoDB and is strictly only for testing.
Lack of Transparency: There can be a lack of transparency into what the database is doing under the hood. For example, DynamoDB provides deletion protection to protect your table from being accidentally deleted. However, the AWS SDK doesn’t include the logic needed to actually implement distributed locks.
Cloud Vendor Lock-in: If you’re using DynamoDB, you’re tied to AWS, which can be a concern for some organizations. The vast majority of decisions to move away from DynamoDB boil down to two critical considerations: cost and cloud vendor lock-in.
Data Modelling Challenges: Shifting from a relational model to DynamoDB’s NoSQL approach requires careful data modeling to ensure efficient retrieval.
Lack of ACID Transactions: DynamoDB doesn’t support full ACID (Atomicity, Consistency, Isolation, Durability) transactions, making it less suitable for scenarios requiring strict data consistency.
Applications
Amazon DynamoDB is a popular cloud-based NoSQL database service that provides reliable, scalable, and highly available databases. Here are some common use cases:
Logging: DynamoDB is a great choice for logging due to its flexibility and scalability. You can store application logs in DynamoDB, which provides fast, reliable, and relatively inexpensive storage. It also offers unique functionality in its global tables feature, which allows you to maintain a localized logging database for each multi-region application instance. This means you can simply add extra properties to your log without needing to update or define your schema.
Analytics: DynamoDB can store all the metrics from multiple data sources, such as IoT sensors, web applications, etc. When coupled with a well-designed big data analytical platform, it allows you to enrich data without worrying about resource constraints due to DynamoDB’s auto-scaling feature. This makes it ideal for storing and analyzing large volumes of data in real-time.
Cache: AWS provides DynamoDB Accelerator (DAX), a natively compatible in-memory caching service for DynamoDB. DAX reduces the single-digit millisecond latency to microseconds, ensuring that the request does not even have to go to the DynamoDB database. This is particularly useful for read-intensive applications where reducing latency is critical.
DynamoDB Streams: DynamoDB Streams capture a time-ordered sequence of item-level modifications in a DynamoDB table and durably store the information for up to 24 hours. This can be used to set up a relationship across multiple tables, trigger an event based on a particular item change, audit or archive data, and replicate data across multiple tables. It’s particularly useful for real-time dashboards or data replication.
Software Application Development: DynamoDB helps in building internet-scale applications supporting user-content metadata and caches that require high concurrency and connections for millions of users, and millions of requests per second. It’s particularly useful for mobile apps, gaming, digital ad serving, live voting, audience interaction for live events, and sensor networks.
Media Metadata Stores: DynamoDB can be used to create media metadata stores. For example, Dropbox used DynamoDB to develop a new managed storage system called Alki, which made room for virtually unlimited user metadata and saved the company millions of dollars.
High Throughput: Traditional databases can struggle with massive read/write requests in real-time applications. DynamoDB’s distributed architecture allows it to handle high volumes of concurrent operations, ensuring smooth performance for your users. This is crucial for applications like online auctions, stock trading platforms, or live chat features.
Scalability on Demand: Real-time apps can experience unpredictable surges in activity. DynamoDB automatically scales storage and throughput capacity to meet these demands without sacrificing speed. You only pay for what you use, so there’s no need to over-provision resources during low-traffic periods.
Flexible Schema: Unlike traditional databases with rigid schemas, DynamoDB offers flexible schema design. This means you can store various data types and structures from different sources without worrying about upfront schema definition, making it perfect for diverse data sets from IoT sensors or web applications.
Cost-Effective Management: Big Data can quickly become expensive to store and manage. DynamoDB’s pay-per-use model ensures you only pay for the storage and throughput you actually utilize. Additionally, its integration with AWS’s big data analytics services allows for efficient data processing and analysis.
Mobile Backends: DynamoDB’s flexible schema and ability to handle unpredictable workloads make it suitable for storing user data, game state, or other frequently accessed information in mobile applications.
Social Media: Social media platforms are prone to sudden spikes in activity due to viral trends. DynamoDB’s elastic scaling automatically adjusts to these surges, preventing downtime or slowdowns during peak usage periods.
Architecture of DynamoDB
Amazon DynamoDB is a NoSQL database service provided by Amazon Web Services (AWS). It’s designed for high availability, durability, and consistently low latency. Here’s an overview of its architecture:
Key-Value Store: DynamoDB operates fundamentally as a key-value store. It’s akin to a persistent hash-map. The primary operations it supports are Get and Put, which allow you to retrieve and store key-value pairs respectively.
Tables, Items, and Attributes: Data in DynamoDB is organized into tables, items, and attributes. Each table contains multiple items, and each item consists of one or more attributes. Primary keys are used to uniquely identify items in a table, while secondary indexes provide additional querying capabilities.
Partitioning: DynamoDB employs partitioning for horizontal scaling. Data is distributed across different partitions, each hosted on a separate machine. As data volume increases, DynamoDB can create more partitions and allocate more machines to host these partitions.
High Performance: DynamoDB stands out for its high performance. It can handle over 10 trillion requests per day, peaking at over 20 million requests per second, and can scale horizontally to support virtually any size.
Managed Service: DynamoDB is a managed service offered by AWS. It runs on a fleet of AWS-managed servers that use solid state drives (SSDs) to create a high-density storage platform. This setup decouples performance from table size, allowing for consistent, low-latency responses to queries regardless of whether the working set of data fits in memory.
Multi-Tenant Architecture: DynamoDB uses a multi-tenant architecture. It stores data from different customers on the same physical machines to maximize resource utilization.
How does DynamoDB Work?
DynamoDB is a NoSQL database service offered by Amazon Web Services (AWS). Unlike traditional relational databases that use SQL, DynamoDB uses a key-value store model with a twist: it also supports documented-oriented data structures.
DynamoDB provides fast and predictable performance with seamless scalability. It’s designed to handle large amounts of data and traffic with ease.
When data enters DynamoDB, it’s first distributed into different partitions by a process known as hashing on the partition key. Each partition can store up to 10GB of data and handle by default 1,000 write capacity units (WCU) and 3,000 read capacity units (RCU). This distribution of data helps in managing the data efficiently and allows for quick access.
One of the key features of DynamoDB is its scalability and performance. It takes away the administrative burdens of operating and scaling a distributed database. This means you don’t have to worry about hardware provisioning, setup and configuration, replication, software patching, or cluster scaling. You can easily scale up or scale down your tables’ throughput capacity without any downtime or performance degradation.
DynamoDB also ensures the high availability and durability of your data. All data is stored on solid-state disks (SSDs) and is automatically replicated across multiple Availability Zones in an AWS Region. This provides built-in high availability and data durability. If you need to keep DynamoDB tables in sync across AWS Regions, you can use global tables.
Security and backup are also taken care of in DynamoDB. It offers encryption at rest, which eliminates the operational burden and complexity involved in protecting sensitive data. DynamoDB provides on-demand backup capability. It allows you to create full backups of your tables for long-term retention and archival for regulatory compliance needs. You can create on-demand backups and enable point-in-time recovery for your Amazon DynamoDB tables.
Lastly, DynamoDB allows you to delete expired items from tables automatically. This feature helps you reduce storage usage and the cost of storing data that is no longer relevant.
When Should I Use DynamoDB?
Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. This means that if your application or service needs to handle a large amount of data and traffic, DynamoDB can scale up or down according to your needs without any manual intervention. This makes it a great choice if you’ve previously experienced scalability issues with traditional database systems.
If you’re developing an application or service, DynamoDB could be a good fit. Its simplicity and performance make it a popular choice for developers. It’s not just for small-scale operations, though. DynamoDB also excels at handling ultra-high-scale operations.
One of the major advantages of DynamoDB is that it takes care of a lot of the administrative tasks that come with managing a database. This includes things like hardware provisioning, setup and configuration, replication, software patching, and cluster scaling. By offloading these tasks to DynamoDB, you can focus more on developing your application and less on managing your database.
Finally, DynamoDB is incredibly versatile when it comes to the types of data it can handle. It can be used as a simple key-value store, which is great for storing metadata. But it can also handle relational data using the Adjacency List Pattern Single Table Design. If you need to store geographical data, you can use Geohashing with DynamoDB. And if you’re working with timeseries data, you can use multiple tables per period. It can even be used for caching.
DynamoDB Pricing
Amazon DynamoDB has two main pricing options:
On-Demand Capacity Mode: This mode is designed to offer flexible pricing. You only pay for the read-and-write requests that your application performs on your tables. There’s no need to estimate the expected read and write throughput, as DynamoDB automatically scales to match your application’s requirements. This mode is particularly useful if:
- You’re creating new tables with unknown workloads.
- Your application traffic is unpredictable.
- You prefer to pay only for what you use.
Provisioned Capacity Mode: In this mode, you specify the number of reads and writes per second that you expect your application to require. You can use auto-scaling to automatically adjust your table’s capacity based on the specified utilization rate. This helps to maintain application performance while reducing costs. This mode is ideal if:
- Your application traffic is predictable.
- Your application’s traffic is consistent or ramps up gradually.
- You can forecast capacity requirements to control costs.
In addition to these, other factors can affect the cost:
- Storage Costs: DynamoDB charges for data storage, as well as the storage of any indexes associated with your tables.
- Data Transfer Costs: There may be costs associated with transferring data in and out of DynamoDB.
- Backup and Restore Costs: DynamoDB has features that enable you to backup and restore your data. There are costs associated with these features.
- Global Tables Costs: If you’re using DynamoDB Global Tables to replicate your tables in multiple regions, there are additional costs.
- DynamoDB Streams Costs: If you’re using DynamoDB Streams to capture table activity, there are additional costs.
Remember, the AWS Pricing Calculator can help you estimate your monthly costs. It considers your read and writes throughput, as well as other chargeable options like data import/export to Amazon S3, backup and restore, and change data capture.
Lastly, DynamoDB is available within the AWS Free Tier, which offers up to 25GB of storage and up to 200 million read/write requests per month.
History
DynamoDB draws its lineage from the influential Dynamo white paper, authored by Amazon developers in 2007. This paper responded to Google’s Bigtable model, emphasizing different design principles. Initially, DynamoDB was an internal, proprietary solution used exclusively within Amazon.
In 2012, Amazon unveiled DynamoDB to the public. This managed NoSQL database service was engineered to handle demanding workloads while dynamically scaling based on application requests.
In 2024, DynamoDB underwent significant changes. It introduced two capacity modes: on-demand and provisioned. These modes allow developers to tailor capacity provisioning to their specific needs.
In March 2024, DynamoDB added support for resource-based policies. These fine-grained access controls apply to tables, indexes, and streams, defining who can access each resource and their permissible actions.
In April 2024, NoSQL Workbench received updates, including native dark mode support, improved table and item operations, and availability of item results and operation builder request information in JSON format.
DynamoDB now supports AWS PrivateLink, simplifying private network connectivity between virtual private clouds (VPCs), DynamoDB, and on-premises data centers using interface VPC endpoints and private IP addresses.
Competitors
Google Cloud Firestore
Google Cloud Firestore and Amazon DynamoDB are both NoSQL database services offered by major cloud providers, but they have some key differences. Firestore lets you store data more naturally, with nested structures like documents within documents. This makes it easier to work with complex information that can change over time. DynamoDB, on the other hand, requires you to define your data structure upfront, which can be less flexible for evolving data.
Firestore automatically scales its resources based on how much you use it. This is perfect for situations where your workload fluctuates, so you only pay for what you need. DynamoDB offers you more control. You can choose on-demand pricing similar to Firestore, or you can reserve a specific amount of capacity for a fixed cost, which can be more economical for consistent workloads. Both services offer on-demand pricing, but DynamoDB might have a slight edge here. Firestore takes into account several factors like reads, writes, storage, and bandwidth, while DynamoDB focuses on read and write capacity units.
While Firestore prioritizes ensuring your data is always available and consistent, it might not be the fastest option for situations where every millisecond counts. DynamoDB can achieve very quick response times, but there might be a slight chance of encountering inconsistencies in your data. Firestore integrates smoothly with other Google Cloud Platform and Firebase tools, making it a good choice if you’re already in that ecosystem. DynamoDB, on the other hand, is designed to work well with other AWS services.
Ultimately, the best choice depends on your project’s specific requirements. If you need a flexible data model, easy scaling, and tight integration with Firebase, Firestore is a great option. If low latency and precise cost control are your top priorities, especially for predictable workloads, then DynamoDB might be a better fit.
Here’s a table summarizing the key points:
Feature | Firestore | DynamoDB |
Data Model | Flexible document model | Table-based with primary keys |
Scalability | Autoscaling | On-demand and provisioned capacity |
Pricing | On-demand (reads, writes, deletes, storage, bandwidth) | On-demand and provisioned capacity |
Performance | Lower latency not prioritized (availability & durability) | Can achieve sub-10 millisecond latencies |
Integration | Firebase and GCP services | AWS services |
Azure Cosmos DB
Azure Cosmos DB and DynamoDB are head-to-head competitors in the realm of NoSQL databases, both offering advantages for building modern applications. Let’s delve deeper into their strengths and weaknesses to understand which might fit your project better.
Scalability on Demand: Both services excel at automatically scaling up or down based on your data access needs. This ensures your database can handle bursts of activity without sacrificing performance or incurring unnecessary costs.
High Availability for Peace of Mind: Downtime is the enemy of any application, and both Cosmos DB and DynamoDB prioritize keeping your data constantly accessible. They achieve this by geographically distributing your data storage, meaning if one region experiences an outage, your data remains available in others.
NoSQL Flexibility: Unlike traditional relational databases with rigid schemas, these NoSQL solutions embrace flexible data structures. This allows you to store a wider variety of data types and lets your schema evolve as your application grows.
Querying Differences: This is a key area where the two services diverge. If your development team is familiar with SQL, Azure Cosmos DB offers a significant advantage. It supports SQL-like queries, making it easier to retrieve and manipulate data. DynamoDB, on the other hand, utilizes a proprietary query language that requires developers to write more specialized code.
Cost Considerations: Regarding pricing, there are some nuances to consider. Cosmos DB provides a pay-per-request model, ideal for applications with fluctuating data access patterns. You only pay for the resources you use. However, it also offers a provisioned throughput option for predictable workloads. DynamoDB leans more heavily on provisioned throughput, which can be more cost-effective for workloads with consistent access patterns, but might lead to overspending for bursty workloads.
Integration Ecosystem: Both services integrate well with their respective cloud platforms. If you’re already heavily invested in Azure services, Cosmos DB offers a smooth integration experience. Similarly, DynamoDB seamlessly connects with other AWS services, making it a natural choice for AWS-centric environments.
Making the Right Choice: Selecting the best service depends on your specific requirements. Here’s a breakdown to help you decide:
- SQL Familiarity and Fluctuating Workloads: If your developers are comfortable with SQL and your data access patterns are unpredictable, then Azure Cosmos DB’s SQL-like queries and pay-per-request model could be a major advantage.
- Cost-Effectiveness and Predictable Access: For applications with a well-defined data access pattern and a focus on cost optimization, DynamoDB’s provisioned throughput pricing might be more economical.
Here’s a table summarizing the key points:
Feature | Azure Cosmos DB | DynamoDB |
Scalability | Automatically scales | Automatically scales |
High Availability | High availability with geographically distributed storage | High availability with geographically distributed storage |
NoSQL Flexibility | Flexible schema | Flexible schema |
Querying | SQL-like queries | Proprietary query language |
Cost Considerations | Pay-per-request or provisioned throughput pricing model | Leans toward provisioned throughput pricing model |
Integration Ecosystem | Integrates well with Azure services | Integrates well with Azure services |
MongoDB
Both MongoDB and DynamoDB are strong contenders in the NoSQL database arena, but they shine in different scenarios. Let’s explore their key distinctions to understand which one best suits your needs.
MongoDB offers unparalleled deployment freedom. You can run it on your own servers (on-premise), leverage various cloud platforms for hosting, or opt for a fully managed service like MongoDB Atlas. This flexibility allows you to tailor your database deployment to your specific infrastructure and budget. In contrast, DynamoDB is tethered to the AWS cloud ecosystem. While a local version exists for development purposes, production deployments are restricted to AWS.
When it comes to data structure, MongoDB embraces a document-oriented approach. Imagine storing data in JSON-like documents with rich schema support. This flexibility allows you to evolve your data model without significant roadblocks. On the other hand, DynamoDB operates primarily as a key-value store. It can optionally handle JSON document structures, but the schema design is more rigid, and data types are limited. This can lead to additional complexity in your application logic if you need to manage diverse data types.
If complex queries are your game, MongoDB reigns supreme. It boasts a powerful query language that empowers you to craft intricate queries, leverage aggregation pipelines for data manipulation, and even perform joins between documents. This makes it ideal for situations where you need to extract insights from your data through sophisticated analysis. DynamoDB, on the other hand, prioritizes simplicity. It excels at fundamental key-value lookups and table scans. For more intricate queries, you might find yourself exporting data to a different system for analysis.
Scaling your database to handle growing data volumes is crucial. MongoDB requires manual sharding for horizontal scaling, which can involve some administrative overhead. DynamoDB takes a different approach – it boasts automatic scaling based on your workload. This means your database seamlessly scales up or down to meet your application’s demands without manual intervention.
Managing a self-hosted MongoDB deployment comes with its own set of responsibilities. You’ll need to handle server maintenance, backups, and security configurations. Thankfully, MongoDB Atlas, the managed service, takes care of these complexities, allowing you to focus on your application development. DynamoDB, being a fully managed service by AWS, eliminates the need for in-depth database administration. AWS handles all the heavy lifting, freeing you to concentrate on your application logic.
The cost structure of these databases also differs. MongoDB Atlas follows a fixed pricing model based on provisioned resources. With self-hosted deployments, your costs are tied to server infrastructure. DynamoDB, on the other hand, operates on a pay-per-use model. You’re charged based on the consumed capacity units, making it a cost-effective option for fluctuating workloads.
MongoDB is your champion if you require a flexible data model, have complex querying needs, and schema evolution is a priority. It shines in content management systems, mobile app development, and scenarios demanding powerful data manipulation capabilities.
DynamoDB takes the crown if you prioritize a simple data model, require exceptional scalability for high-performance workloads, and are already invested in the AWS ecosystem. It excels in serverless applications running on AWS.
Here’s a table summarizing the key points:
Feature | MongoDB | DynamoDB |
Deployment | Flexible (on-premise, cloud platforms, managed service) | AWS cloud platforms only |
Data Model | Document-oriented (JSON-like, rich schema) | Key-value store (optional JSON document structure) |
Querying | Powerful (complex queries, aggregation, joins) | Simple (key-value lookups, table scans) |
Scalability | Manual sharding for horizontal scaling | Automatic scaling based on workload |
Management | Self-hosted (overhead) or managed service (Atlas) | Fully managed by AWS |
Cost | Fixed pricing (Atlas) or server costs (self-hosted) | Pay-per-user (consumed capacity units) |
Cassandra
Suppose you have a massive warehouse to store information. Both Cassandra and DynamoDB are like these warehouses, but each one is optimized for slightly different situations.
Cassandra offers a more flexible storage system. It’s like having a warehouse with many different sections and shelves to categorize your items. You can put similar things together, but you also have the freedom to arrange the best for you. This allows Cassandra to handle complex data structures, like information with many attributes. Additionally, Cassandra gives you much control over how your data is replicated and accessed. This is useful if you have very specific requirements for your information’s availability and consistency. On the flip side, this flexibility also means you’ll need to manage the warehouse yourself, which can be more work.
DynamoDB, on the other hand, is a more streamlined warehouse solution. It uses a simpler organization system, like having large bins for different categories of items. This makes it easier to set up and use, especially if you’re not familiar with managing complex data structures. Additionally, DynamoDB is like a fully-managed warehouse. You tell it what you need to store, and it takes care of all the back-end operations, freeing you to focus on other tasks. This can be a big advantage if you have fluctuating workloads or cost is a major concern. However, DynamoDB offers less flexibility in how you organize your data and how you access it.
So, which database system is right for you? If you have a very specific way you need to organize your information and require strict control over its access, then Cassandra’s flexible storage and control options might be a good fit. On the other hand, if you prioritize ease of use, automatic scaling, and cost-effectiveness, then DynamoDB’s managed service approach might be more suitable.
Here’s a table summarizing the key differences between Cassandra and DynamoDB:
Feature | Cassandra | DynamoDB |
Data Model | Column-oriented (flexible) | Key-value/document (simpler) |
Management | Self-managed | Fully-managed |
Scalability | Linear (add more nodes) | Automatic |
Consistency | Fine-grained control | Two options (strong or eventual) |
Cost | Pay per mode | Pay per use |
Latency | Lower | Higher |
Google Cloud Bigtable
Choosing between Google Cloud Bigtable and Amazon DynamoDB depends on the specific needs of your application. DynamoDB offers a key-value store with the option to store data in JSON documents. You access data using a primary key, similar to a hash table. This makes it efficient for simple lookups based on a single key.
On the other hand, Bigtable utilizes a wide-column store. Data is organized into rows, which are further divided into column families and individual columns. Each cell within a column family can hold a timestamped value. This structure allows efficient retrieval of related data groups, particularly when working with time-series data or data with complex relationships between attributes.
Both Bigtable and DynamoDB are schemaless. This means you don’t need to define a rigid structure for your data upfront. You can add new columns or attributes to existing rows as needed, offering flexibility for evolving data models.
In DynamoDB, individual data items are limited to 400 KB. If you have larger data objects, you’ll need to work around this limitation by splitting the data across multiple items or using DynamoDB Streams for real-time updates.
Bigtable offers significantly more space for individual data elements. Each cell can hold up to 100 MB, and a single row can support up to 256 MB. This makes it ideal for storing large objects like images, videos, or sensor data.
DynamoDB is a fully serverless offering. You simply configure the expected read and write throughput for your table, and DynamoDB automatically scales the underlying infrastructure to meet those demands. This is a hands-off approach that simplifies management.
Bigtable is a managed service, but it’s not entirely serverless. You provision instances with a specific number of nodes, and scaling requires manual adjustments. This gives you finer control over resource allocation but requires more management overhead.
Here’s a quick table summarizing the key points to help you decide:
Feature | DynamoDB | Google Cloud Bigtable |
Data Model | Key-value store (optional document support) | Wide-column store |
Schema | Schemaless | Schemaless |
Data Size Limit per Item | 400 KB | 100 MB per cell, 256 MB per row |
Management | Serverless (automatic scaling) | Managed service (manual scaling) |
Ideal Use Cases | Simple lookups, data under 400 KB, existing AWS env. | Large data objects, time-series data, granular scaling |
Couchbase
Choosing a NoSQL database comes down to finding the right fit for your application’s needs. While both Couchbase and DynamoDB are strong contenders, they cater to different scenarios.
DynamoDB takes a key-value approach, where each piece of data is identified by a unique key. This is efficient for simple lookups, but complex data structures require multiple key-value pairs. Couchbase, on the other hand, stores data in JSON documents. These documents can hold rich and interconnected information, making them ideal for scenarios where data has a complex structure.
DynamoDB prioritizes scalability and performance. By default, it offers eventual consistency, meaning writes might not be immediately reflected across all copies of the data. This is suitable for situations where occasional lags are acceptable. Couchbase, on the other hand, ensures strong consistency by default. Every read operation guarantees that you’ll see the latest data, making it a better choice for applications requiring strict data integrity.
Both databases are known for their speed, but Couchbase has a built-in caching layer. This layer acts like a fast-access memory, storing frequently accessed data for quicker retrieval. While DynamoDB can also achieve high performance, it relies more on the underlying storage infrastructure.
DynamoDB is a managed service offered by Amazon Web Services (AWS). This means AWS takes care of setup, maintenance, and scaling, making it very user-friendly. Couchbase offers more deployment options. You can deploy it on-premises, in the cloud (including on various cloud providers besides AWS), or even in a hybrid model. This flexibility comes with the responsibility of managing the database yourself.
DynamoDB uses a pay-per-use pricing model based on reads, writes, and storage consumed. This can be cost-effective for low-traffic applications but might become expensive for high-volume scenarios. Couchbase offers an open-source version, making it a more budget-friendly option for certain use cases. However, the commercial edition provides additional features and support.
If your application prioritizes scalability, ease of management, and cost-efficiency for predictable workloads, and you can tolerate eventual consistency, then DynamoDB is a great choice.
If your application demands strong consistency, complex data structures, flexible querying capabilities, peak performance, and you have the resources to manage deployments, then Couchbase might be a better fit.
Here’s a table summarizing the key points:
Feature | DynamoDB | Cocuhbase |
Data Model | Key-value | Document (JSON) |
Consistency | Eventually consistent (default) | Strongly consistent (default) |
Performance | Highly scalable | Potentially faster with built-in caching |
Management | Managed service (SaaS) | On-premises, cloud, or hybrid deployment |
Cost | Pay-per-use based on workload | Open-source and commercial editions are available |
Redis
Redis and DynamoDB are both NoSQL databases that offer high performance and scalability, but they target different use cases. Here’s a detailed comparison to help you pick the right tool for the job.
Redis shines in applications that demand lightning-fast data access. Since it stores data primarily in memory, retrieval times are measured in microseconds. This makes it ideal for:
Caching: Store frequently accessed data from a primary database to reduce load times.
Leaderboards: Update and display rankings in real-time for applications like games or social media.
Real-time Analytics: Process and analyze high-velocity data streams for fraud detection, stock markets, etc.
Redis’s strength lies in its versatility. It supports a rich set of data structures beyond simple key-value pairs. You can store complex data models like Hashes (maps), Lists (ordered collections), Sets (unique elements), Sorted Sets (ordered sets with scores), and more. This flexibility allows you to efficiently model and manipulate your data.
However, Redis has some drawbacks. By default, it stores data in memory, which means a server restart can lead to data loss. While persistence options exist to save data to disk, they introduce performance overhead. Scaling Redis also requires manual sharding (partitioning data across multiple servers) and managing those servers, which can be complex. Additionally, depending on the deployment option (cloud vs self-hosted), costs can be higher compared to DynamoDB.
DynamoDB excels in handling massive datasets and scaling seamlessly to accommodate ever-increasing traffic. It’s a fully managed service offered by AWS, so you don’t have to worry about server provisioning, configuration, or maintenance. DynamoDB ensures high availability and data durability by automatically replicating data across multiple geographically separated zones. This eliminates single points of failure and guarantees that your data is always accessible.
Cost-effectiveness is another advantage of DynamoDB. Its pay-per-use model makes it a good choice for applications with bursty workloads or storing large datasets that aren’t accessed frequently. However, due to its distributed nature, DynamoDB’s performance isn’t quite on par with Redis’s in-memory prowess. While it still offers good speed for most use cases, it might not be suitable for applications requiring sub-millisecond response times.
In terms of data structures, DynamoDB is primarily a key-value store. It offers limited support for complex data structures compared to Redis. Additionally, being an AWS service, you’re locked into the AWS ecosystem for data storage.
Here’s a table summarizing the key points:
Feature | Redis | DynamoDB |
Speed | Extremely fast (in-memory) | Very good |
Data Structures | Rich variety (Hashes, Lists, Sets, etc.) | Limited (primarily key-value) |
Durability | Optional persistence (data loss risk on restart) | High durability with automatic replication |
Scalability | Horizontal scaling (manual sharding) | Highly scalable (fully managed by AWS) |
Cost | Can be higher depending on deployment | Potentially cost-effective for large datasets |
Vendor Lock-in | No vendor lock-in (open-source) | Locked into AWS ecosystem |
Amazon DocumentDB
Amazon DynamoDB and Amazon DocumentDB are both robust NoSQL database services provided by Amazon, each with its own unique features and use cases.
Amazon DynamoDB is a serverless, fully managed NoSQL database that supports both key-value and document data models. It’s known for its impressive performance, offering single-digit millisecond latency, and the ability to scale to handle more than 20 million requests per second. It can handle data ranging from 1 gigabyte to 1 petabyte. DynamoDB uses an array of SSDs spread across multiple partitions to store data in a table. This design allows it to offer a single-digit millisecond latency to every request, and it scales up based on demand to service more than 20 million requests per second without performance loss.
On the other side, Amazon DocumentDB is a fully managed NoSQL database designed specifically for managing JSON data models. It provides a fully scalable, low-latency service to manage mission-critical MongoDB workloads. It automatically replicates six copies of your data across 3 availability zones to offer a 99.99% availability. Additionally, it can serve millions of requests per second. DocumentDB has a scalable in-memory optimized architecture that allows the database to evaluate queries faster for larger datasets.
While both services can scale up on demand to query large datasets within a few milliseconds, they differ in terms of the data storage model. For instance, DocumentDB does not support a key-value data model, while DynamoDB does. This means that DynamoDB can scale horizontally, making it a more scalable service than DocumentDB. Furthermore, DynamoDB can store petabytes of data in a table with a 400KB per item constraint. However, DocumentDB has a maximum storage limit of 64 TiB (tebibyte) for the database.
In summary, if you’re working with MongoDB workloads and need a high-performance, scalable, and available database, then DocumentDB is the way to go. If you need a NoSQL database that can handle any amount of traffic and request throughput, then DynamoDB is the right choice.
Here is a summary of their key points:
Feature | DynamoDB | DocumentDB |
Data Model | Key-value (simple structures) | Document (complex structures with nested data) |
Use Cases | High-performance, simple data, variable workloads | Complex queries, data relationships, consistent workloads |
Scalability | Horizontal (adding capacity units) | Vertical (adding cluster instances) |
Pricing | Pay-as-you-go (based on usage) | Pay-as-you-go & Reserved Instances |
Apache HBase
Amazon DynamoDB and Apache HBase are both NoSQL databases, but they serve different purposes and have different features. Amazon DynamoDB is a fully managed NoSQL database service provided by AWS. It’s designed to provide fast and predictable performance with seamless scalability. With DynamoDB, you can offload the administrative burdens of operating and scaling a distributed database. This means you don’t have to worry about hardware provisioning, setup, and configuration, replication, software patching, or cluster scaling.
On the other hand, Apache HBase is an open-source, column-oriented, distributed big data store. It runs on the Apache Hadoop framework and is typically deployed on top of the Hadoop Distributed File System (HDFS), which provides a scalable, persistent storage layer. In the AWS Cloud, you can choose to deploy Apache HBase on Amazon Elastic Compute Cloud (Amazon EC2) and manage it yourself, or you can leverage Apache HBase as a managed service on Amazon EMR.
Both Amazon DynamoDB and Apache HBase can process large volumes of data with high performance and throughput. They also have tight integration with popular open-source processing frameworks like Apache Hive and Apache Spark to enhance querying capabilities.
In terms of use cases, DynamoDB is often used for applications that need consistent, single-digit millisecond latency at any scale, such as mobile, web, gaming, ad tech, IoT, and many other applications. HBase, on the other hand, is typically used for real-time analytics and data-intensive tasks.
The choice between Amazon DynamoDB and Apache HBase really depends on your specific needs and the nature of your project. If you need more help deciding, there are resources available that compare these two databases in more detail.
Here’s a summary of their key points:
Feature | DynamoDB | HBase |
Management | Fully managed by AWS | Open-source, self-managed |
Data Model | Key-value and document store | Wide-column store |
Schema | Flexible, good for predictable access patterns | More flexible, but less defined schema |
Scalability | Automatic | Manual scaling by adding/removing nodes |
Cost | Pay-per-use model | Free, but with infrastructure costs |
Use Cases | Web applications, mobile backends, IoT data | Big-data, real-time analytics, Hadoop ecosystem |
Apache CouchDB
Apache CouchDB and Amazon DynamoDB are both popular database management systems, but they cater to different needs and use cases. Amazon DynamoDB, developed by Amazon, is a document store and key-value store database model. It was initially released in 2012 and is only available as a cloud service. DynamoDB is implemented in Erlang and provides access through a RESTful HTTP API. It offers both eventual consistency and immediate consistency for read operations. Being a commercial product, it comes with a free tier for a limited amount of database operations.
On the other hand, Apache CouchDB is an open-source project under Apache, originally developed by Damien Katz. It follows a document store database model and was initially released in 2005. Unlike DynamoDB, CouchDB is not exclusively cloud-based and can be installed on various operating systems including Android, BSD, Linux, OS X, Solaris, and Windows. CouchDB also uses Erlang for implementation and provides a RESTful HTTP/JSON API for access. However, it only offers eventual consistency.
Now, let’s summarize their key points:
Feature | DynamoDB | CouchDB |
Data Model | Key-value store | Document store (JSON-like) |
Schema | Flexible | Schema-less |
Scalability | Automatic, highly scalable | Manual setup, distributed architecture |
Use Cases | High-traffic web apps, mobile backends, IoT | Content management systems, e-commerce, complex data retrieval |
Deployment | Cloud-based (AWS) | On-premise or cloud |
Cost | Free tier with limitations, pay-as-you-go | Free (open-source), manage your infrastructure |
Neo4j
Neo4j is a graph database that is designed to handle highly connected data. It’s optimized for storing and querying data that can be naturally represented as nodes and relationships (or edges). This makes it an excellent choice for use cases such as social networks, recommendation engines, fraud detection, knowledge graphs, and identity and access management. Neo4j uses nodes to represent entities, relationships to connect nodes, and properties to store data about nodes and relationships. It uses Cypher, a declarative graph query language specially designed to query and manipulate graph data. Neo4j offers a free community version, but for scaling, you’d need the Enterprise Edition, which is paid. The community edition requires manual setup and maintenance, but the Enterprise version comes with additional tools and features for operations. Neo4j focuses on ensuring data consistency and integrity given the interconnected nature of graph data.
On the other hand, DynamoDB is a managed NoSQL database service provided by Amazon Web Services (AWS). It primarily supports key-value and document data models. This makes it suitable for scalable web applications, mobile backends, gaming services, content delivery, and real-time big data analytics. DynamoDB uses tables, items (equivalent to rows), and attributes (equivalent to columns). For more advanced querying, AWS introduced PartiQL, a SQL-compatible query language. DynamoDB offers automatic scaling where throughput capacity can adjust up or down according to actual traffic. It uses a pay-per-what-you-use pricing model. You’re billed for data storage, the read/write capacity mode you choose, and data transfer out. As a managed service, most of the administrative tasks (like hardware and software patching, setting up, configuring, and scaling) are handled by AWS. DynamoDB supports tunable consistency. You can choose between “strongly consistent” or “eventually consistent” reads based on your use case.
Here’s a summary of their key points:
|
Feature |
DynamoDB |
Neo4j |
|
Data Model |
NoSQL (key-value, document) |
Graph (nodes, relationships, properties) |
|
Strengths |
Scalability, flexibility, simple queries |
Connected data, relationship analysis, complex queries |
|
Ideal Use Cases |
User profiles, application settings, e-commerce data |
Social networks, recommendation systems, fraud detection |
|
Analogy |
Library and catalog |
Library mind map |
ScyllaDB
When it comes to performance and cost, ScyllaDB is known to deliver significantly higher throughput and lower latencies at less than one-fifth the cost of DynamoDB. This efficiency and pricing model of ScyllaDB provide savings and predictability. On the other hand, DynamoDB can be quite expensive as teams pay for read-and-write transactions and are charged by the hour, plus storage and optional services.
In terms of scalability, ScyllaDB allows you to scale beyond the limits of DynamoDB. DynamoDB, however, tracks how close usage is to upper bounds, allowing the user to auto-scale and the system to adjust based on data traffic.
ScyllaDB offers flexibility in deployment. It can be deployed anywhere, be it bare-metal, on-premise, Kubernetes, or any cloud provider. DynamoDB, on the other hand, is offered as part of the Amazon Web Services (AWS) cloud service platform.
Throughput is another important factor. DynamoDB’s provisioned throughput model can lead to throttling when the provisioned capacity is exceeded. ScyllaDB, however, is designed to sustain millions of IOPS.
When it comes to latency, ScyllaDB’s specialized cache eliminates the need for an external cache (e.g., DAX), delivering predictable low latencies.
Troubleshooting is easier with ScyllaDB as it offers detailed metrics and logs, making it easy to troubleshoot issues and identify performance bottlenecks across a transparent architecture.
Lastly, customer satisfaction is higher with ScyllaDB. It has an average customer rating of 96% happy with performance and 90% ROI within 12 months. DynamoDB has an average customer rating of 88% happy with performance and 77% ROI within 12 months.
Here’s a summary of their key points:
Feature | ScyllaDB | DynamoDB |
Performance (Latency, Throughput) | Superior | Good |
Cost | More cost-effective (at scale) | Unpredictable, can be expensive |
Deployment & Vendor Lock-in | Flexible (any cloud/on-premises) | AWS only |
Data Model | Wide column store (flexible schema) | Key-value store (simpler model) |
Scalability | Easier to scale | Limited by partitions & item size |
Management | Manual Management | Fully managed |
Companies Using DynamoDB
Amazon
Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. Amazon uses DynamoDB in a variety of ways.
One of the key features of DynamoDB is its scalability. It allows Amazon to create database tables that can store and retrieve any amount of data and serve any level of request traffic. This means that Amazon can scale up or down its tables’ throughput capacity without any downtime or performance degradation.
DynamoDB also offers high availability and durability. The data and traffic for Amazon’s tables are automatically spread over a sufficient number of servers to handle throughput and storage requirements while maintaining consistent and fast performance. All data is stored on solid-state disks (SSDs) and is automatically replicated across multiple Availability Zones in an AWS Region, providing built-in high availability and data durability.
Amazon also uses the global tables feature of DynamoDB to keep its tables in sync across AWS Regions. This ensures that data is available and consistent across different geographical locations.
DynamoDB also provides a backup and restore capability. Amazon can create full backups of its tables for long-term retention and archival for regulatory compliance needs. It can also enable point-in-time recovery for its DynamoDB tables.
Another feature that Amazon uses is Time to Live (TTL). This allows Amazon to delete expired items from tables automatically, helping to reduce storage usage and the cost of storing data that is no longer relevant.
DynamoDB is also popular with developers at Amazon who are building serverless applications with services such as AWS Lambda and AWS API Gateway. It was built for scale and is based on the learning from Amazon engineers as they scaled the Amazon.com retail infrastructure to handle worldwide scale.
In terms of security, DynamoDB offers a broad set of security controls and compliance standards, which Amazon uses to ensure the safety and integrity of its data.
Finally, Amazon uses DynamoDB streams to build serverless event-driven applications. This allows Amazon to respond to changes in its data in real-time, enabling it to build responsive and dynamic applications.
Netflix
Netflix, a global leader in streaming services, relies heavily on Amazon DynamoDB to provide a personalized and interactive viewing experience for its users. DynamoDB, a NoSQL database service, offers scalable and low-latency data access, which is crucial for high-performance database management. Netflix extensively uses this feature of DynamoDB to ensure rapid and flexible data retrieval, thereby enabling the platform to customize content recommendations based on individual user preferences.
In addition to managing high-performance databases, Netflix also uses DynamoDB to store and retrieve user data swiftly and effortlessly. This functionality aids in delivering a personalized viewing experience for Netflix users.
Netflix generates a significant amount of analytics data from its vast customer base. DynamoDB plays a pivotal role in storing and utilizing this data. It enables Netflix to gauge the success of each rollout during numerous A/B tests, which are conducted to improve user experience and engagement.
Furthermore, Netflix employs a sophisticated recommendation system to offer its users a personalized streaming experience. This system is a machine learning algorithm that leverages “Big Data” to suggest content that users are likely to enjoy based on their viewing history, ratings, and other data points.
Dropbox
In the summer of 2018, Dropbox was facing a significant challenge. Their on-premises metadata store was running out of capacity due to rapid data growth in some of the partitions. They had three options to address this issue. The first was to double their on-premises storage capacity, which would have cost them millions of dollars. The second was to delete large amounts of metadata, and the third was to find a new, highly scalable yet cost-effective solution.
Dropbox decided to go with the third option and turned to Amazon Web Services (AWS) for a solution. They used Amazon DynamoDB, a fully managed, flexible NoSQL database that delivers single-digit millisecond performance at any scale, and Amazon Simple Storage Service (Amazon S3), a cloud object storage service. Using these services, Dropbox was able to rapidly develop a new managed storage system called Alki. This new system allowed for virtually unlimited user metadata storage and saved the company millions of dollars as they did not have to increase on-premises storage. Additionally, it reduced the cost per gigabyte by a factor of 5.5.
The migration process to this new system was swift and efficient. Dropbox was able to migrate 300 TB of data in less than 2 weeks. The new system, Alki, was capable of ingesting data at 4,000-6,000 queries per second.
This move to DynamoDB and S3 brought several benefits to Dropbox. It allowed them to save millions of dollars in expansion costs and reduce data-storage costs. It also provided a scalable solution that could handle the company’s rapid data growth.
Before this move, Dropbox’s metadata stores were housed solely within the company’s main data store, Edgestore, which was hosted in an on-premises distributed database built on top of sharded MySQL clusters. By mid-2018, the rapidly growing cold metadata, which is data that is accessed infrequently but needs to be stored durably and available instantly, was less than 2 years away from overwhelming Edgestore. Yet increasing the capacity of the on-premises database would require splitting existing partitions and buying new machines to host them, which would double the cost of Edgestore by adding millions of dollars per year. Additionally, it no longer made sense to store cold metadata in the same database as hot, or frequently used, metadata.
Expedia
Expedia, a leading online travel agency, relies heavily on DynamoDB for its data storage needs. One of the key aspects of their usage is data modeling. This process involves configuring how data is organized within the table, which is crucial for the performance and scalability of the DynamoDB table.
Another important aspect is partitioning. When a DynamoDB table is created, an attribute is chosen as the Partition Key of the table. This allows DynamoDB to split the entire table data into smaller partitions, based on the Partition Key. This partitioning helps in routing the request to the exact partition that contains the required data.
Expedia also makes use of Global Secondary Indexes (GSIs) to support specific use cases. For instance, they might create a GSI named “Landmarks_1” and set “City” as the Partition Key. This allows them to efficiently query data based on the city.
In terms of reads and writes, there are costs associated with reading and writing data in DynamoDB. These costs are packaged into Read Capacity Units (RCUs) and Write Capacity Units (WCUs). Expedia optimizes these reads and writes by retrieving only the required attributes.
Lastly, Expedia uses technology from Amazon Web Services (AWS) including Amazon SageMaker and Amazon DynamoDB Accelerator (DAX). These help serve the most relevant photos and reviews for each customer at sub-millisecond latency and with more than 99 percent accuracy.
Lyft
Lyft, the well-known ridesharing service, relies heavily on Amazon DynamoDB in its technology stack. The company uses DynamoDB in conjunction with Amazon Simple Storage Service (Amazon S3) to store vital data related to customers and rides. This data is integral to the operation of the Lyft app, which facilitates millions of daily trips for customers across the US and Canada.
When a Lyft customer orders a ride through the app, the request is directed to a microservices cluster running on Amazon Elastic Compute Cloud (Amazon EC2). Given the high-performance nature of such applications, DynamoDB’s capabilities make it an ideal choice for this architecture.
As Lyft experienced significant growth, its usage of AWS services increased, leading to financial management challenges. To better understand and manage these costs, Lyft utilized AWS Cost Management services and developed a tool that categorized spending by services and teams. By tracking costs per ride and dividing AWS costs by the number of rides, Lyft gained a clearer understanding of its AWS expenditure. This visibility initiated a wave of cost-reduction activities.
Scalability is another crucial aspect of DynamoDB that makes it attractive to companies like Lyft. As Lyft’s business expanded, so did its data. DynamoDB handles infrastructure provisioning and setup in the background, allowing Lyft to simply specify the read and write capacity units for a table. DynamoDB automatically partitioned the data and workload over additional servers when the application demands exceeded the provisioned capacity. This allowed Lyft’s application to scale seamlessly for high performance.
Slack
Slack, the popular communication platform, uses Amazon DynamoDB, a managed NoSQL database service, in a variety of ways to enhance its functionality and performance. One of the primary uses is for storing real-time user presence information. This data needs to be accessed quickly and efficiently, and DynamoDB provides the perfect solution for this.
In addition to storing user presence information, Slack has developed a serverless app using AWS Step Functions and AWS Lambda. In this architecture, DynamoDB plays a crucial role by storing permissions for each Slack user. This ensures that users only have access to the resources designated to them. This setup allows Slack users to invoke AWS resources such as AWS Lambda functions and Step Functions via the Slack Desktop UI and Mobile UI using Slack apps.
DynamoDB also serves as a datastore for Slack’s workflow apps. These datastores, backed by DynamoDB, store data for workflow apps and are available for these apps only. This use of DynamoDB helps Slack provide a robust and scalable platform for its users.
Slack also integrates with AWS Lambda using DynamoDB. In this setup, an AWS Lambda function is triggered by the DynamoDB Stream. Whenever a change occurs, the function processes the event and posts a message to the Slack channel.
Finally, when Slack uses Airbyte to move data, it extracts data from Slack using the source connector, converts it into a format that DynamoDB can ingest using the provided schema, and then loads it into DynamoDB via the destination connector.
Duolingo
Duolingo, a popular language-learning platform, makes extensive use of Amazon DynamoDB. With over 31 billion items stored on DynamoDB, Duolingo manages a massive amount of data. This is due to the platform’s approximately 18 million monthly users who perform about six billion exercises. The highly scalable nature of DynamoDB allows it to handle this data efficiently.
Performance is another key aspect of Duolingo’s use of DynamoDB. The platform requires high performance to serve its millions of users without significant latency. DynamoDB delivers on this front, reaching 24,000 read units per second and 3,300 write units per second.
In addition to storing user data and exercises, Duolingo also uses DynamoDB for data management in its machine learning pipelines. This enables Duolingo to create personalized language-learning experiences for its users.
Duolingo’s use of DynamoDB is part of a larger AWS ecosystem. The platform also uses other AWS services such as Amazon Elastic Compute Cloud (Amazon EC2), Amazon ElastiCache, Amazon Simple Storage Service (Amazon S3), and Amazon Relational Database Service (Amazon RDS). These services work together to provide a robust and efficient infrastructure for Duolingo’s platform.
Looking ahead, Duolingo plans to further leverage AWS services. The platform intends to use AWS Elastic Beanstalk and AWS Lambda for its microservices architecture and Amazon Redshift for its data analytics.
SmugMug
SmugMug, a popular online photo platform, relies heavily on Amazon DynamoDB for its operations. One of the key uses of DynamoDB at SmugMug is as a durable store for index data, which goes beyond the OpenSearch index. This forms a crucial part of their backup strategy and provides scalability and integration with AWS Lambda. DynamoDB is also used for other non-search services, making it a natural fit for their infrastructure.
The publishing pipeline at SmugMug is driven by various events such as a user entering keywords or captions, new uploads, or label detection through Amazon Rekognition. These events are processed by combining data from a few other asset stores like Amazon Aurora MySQL Compatible Edition and Amazon Simple Storage Service (Amazon S3), before writing a single item into DynamoDB.
The act of writing to DynamoDB triggers a Lambda publishing function, through the DynamoDB Streams Kinesis Adapter. This function takes a batch of updated items from DynamoDB and indexes them into OpenSearch. The publishing Lambda function uses environment variables to determine what OpenSearch domain and index to publish to.
When it comes to testing new configurations or migrating, a migration alias is configured to write to the new OpenSearch domain but uses the same trigger as the production alias. This allows for seamless transitions and minimal disruption to the service.
SmugMug also shares code and information about their stats stack, which includes an HTTP interface to Amazon DynamoDB. This interface interacts with their internal PHP stack and other tools such as Memcached.
AdRoll
AdRoll, a global leader in retargeting, uses Amazon’s DynamoDB extensively in its operations. One of the key areas where DynamoDB is used is in AdRoll’s real-time bidding infrastructure. AdRoll needed to sync data for every user across four regions, involving hundreds of millions of users and tens of thousands of writes per second. The bidding system has a hard cap of 100 milliseconds for every bid request, so AdRoll needs strong guarantees on read performance. To meet these requirements, AdRoll decided on DynamoDB for its low latency, guaranteed throughput, and ability to scale quickly.
DynamoDB is a NoSQL database service with guaranteed throughput and single-digit millisecond latency. As a fully managed service, DynamoDB provides automatic three-way replication and seamless throughput and storage scaling via API and an easy-to-use management console.
AdRoll also uses Amazon DynamoDB in conjunction with Apache Storm. This combination allows AdRoll to replicate its data set across the globe in under 50 milliseconds. This provides speedy response times for both bidding and serving up ads to customers—while keeping costs low. AdRoll also benefits from the scalability provided by AWS.
In the realm of ad targeting, Ad tech companies such as AdRoll rely on DynamoDB to deliver single-digit millisecond latency at any scale. User profiles are stored in a DynamoDB table using 1:1 or 1:M modeling.
Instacart
Instacart, a leading online grocery company in North America, has been leveraging Amazon DynamoDB for their data management needs. Initially, they were using Postgres as their primary datastore. However, as their user base expanded and certain use cases began to exceed the capacity of the largest Amazon EC2 instance size offered by Amazon Web Services (AWS), they realized the need for a different solution. After evaluating several alternatives, they found Amazon DynamoDB to be the best fit.
One of the key areas where Instacart uses DynamoDB is for managing push notifications, which are crucial for communicating with both their shoppers and customers. Shoppers need to be informed about new batches of orders as soon as possible to maximize their earnings opportunities. On the other hand, customers want to be instantly informed if items they originally selected are not in stock so they can choose replacements.
Initially, Instacart used Postgres to store the state machine around messages sent to a user. However, as the number of notifications sent increased linearly with the number of shoppers and customers on the platform, they realized that a single Postgres instance would not be able to support the required throughput.
The ability of DynamoDB to scale on-demand was a significant factor in Instacart’s decision to switch. With Postgres, they would have had to pay for extra capacity, even during off-peak hours when only a few push notifications are sent out. DynamoDB’s ability to scale elastically when required made it a perfect fit for Instacart’s needs.
To adapt to DynamoDB, Instacart made significant changes to their data model to reduce costs. They were confident that DynamoDB would fulfill their latency and scaling requirements based on the SLA guarantees and existing literature.
The transition to DynamoDB was done gradually. Instacart started by rolling out dual writing, i.e., writing notifications to both Postgres and DynamoDB. Later, they added reads and were able to switch teams over from the Postgres codepath to DynamoDB.
Zynga
Amazon DynamoDB is a fully managed NoSQL database service that Zynga uses in its backend infrastructure. This service offers fast and predictable performance along with seamless scalability. It’s essentially a key-value and document database that delivers single-digit millisecond performance at any scale. It’s a fully managed, multiregion, multimaster database with built-in security, backup and restores, and in-memory caching for internet-scale applications.
Zynga’s real-time games use a variety of technologies. For multiplayer games, they use UDP technologies, and some of the latest services use Golang and Nakama. When it comes to storage, Zynga has a diverse stack that includes not just DynamoDB, but also Redis, Aurora, RDS, S3, Couchbase, and more. This diverse technology stack enables Zynga to deliver high-performance, scalable, and reliable gaming experiences to its users.
While the specific use cases and benefits of DynamoDB in Zynga’s infrastructure are not detailed in the sources, we can infer from the general use of DynamoDB in gaming applications. DynamoDB is often chosen for its ability to support large-scale, globally distributed, high-traffic web applications. Its features like automatic scaling, high availability, and data replication make it suitable for applications that need to handle large amounts of data and traffic, much like Zynga’s mobile games.
Mapbox
Mapbox, a location data platform for mobile and web applications, has integrated Amazon DynamoDB into its infrastructure in several innovative ways. One of the key changes was migrating their primary database from a self-managed CouchDB cluster to DynamoDB. This shift provided Mapbox with more stability, redundancy, and speed for their document-based data, while also reducing the need for hands-on administration.
To further enhance their services, Mapbox utilizes DynamoDB Streams, which provide a continuous log of events for Amazon DynamoDB. This feature enables cross-region replication, allowing Mapbox to distribute data across the world for redundancy and speed. When a user requests a map, the data is served from the closest database location, which significantly increases the speed at which their maps are delivered and rendered.
Redundancy and availability are also improved by having multiple redundant copies of the same data. If one region becomes slow or unavailable, database requests are automatically redirected to a stable region. Replica tables ensure users’ data is safe and always available.
Mapbox also uses AWS Lambda in conjunction with DynamoDB Streams. A DynamoDB Stream is a continuous pipeline of every modification made to a DynamoDB database. Mapbox uses AWS Lambda to process events emitted by the DynamoDB Stream from the primary table and replays those modifications onto the replica table in another region. This approach reduces management overhead and allows Mapbox to focus on providing the most efficient and robust structure for serving maps.
Lastly, Mapbox launched their Asset Tracking Solution Architecture to help developers in the logistics space meet the need for a flexible way to ingest, process, and act upon data, without sacrificing security or best practices. The data exposed by this API is backed by a high-performance database (DynamoDB) to enable visualization in real-time on the map client.
Epic Games
Epic Games, the creator of popular games like Fortnite and the Unreal Engine, initially used Amazon DynamoDB, a managed NoSQL database service provided by Amazon Web Services (AWS), for their prototype. DynamoDB offers fast and predictable performance with seamless scalability, which makes it an attractive choice for Epic Games.
However, as the company grew and the needs of their games evolved, they began to look for alternatives that could offer faster performance and more cost-efficiency. One of the main reasons for this was the need to handle the distribution of large game assets for the Unreal Engine. These assets, which include 3D models, textures, sounds, music, and specialized particle systems, can vary greatly in size, from kilobytes to gigabytes.
The shift to developers primarily working from home or in smaller studios around the world due to the COVID-19 pandemic further complicated matters. All the game assets required for a single game, including some extremely large assets, needed to be readily available to people around the world. This necessitated a system that could handle the rapid propagation of these assets across the team whenever a collaborator added or changed something.
While DynamoDB was simple to adopt, Epic Games needed something more practical for their long-term goals. They eventually transitioned from DynamoDB to ScyllaDB, a highly performant, open-source distributed NoSQL database. ScyllaDB is used as a binary cache in front of NVMe and S3 to accelerate the global distribution of large game assets used by Unreal Cloud DDC.
Languages, Frameworks, and Technologies Used
Amazon DynamoDb is a fully managed NoSQL database service that uses various languages, technologies, and frameworks.
Languages: DynamoDB can be interacted with using various programming languages through the AWS SDK. These languages include C++, Go, Java, JavaScript, Microsoft.NET, Node.js, PHP, Python, and Ruby. This wide range of language support makes DynamoDB accessible to developers with different programming backgrounds.
Syntax: DynamoDB uses JSON for its syntax3. Unlike traditional SQL databases, DynamoDB uses a proprietary API based on JavaScript Object Notation (JSON). This API is generally invoked through AWS Software Developer Kits (SDKs) for DynamoDB1. This means that data in DynamoDB is stored and retrieved as JSON objects, making it a good fit for web applications that use JSON for data exchange.
Frameworks and Technologies: DynamoDB is often used in conjunction with other AWS services like AWS Lambda. It is designed for simplicity, predictability, scalability, and reliability. It also supports native write-through caching with Amazon DynamoDB Accelerator (DAX) as well as multiple global secondary indexes. This means that DynamoDB can be integrated into a larger AWS ecosystem, benefiting from the scalability and performance optimization features of these services.
In terms of how these elements work together, developers write code in their chosen language (such as Java, Python, or Ruby) and use the AWS SDK to interact with DynamoDB. They structure their data as JSON objects, which are then stored in DynamoDB tables. When needed, this data can be retrieved and manipulated through API calls made using the SDK.
For example, a developer might write a JavaScript application that uses the AWS SDK for JavaScript to store user profile data as JSON objects in a DynamoDB table. The application could then retrieve and update this data as needed, using API calls to perform operations such as GetItem, PutItem, or UpdateItem on the DynamoDB table.
It’s noticeable that DynamoDB supports secondary indexes. This means that you can create alternate views of your data that are organized differently from your primary table structure, allowing for more flexible and efficient querying.
Finally, DynamoDB integrates with Amazon DynamoDB Accelerator (DAX), which provides in-memory caching for read-intensive workloads. This can significantly improve performance for such workloads by reducing the need to access the database directly.
Overall, the combination of multiple language support, JSON syntax, and integration with other AWS services makes DynamoDB a powerful and flexible NoSQL database option for developers. It’s capable of handling large amounts of data with low latency, making it suitable for many types of applications, including gaming, AdTech, IoT, and many others.
Integrations Used in DynamoDB
Amazon DynamoDB can be integrated with numerous services to enhance its functionality. Here are some of the key integrations:
Amazon DynamoDB Accelerator (DAX): DAX is an in-memory cache for DynamoDB databases that improves the single-digit millisecond performance of DynamoDB to microseconds even with heavy workloads. It addresses three core scenarios:
- As an in-memory cache, DAX reduces the response time of eventually consistent read workloads by an order of magnitude from single-digit milliseconds to microseconds.
- DAX reduces operational and application complexity by providing a managed service that is API-compatible with DynamoDB.
- For ready-heavy or bursty workloads, DAX provides increased throughput and potential operational cost savings by reducing overprovision read capacity units.
- DAX supports server-side encryption. With encryption at rest, the data persisted by DAX on disk will be encrypted.
Amazon OpenSearch Service: DynamoDB offers a zero-ETL integration with Amazon OpenSearch service through the DynamoDB plugin for OpenSearch Ingestion. This integration provides a fully-managed, no-code experience for ingesting data into Amazon OpenSearch Service. Here’s how it works:
- This plugin uses DynamoDB expert to Amazon S3 to create an initial snapshot to load into OpenSearch.
- After the snapshot has been loaded, the plugin uses DynamoDB Streams to replicate any further changes in near real-time.
- Every item is processed as an event in OpenSearch Ingestion and can be modified with processor plugins.
AWS Lambda: AWS Lambda triggers for Amazon DynamoDB enable you to easily set up custom logic to run in response to any changes to an item in DynamoDB. Here’s how it works:
- If you enable DynamoDB Streams on a table, you can associate the stream Amazon Resource Name (ARN) with an AWS Lambda function that you write.
- All mutation actions to that DynamoDB table can then be captured as an item in the stream.
- When new stream records are available, your Lambda function is synchronously invoked.
In addition to these, DynamoDB can also be integrated with various programming languages and frameworks. For instance, it can be configured to use a local DynamoDB instance using Spring Data, or integrated with Node.js. The AWS SDKs provide broad support for Amazon DynamoDB in various languages including Java, JavaScript, .NET, Node.js, PHP, Python, Ruby, C++, Go, Android, and iOS.