Amazon DynamoDB - Big Data Analytics Options on AWS

Amazon DynamoDB

Amazon DynamoDB is a fast, fully-managed NoSQL database service that makes it simple and cost effective to store and retrieve any amount of data, and serve any level of request traffic. DynamoDB helps offload the administrative burden of operating and scaling a highly-available distributed database cluster. This storage alternative meets the latency and throughput requirements of highly demanding applications by providing single-digit millisecond latency and predictable performance with seamless throughput and storage scalability.

DynamoDB stores structured data in tables, indexed by primary key, and allows low-latency read and write access to items ranging from 1 byte up to 400 KB. DynamoDB supports three data types (number, string, and binary), in both scalar and multi-valued sets. It supports document stores such as JSON, XML, or HTML in these data types. Tables do not have a fixed schema, so each data item can have a different number of attributes. The primary key can either be a single-attribute hash key or a composite hash-range key.

DynamoDB offers both global and local secondary indexes provide additional flexibility for querying against attributes other than the primary key. DynamoDB provides both eventually-consistent reads (by default), and strongly-consistent reads (optional), as well as implicit item-level transactions for item put, update, delete, conditional operations, and increment/decrement.

With DynamoDB, you can create database tables that can store and retrieve any amount of data and serve any level of request traffic. You can scale up or scale down your tables' throughput capacity without downtime or performance degradation.

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. Point-in-time recovery helps protect your tables from accidental write or delete operations. With point-in-time recovery, you can restore a table to any point in time during the last 35 days.

DynamoDB enables you to delete expired items from tables automatically to help you reduce storage usage and the cost of storing data that is no longer relevant. For more information, see Expiring Items By Using DynamoDB Time to Live (TTL).

DynamoDB is integrated with other services, such as Amazon EMR, Amazon Redshift, AWS Data Pipeline, and S3 for analytics, data warehouse, data import/export, backup, and archive.

Ideal usage patterns

DynamoDB is ideal for existing or new applications that need a flexible NoSQL database with low read and write latencies, and the ability to scale storage and throughput up or down as needed without code changes or downtime.

Common use cases include:

  • Mobile apps

  • Gaming

  • Digital ad serving

  • Live voting

  • Audience interaction for live events

  • Sensor networks

  • Log ingestion

  • Access control for web-based content

  • Metadata storage for Amazon S3 objects

  • Ecommerce shopping carts

  • Web session management

Many of these use cases require a highly available and scalable database because downtime or performance degradation has an immediate negative impact on an organization’s business.

Cost model

With DynamoDB, you pay only for what you use and there is no minimum fee.

DynamoDB charges for reading, writing, and storing data in your DynamoDB tables, along with any optional features you choose to enable. DynamoDB has two capacity modes and those come with specific billing options for processing reads and writes on your tables: on-demand and provisioned.

  • With on-demand capacity mode, DynamoDB charges you for the data reads and writes your application performs on your tables. You do not need to specify how much read and write throughput you expect your application to perform, because DynamoDB instantly accommodates your workloads as they ramp up or down.

  • 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.

New customers can start using DynamoDB for free as part of the AWS Free Usage Tier. For more information, see Amazon DynamoDB pricing.


DynamoDB is a key-value and document database that can support tables of virtually any size with horizontal scaling. This enables DynamoDB to scale to more than ten trillion requests per day with peaks greater than 20 million requests per second, over petabytes of storage.

DynamoDB supports both key-value and document data models. This enables DynamoDB to have a flexible schema, so each row can have any number of columns at any point in time. This enables you to easily adapt the tables as your business requirements change, without having to redefine the table schema as you would in relational databases.

DynamoDB Accelerator (DAX) is an in-memory cache that delivers fast read performance for your tables at scale by enabling you to use a fully managed in-memory cache. Using DAX, you can improve the read performance of your DynamoDB tables by up to ten times—taking the time required for reads from milliseconds to microseconds, even at millions of requests per second.

DynamoDB global tables replicate your data automatically across your choice of AWS Regions, and automatically scale capacity to accommodate your workloads. With global tables, your globally distributed applications can access data locally in the selected Regions to get single-digit millisecond read and write performance.

Amazon Kinesis Data Streams for DynamoDB captures item-level changes in your DynamoDB tables as a Kinesis data stream. This feature enables you to build advanced streaming applications such as real-time log aggregation, real-time business analytics, and IoT data capture. Through Kinesis Data Streams, you also can use Amazon Data Firehose to deliver DynamoDB data automatically to other AWS services.

AWS Glue Elastic Views supports DynamoDB as a source to combine and replicate data continuously across multiple databases in near-real-time.

Durability and availability

DynamoDB automatically spreads the data and traffic for your tables over a sufficient number of servers to handle your throughput and storage requirements, while maintaining consistent and fast performance. All of your 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. You can use global tables to keep DynamoDB tables in sync across AWS Regions.

Amazon DynamoDB Streams captures all data activity that happens on your table and enables you to set up Regional replication from one geographic Region to another to provide even greater availability.

Scalability and elasticity

DynamoDB is both highly scalable and elastic. There is no limit to the amount of data that you can store in a DynamoDB table, and the service automatically allocates more storage as you store more data using the DynamoDB write API operations. Data is automatically partitioned and re-partitioned as needed, while the use of SSDs provides predictable low-latency response times at any scale. The service is also elastic, in that you can simply “dial-up” or “dial-down” the read and write capacity of a table as your needs change.

When you create a DynamoDB table, auto scaling is the default capacity setting, but you can also enable auto scaling on any table that does not have it active. Behind the scenes, DynamoDB auto scaling uses a scaling policy in Application Auto Scaling. To configure auto scaling in DynamoDB, you set the minimum and maximum levels of read and write capacity in addition to the target utilization percentage. Auto scaling uses Amazon CloudWatch to monitor a table’s read and write capacity metrics by creating CloudWatch alarms that track consumed capacity.


DynamoDB provides a low-level RESTAPI, as well as higher-level SDKs for Java, ET, and PHP that wrap the low-level REST API and provide some object-relational mapping (ORM) functions. These APIs provide both a management and data interface for DynamoDB. The API currently offers operations that enable table management (creating, listing, deleting, and obtaining metadata) and working with attributes (getting, writing, and deleting attributes; query using an index, and full scan).

While standard SQL isn’t available, you can use the DynamoDB select operation to create SQL-like queries that retrieve a set of attributes based on criteria that you provide. You can also work with DynamoDB using the console.


DynamoDB has the following anti-patterns:

  • Prewritten application tied to a traditional relational database – If you are attempting to port an existing application to the AWS Cloud and need to continue using a relational database, you can use either Amazon RDS (Amazon Aurora, MySQL, PostgreSQL, Oracle, or SQL Server), or one of the many pre-configured Amazon EC2 database AMIs. You can also install your choice of database software on an EC2 instance that you manage.

  • Joins or complex transactions – While many solutions are able to leverage DynamoDB to support their users, it’s possible that your application may require joins, complex transactions, and other relational infrastructure provided by traditional database platforms. If this is the case, you may want to explore Amazon Redshift, Amazon RDS, or Amazon EC2 with a self-managed database.

  • Binary large objects (BLOB) data – If you plan on storing large (greater than 400 KB) BLOB data, such as digital video, images, or music, you’ll want to consider Amazon S3. However, DynamoDB can be used in this scenario for keeping track of metadata (such as item name, size, date created, owner, location, and so on) about your binary objects.

  • Large data with low I/O rate – DynamoDB uses SSD drives and is optimized for workloads with a high I/O rate per GB stored. If you plan to store very large amounts of data that are infrequently accessed, other storage options may be a better choice, such as Amazon S3.