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Amazon DynamoDB vs Amazon Redshift: What are the differences?

Amazon DynamoDB: Fully managed NoSQL database service. All data items are stored on Solid State Drives (SSDs), and are replicated across 3 Availability Zones for high availability and durability. With DynamoDB, you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use; Amazon Redshift: Fast, fully managed, petabyte-scale data warehouse service. Redshift makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools. It is optimized for datasets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Amazon DynamoDB belongs to "NoSQL Database as a Service" category of the tech stack, while Amazon Redshift can be primarily classified under "Big Data as a Service".

Some of the features offered by Amazon DynamoDB are:

  • Automated Storage Scaling – There is no limit to the amount of data you can store in a DynamoDB table, and the service automatically allocates more storage, as you store more data using the DynamoDB write APIs.
  • Provisioned Throughput – When creating a table, simply specify how much request capacity you require. DynamoDB allocates dedicated resources to your table to meet your performance requirements, and automatically partitions data over a sufficient number of servers to meet your request capacity. If your throughput requirements change, simply update your table's request capacity using the AWS Management Console or the Amazon DynamoDB APIs. You are still able to achieve your prior throughput levels while scaling is underway.
  • Fully Distributed, Shared Nothing Architecture – Amazon DynamoDB scales horizontally and can seamlessly scale a single table over hundreds of servers.

On the other hand, Amazon Redshift provides the following key features:

  • Optimized for Data Warehousing- It uses columnar storage, data compression, and zone maps to reduce the amount of IO needed to perform queries. Redshift has a massively parallel processing (MPP) architecture, parallelizing and distributing SQL operations to take advantage of all available resources.
  • Scalable- With a few clicks of the AWS Management Console or a simple API call, you can easily scale the number of nodes in your data warehouse up or down as your performance or capacity needs change.
  • No Up-Front Costs- You pay only for the resources you provision. You can choose On-Demand pricing with no up-front costs or long-term commitments, or obtain significantly discounted rates with Reserved Instance pricing.

"Predictable performance and cost" is the top reason why over 53 developers like Amazon DynamoDB, while over 27 developers mention "Data Warehousing" as the leading cause for choosing Amazon Redshift.

Netflix, Medium, and Lyft are some of the popular companies that use Amazon DynamoDB, whereas Amazon Redshift is used by Lyft, Coursera, and 9GAG. Amazon DynamoDB has a broader approval, being mentioned in 444 company stacks & 187 developers stacks; compared to Amazon Redshift, which is listed in 270 company stacks and 68 developer stacks.

Advice on Amazon DynamoDB and Amazon Redshift

We need to perform ETL from several databases into a data warehouse or data lake. We want to

  • keep raw and transformed data available to users to draft their own queries efficiently
  • give users the ability to give custom permissions and SSO
  • move between open-source on-premises development and cloud-based production environments

We want to use inexpensive Amazon EC2 instances only on medium-sized data set 16GB to 32GB feeding into Tableau Server or PowerBI for reporting and data analysis purposes.

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Replies (3)

You could also use AWS Lambda and use Cloudwatch event schedule if you know when the function should be triggered. The benefit is that you could use any language and use the respective database client.

But if you orchestrate ETLs then it makes sense to use Apache Airflow. This requires Python knowledge.

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Recommends
AirflowAirflow

Though we have always built something custom, Apache airflow (https://airflow.apache.org/) stood out as a key contender/alternative when it comes to open sources. On the commercial offering, Amazon Redshift combined with Amazon Kinesis (for complex manipulations) is great for BI, though Redshift as such is expensive.

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Recommends

You may want to look into a Data Virtualization product called Conduit. It connects to disparate data sources in AWS, on prem, Azure, GCP, and exposes them as a single unified Spark SQL view to PowerBI (direct query) or Tableau. Allows auto query and caching policies to enhance query speeds and experience. Has a GPU query engine and optimized Spark for fallback. Can be deployed on your AWS VM or on prem, scales up and out. Sounds like the ideal solution to your needs.

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Pros of Amazon DynamoDB
Pros of Amazon Redshift
  • 62
    Predictable performance and cost
  • 56
    Scalable
  • 35
    Native JSON Support
  • 21
    AWS Free Tier
  • 7
    Fast
  • 3
    No sql
  • 3
    To store data
  • 2
    Serverless
  • 2
    No Stored procedures is GOOD
  • 1
    ORM with DynamoDBMapper
  • 1
    Elastic Scalability using on-demand mode
  • 1
    Elastic Scalability using autoscaling
  • 1
    DynamoDB Stream
  • 37
    Data Warehousing
  • 27
    Scalable
  • 17
    SQL
  • 14
    Backed by Amazon
  • 5
    Encryption
  • 1
    Cheap and reliable
  • 1
    Isolation
  • 1
    Best Cloud DW Performance
  • 1
    Fast columnar storage

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Cons of Amazon DynamoDB
Cons of Amazon Redshift
  • 4
    Only sequential access for paginate data
  • 1
    Scaling
  • 1
    Document Limit Size
    Be the first to leave a con

    Sign up to add or upvote consMake informed product decisions

    What is Amazon DynamoDB?

    With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.

    What is Amazon Redshift?

    It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

    Need advice about which tool to choose?Ask the StackShare community!

    Jobs that mention Amazon DynamoDB and Amazon Redshift as a desired skillset
    What companies use Amazon DynamoDB?
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    See which teams inside your own company are using Amazon DynamoDB or Amazon Redshift.
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    What tools integrate with Amazon DynamoDB?
    What tools integrate with Amazon Redshift?

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    Jul 9 2019 at 7:22PM

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    What are some alternatives to Amazon DynamoDB and Amazon Redshift?
    Google Cloud Datastore
    Use a managed, NoSQL, schemaless database for storing non-relational data. Cloud Datastore automatically scales as you need it and supports transactions as well as robust, SQL-like queries.
    MongoDB
    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
    Amazon SimpleDB
    Developers simply store and query data items via web services requests and Amazon SimpleDB does the rest. Behind the scenes, Amazon SimpleDB creates and manages multiple geographically distributed replicas of your data automatically to enable high availability and data durability. Amazon SimpleDB provides a simple web services interface to create and store multiple data sets, query your data easily, and return the results. Your data is automatically indexed, making it easy to quickly find the information that you need. There is no need to pre-define a schema or change a schema if new data is added later. And scale-out is as simple as creating new domains, rather than building out new servers.
    MySQL
    The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.
    Amazon S3
    Amazon Simple Storage Service provides a fully redundant data storage infrastructure for storing and retrieving any amount of data, at any time, from anywhere on the web
    See all alternatives