Amazon Redshift logo

Amazon Redshift

Fast, fully managed, petabyte-scale data warehouse service
653
338
+ 1
86

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.
Amazon Redshift is a tool in the Big Data as a Service category of a tech stack.

Who uses Amazon Redshift?

Companies
335 companies reportedly use Amazon Redshift in their tech stacks, including Lyft, Coursera, and 9GAG.

Developers
283 developers on StackShare have stated that they use Amazon Redshift.

Amazon Redshift Integrations

MySQL, SQLite, Amplitude, Metabase, and Redash are some of the popular tools that integrate with Amazon Redshift. Here's a list of all 49 tools that integrate with Amazon Redshift.

Why developers like Amazon Redshift?

Here’s a list of reasons why companies and developers use Amazon Redshift
Amazon Redshift Reviews

Here are some stack decisions, common use cases and reviews by companies and developers who chose Amazon Redshift in their tech stack.

Julien DeFrance
Julien DeFrance
Principal Software Engineer at Tophatter · | 16 upvotes · 517.1K views
atSmartZipSmartZip
Rails
Rails
Rails API
Rails API
AWS Elastic Beanstalk
AWS Elastic Beanstalk
Capistrano
Capistrano
Docker
Docker
Amazon S3
Amazon S3
Amazon RDS
Amazon RDS
MySQL
MySQL
Amazon RDS for Aurora
Amazon RDS for Aurora
Amazon ElastiCache
Amazon ElastiCache
Memcached
Memcached
Amazon CloudFront
Amazon CloudFront
Segment
Segment
Zapier
Zapier
Amazon Redshift
Amazon Redshift
Amazon Quicksight
Amazon Quicksight
Superset
Superset
Elasticsearch
Elasticsearch
Amazon Elasticsearch Service
Amazon Elasticsearch Service
New Relic
New Relic
AWS Lambda
AWS Lambda
Node.js
Node.js
Ruby
Ruby
Amazon DynamoDB
Amazon DynamoDB
Algolia
Algolia

Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

Future improvements / technology decisions included:

Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

See more
Jake Stein
Jake Stein
CEO at Stitch · | 13 upvotes · 101.8K views
atStitchStitch
Go
Go
Amazon RDS
Amazon RDS
Amazon S3
Amazon S3
Amazon Redshift
Amazon Redshift
Amazon EC2
Amazon EC2
AWS OpsWorks
AWS OpsWorks
Kubernetes
Kubernetes
Python
Python
JavaScript
JavaScript
Clojure
Clojure

Stitch is run entirely on AWS. All of our transactional databases are run with Amazon RDS, and we rely on Amazon S3 for data persistence in various stages of our pipeline. Our product integrates with Amazon Redshift as a data destination, and we also use Redshift as an internal data warehouse (powered by Stitch, of course).

The majority of our services run on stateless Amazon EC2 instances that are managed by AWS OpsWorks. We recently introduced Kubernetes into our infrastructure to run the scheduled jobs that execute Singer code to extract data from various sources. Although we tend to be wary of shiny new toys, Kubernetes has proven to be a good fit for this problem, and its stability, strong community and helpful tooling have made it easy for us to incorporate into our operations.

While we continue to be happy with Clojure for our internal services, we felt that its relatively narrow adoption could impede Singer's growth. We chose Python both because it is well suited to the task, and it seems to have reached critical mass among data engineers. All that being said, the Singer spec is language agnostic, and integrations and libraries have been developed in JavaScript, Go, and Clojure.

See more
Ankit Sobti
Ankit Sobti
CTO at Postman Inc · | 11 upvotes · 86.1K views
atPostmanPostman
Looker
Looker
Stitch
Stitch
Amazon Redshift
Amazon Redshift
dbt
dbt

Looker , Stitch , Amazon Redshift , dbt

We recently moved our Data Analytics and Business Intelligence tooling to Looker . It's already helping us create a solid process for reusable SQL-based data modeling, with consistent definitions across the entire organizations. Looker allows us to collaboratively build these version-controlled models and push the limits of what we've traditionally been able to accomplish with analytics with a lean team.

For Data Engineering, we're in the process of moving from maintaining our own ETL pipelines on AWS to a managed ELT system on Stitch. We're also evaluating the command line tool, dbt to manage data transformations. Our hope is that Stitch + dbt will streamline the ELT bit, allowing us to focus our energies on analyzing data, rather than managing it.

See more
nikolovivan
nikolovivan
Amazon Redshift
Amazon Redshift

We use Amazon Redshift because it's based on PostgreSQL and allows us to quickly query vast amounts of data for reporting and analytics.

See more

Amazon Redshift's 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.
  • Fault Tolerant- Amazon Redshift has multiple features that enhance the reliability of your data warehouse cluster. All data written to a node in your cluster is automatically replicated to other nodes within the cluster and all data is continuously backed up to Amazon S3.
  • SQL - Amazon Redshift is a SQL data warehouse and uses industry standard ODBC and JDBC connections and Postgres drivers.
  • Isolation - Amazon Redshift enables you to configure firewall rules to control network access to your data warehouse cluster.
  • Encryption – With just a couple of parameter settings, you can set up Amazon Redshift to use SSL to secure data in transit and hardware-acccelerated AES-256 encryption for data at rest.<br>

Amazon Redshift Alternatives & Comparisons

What are some alternatives to Amazon Redshift?
Google BigQuery
Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.
Amazon Athena
Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.
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.
Amazon Redshift Spectrum
With Redshift Spectrum, you can extend the analytic power of Amazon Redshift beyond data stored on local disks in your data warehouse to query vast amounts of unstructured data in your Amazon S3 “data lake” -- without having to load or transform any data.
Hadoop
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
See all alternatives

Amazon Redshift's Followers
338 developers follow Amazon Redshift to keep up with related blogs and decisions.
Abhay Sarda
Khang Pham
Shahdat Hossain
northern sat
Scott Smith
Kyle Prifogle
julienmontreal
Jack Bunnage
War Dah
Ratan Jena