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Amazon Redshift
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Amazon Redshift vs MemSQL: What are the differences?

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; MemSQL: Database for real-time transactions and analytics. MemSQL converges transactions and analytics for sub-second data processing and reporting. Real-time businesses can build robust applications on a simple and scalable infrastructure that complements and extends existing data pipelines.

Amazon Redshift can be classified as a tool in the "Big Data as a Service" category, while MemSQL is grouped under "In-Memory Databases".

Some of the features offered by Amazon Redshift are:

  • 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.

On the other hand, MemSQL provides the following key features:

  • ANSI SQL Support
  • Fully-distributed Joins
  • Compiled Queries

Lyft, Coursera, and 9GAG are some of the popular companies that use Amazon Redshift, whereas MemSQL is used by Shutterstock, Zynga, and StreetHawk. Amazon Redshift has a broader approval, being mentioned in 269 company stacks & 67 developers stacks; compared to MemSQL, which is listed in 10 company stacks and 4 developer stacks.

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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.

What is MemSQL?

MemSQL converges transactions and analytics for sub-second data processing and reporting. Real-time businesses can build robust applications on a simple and scalable infrastructure that complements and extends existing data pipelines.
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      What are some alternatives to Amazon Redshift and MemSQL?
      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.
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      Decisions about Amazon Redshift and MemSQL
      Ankit Sobti
      Ankit Sobti
      CTO at Postman Inc · | 10 upvotes · 73.3K views
      atPostmanPostman
      dbt
      dbt
      Amazon Redshift
      Amazon Redshift
      Stitch
      Stitch
      Looker
      Looker

      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.

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      How developers use Amazon Redshift and MemSQL
      Avatar of Olo
      Olo uses Amazon RedshiftAmazon Redshift

      Aggressive archiving of historical data to keep the production database as small as possible. Using our in-house soon-to-be-open-sourced ETL library, SharpShifter.

      Avatar of Christian Moeller
      Christian Moeller uses Amazon RedshiftAmazon Redshift

      Connected to BI (Pentaho)

      Avatar of Kovid Rathee
      Kovid Rathee uses Amazon RedshiftAmazon Redshift

      OLAP and BI

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