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Amazon Redshift
Amazon Redshift

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86
Sequelize
Sequelize

265
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37
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Amazon Redshift vs Sequelize: What are the differences?

Developers describe Amazon Redshift as "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. On the other hand, Sequelize is detailed as "Easy-to-use multi sql dialect ORM for Node.js & io.js". Sequelize is a promise-based ORM for Node.js and io.js. It supports the dialects PostgreSQL, MySQL, MariaDB, SQLite and MSSQL and features solid transaction support, relations, read replication and more.

Amazon Redshift can be classified as a tool in the "Big Data as a Service" category, while Sequelize is grouped under "Object Relational Mapper (ORM)".

"Data Warehousing" is the top reason why over 27 developers like Amazon Redshift, while over 17 developers mention "Good ORM for node.js" as the leading cause for choosing Sequelize.

Sequelize is an open source tool with 19.2K GitHub stars and 3.01K GitHub forks. Here's a link to Sequelize's open source repository on GitHub.

According to the StackShare community, Amazon Redshift has a broader approval, being mentioned in 269 company stacks & 67 developers stacks; compared to Sequelize, which is listed in 38 company stacks and 33 developer stacks.

- No public GitHub repository available -

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 Sequelize?

Sequelize is a promise-based ORM for Node.js and io.js. It supports the dialects PostgreSQL, MySQL, MariaDB, SQLite and MSSQL and features solid transaction support, relations, read replication and more.
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Why do developers choose Amazon Redshift?
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      What are some alternatives to Amazon Redshift and Sequelize?
      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 Sequelize
      Ankit Sobti
      Ankit Sobti
      CTO at Postman Inc · | 10 upvotes · 73K 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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      Interest over time
      Reviews of Amazon Redshift and Sequelize
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      How developers use Amazon Redshift and Sequelize
      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 Ralic Lo
      Ralic Lo uses SequelizeSequelize

      Used it for full stack web application development, especially to interact with MySQL/ MariaDB / PostgreSQL server.

      Avatar of toby lin
      toby lin uses SequelizeSequelize

      ORM for mysql with express. so far so good

      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

      How much does Amazon Redshift cost?
      How much does Sequelize cost?
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