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