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Amazon Redshift
Amazon Redshift

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Amazon Redshift vs Panoply: What are the differences?

Developers describe Amazon Redshift as "Fast, fully managed, petabyte-scale data warehouse service". 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. On the other hand, Panoply is detailed as "Collect, combine, and integrate all your data with any analytics tools". It is the data warehouse built for analysts. Our data management platform automates all three key aspects of the data stack: data collection, management, and query optimization.

Amazon Redshift and Panoply belong to "Big Data as a Service" category of the tech stack.

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, Panoply provides the following key features:

  • Data warehouse
  • Business Intelligence
  • Optimized Query Engine
- No public GitHub repository available -
- 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 Panoply?

It is the data warehouse built for analysts. Our data management platform automates all three key aspects of the data stack: data collection, management, and query optimization.
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          What are some alternatives to Amazon Redshift and Panoply?
          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 Panoply
          Ankit Sobti
          Ankit Sobti
          CTO at Postman Inc · | 11 upvotes · 167.1K views
          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.

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          Reviews of Amazon Redshift and Panoply
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          How developers use Amazon Redshift and Panoply
          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

          How much does Amazon Redshift cost?
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