difference between athena and redshift spectrum

Spectrum is the “glue” or “bridge” layer that provides Redshift an interface to S3 data. Spectrum queries cost a reasonable $5 /terabyte of data processed.ĭata Stack with Amazon Redshift, Amazon Redshift Spectrum, Amazon Athena, AWS Glue and S3.

Pay only when you run queries against S3 data.

Because there’s no need to increase cluster size, you can save on Redshift storage. That includes joining data from your data lake with data in Redshift, using a single query.

Leave cold data as-is in S3, and query it via Amazon Redshift, without ETL processing.

Continue using your analytics applications, with the same queries you’ve written for Redshift.

Redshift Spectrum offers the best of both worlds. Meanwhile, you continue to pay S3 storage charges for retaining your cold data. That translates to paying more, as Redshift pricing is based on the size of your cluster. Uploading lots of cold S3 data for analysis requires growing your clusters. You may not even know what data to extract until you have analyzed it a bit. Amazon estimates that figuring out the right ETL consumes 70% of an analytics project. Those steps are necessary to convert and structure data for analysis. Loading data into Amazon Redshift involves extract, transform, and load (ETL) steps. So why not load that cold data from S3 into Redshift and call it a day? So why not use these existing options? For example, companies already use Amazon Redshift to analyze their “hot” data. You can load data from S3 into an Amazon Redshift cluster for analysis. Athena offers a console to query S3 data with standard SQL and no infrastructure to manage. EMR uses Hadoop-style queries to access and process large data sets in S3. There are three major existing ways to access and analyze data in S3. For nomenclature purposes, I’ll use “Redshift” for “Amazon Redshift,” and “Spectrum” for “Amazon Redshift Spectrum.” With Amazon Redshift Spectrum, you can query data in Amazon S3 without first loading it into Amazon Redshift. That means analysts need solutions that allow them to access petabytes of dark data. Then then need to analyze it in search of valuable insights.

So what lies below the surface of data lakes? The first thing for organizations to do is to find out what dark data they have accumulated. But much of this data lies inert, in “cold” data lakes, unavailable for analysis, as so-called “dark data.” The Dark Data Problem. These companies know their data is valuable and worth preserving. Within 10 years of its birth, S3 stored over 2 trillion objects, each up to 5 terabytes in size.

Enterprises have been pumping their data into this data lake at a furious rate. By Lars Kamp How you can access your “dark data” with Amazon Redshift SpectrumĪmazon’s Simple Storage Service ( S3) has been around since 2006.

Difference between athena and redshift spectrum