![]() ![]() That translates to extra cost, since Redshift pricing is based on the size of your cluster. ![]() Uploading lots of cold S3 data for analysis requires growing your clusters. ![]() Cost: 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. Effort: Loading data into Amazon Redshift involves extract, transform and load (ETL) steps. ETL is necessary to convert and structure data for analysis.There are two reasons why doing so becomes unfeasible, especially as your data volume grows: So Why not load the cold data from S3 into Redshift for analysis? So why not use these existing options for analyzing data in S3? For example, companies already use Amazon Redshift to analyze their “hot” data. Amazon Redshift: You can load data from S3 into an Amazon Redshift cluster for analysis.Amazon Athena: Athena offers a console to query S3 data with standard SQL and no infrastructure to manage.Amazon Elastic MapReduce (EMR): EMR uses Hadoop-style queries to access and process large data sets in S3.Today, there are three primary ways to access and analyze data in S3: This ‘dark data’ can hold valuable business insights, which means, analysts need solutions that give them access to petabytes of dark data. Over time, enterprises can accumulate a lot of data that gets buried underneath the pile of ‘hot’ data. Amazon Redshift Spectrum for Analyzing Data in Amazon S3 ![]()
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