Cloud-based providers host the necessary physical resources “in the cloud” while you simply pay for using the service. Physical data warehouses are complex and expensive to build and maintain, though.Ĭloud-based data warehouse services offer a much cheaper and easier way to use a data warehouse without needing any physical resources on site. Traditional on-premise data warehouses are used for analyzing an organization’s historical data in one unified repository, pulling data from many different source systems, such as operational databases. The comparison can also teach you what to look for in more general terms when considering any cloud-based data solution currently available. When you’re finished reading, you’ll know which service you should choose between Athena and Redshift. In this post, you’ll get a broad overview of cloud-based data warehousing, and you’ll come to understand the main differences between Amazon Redshift and Amazon Athena (also see this post by Panoply on the subject). However, with a dizzying amount of information available on both services, it’s a challenge to recognize what to look out for when choosing a cloud-based data service to meet your needs. Two popular AWS cloud computing services for data analytics and BI are Amazon Redshift and Amazon Athena, both of which are useful for delivering actionable insights that drive better decision making from your data. I'll recommend Redshift for now since it can address a wider range of use cases, but we could give you better advice if you described your use case in depth.( Note: This is a guest post from our friends at Panoply)Ĭloud-based data services are all the rage these days for many good reasons, and AWS (Amazon Web Services) is the current king of cloud-based data service providers, as this analysis carried out by StackOverflow indicates.
If you choose Redshift you'll need to ingest the data from your files into it and maybe carry out some tuning tasks for performance gain. In the case you go for Athena you'd also proabably need to change your file format to Parquet or Avro and review your partition strategy depending on your most frequent type of query. In both cases you may need to adapt the data model to fit your queries better. Once you select the technology you'll need to optimize your data in order to get the queries executed as fast as possible. If performance is not so critical and queries will be predictable somewhat I'd go for Athena. If performance is a key factor, users are going to execute unpredictable queries and direct and managing costs are not a problem I'd definitely go for Redshift. Amazon Athena has a broader approval, being mentioned in 68 company stacks & 60 developers stacks compared to Amazon Quicksight, which is listed in 13 company stacks and 19 developer stacks.įirst of all you should make your choice upon Redshift or Athena based on your use case since they are two very diferent services - Redshift is an enterprise-grade MPP Data Warehouse while Athena is a SQL layer on top of S3 with limited performance. Amazon QuickSight is a fast, cloud-powered business analytics service that makes it easy to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data.Īmazon Athena and Amazon Quicksight are primarily classified as "Big Data" and "Business Intelligence" tools respectively.Īuto Trader, Zola, and Twilio SendGrid are some of the popular companies that use Amazon Athena, whereas Amazon Quicksight is used by Haymarket Media Asia, MPOWER Financing, and Kobalt Music. On the other hand, Amazon Quicksight is detailed as " Fast, easy to use business analytics at 1/10th the cost of traditional BI solutions". Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL.
Amazon Athena vs Amazon Quicksight: What are the differences?ĭevelopers describe Amazon Athena as " Query S3 Using SQL".
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