![]() The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. Assuming you have objects on S3 that Athena can consume, then you might start with Athena, rather than spinning up Redshift. The cookie is used to store the user consent for the cookies in the category "Performance". This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other. The cookies is used to store the user consent for the cookies in the category "Necessary". The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional". In addition to providing different functions, Amazon RedShift and Athena provide different approaches to managing your data and gaining value from it. The cookie is used to store the user consent for the cookies in the category "Analytics". These cookies ensure basic functionalities and security features of the website, anonymously. Necessary cookies are absolutely essential for the website to function properly. See how AtScale’s Intelligent Data Virtualization platform works in the new cloud analytics stack for the Amazon cloud (3 minute video): By leveraging tools like Amazon Redshift Spectrum and Amazon Athena, you can provide your business users and data scientists access to data anywhere, at any grain, with the same simple interface. ![]() ![]() With a virtualization layer like AtScale, you can have your cake and eat it too. S3) and only load what’s needed into the data warehouse. Often, enterprises leave the raw data in the data lake (i.e. Later, the data may be cleansed, augmented and loaded into a cloud data warehouse like Amazon Redshift or Snowflake for running analytics at scale. In today’s cloud-y world, just about all data starts out in a data lake, or data file system, like Amazon S3. In this blog, I will demonstrate a new cloud analytics stack in action that makes use of the data lake and the data warehouse by leveraging AtScale’s Intelligent Data Virtualization platform. Cloud data lakes like Amazon S3 and tools like Redshift Spectrum and Amazon Athena allow you to query your data using SQL, without the need for a traditional data warehouse. It’s no longer necessary to pipe all your data into a data warehouse in order to analyze it. Hadoop pioneered the concept of a data lake but the cloud really perfected it.
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