Building a data lake in Amazon S3 using AWS Spectrum to query the data from a Redshift cluster is a common practice. However, when it comes to boosting performance, there are some tricks that are worth learning. One of those is using data in Parquet format, which Redshift considers a best practice. Here's how to use Parquet format with Xplenty for the best data lake performance.

The data lakes concept has come back into popular focus with Amazon Athena, an innovative, serverless solution. But does it fit into your organization’s data stack? This article covers Amazon Athena capabilities, pros and cons, competitors, and use cases.

Public preview of the data lake export feature is now available. Snowflake announced a private preview of data lake export at the Snowflake virtual summit in June 2020. Data lake export is one of the key features of the data lake workload in the Snowflake Data Cloud. The feature makes Snowflake data accessible to the external data lake, and it enables customers to take advantage of Snowflake’s reliable and performant processing capabilities.

Imagine if you bought a beautiful lake house, invited all your friends to come and visit, and the lake was dry? Not much value and a little embarrassing, right? Now imagine you have that beautiful lake house and you have special water valves to control not just if there is water in the lake but also control the water quality, clarity, and what fish the lake is stocked with? Much more impressive, correct?

There is no debate. Making sense of your data is just good business. Studies from Forrester Research, McKinsey and more show that companies that leverage their data better tend to outperform their less-informed counterparts. Our own recent research, Data as the New Water: The Importance of Investing in Data and Analytics Pipelines, done in partnership with IDC, shows that companies that optimize their data pipelines see enhanced operational efficiency (88 percent vs.