Key techniques to optimally storing data
As mentioned earlier, the data extraction process is one of the most important phases to consider when optimizing your analytic workloads. In the usual process of data retrieval, users such as data analysts, business intelligence engineers, and data engineers run queries to a distributed analytics engine such as Apache Spark and Trino. Then, the distributed analytics engine gets information about the data, such as each file location and metadata. Usually, this kind of data is stored in distributed storage such as Amazon S3, HDFS, and more. After getting all the information about the data, the computing engine actually accesses and reads the data that you specify in the queries. Finally, it returns query results to the users.
To make the data retrieval process faster for further analysis, it’s important to consider how you store data. In particular, you can optimize workloads for analysis by storing data in the most suitable condition...
