Big data has evolved since its inception to be more query-based than the old days of MapReduce. Outside of Spark, Hive and other query-based workloads dominate the landscape. Queries represent an unprecedented portion of big data workloads running in production which presents a problem of scale and complexity when it comes time to optimize these workloads for peak efficiency.
Expensive or inefficient queries can seriously hamper your system’s overall performance, causing missed SLAs, slow database resources, and negative impacts on other users. From the enterprise down to the small startup, these increasingly cloud-based workloads are presenting challenges for operations teams and developers.
Each big data vendor and cloud provider brings its own tools to the table, but each of these tools is so specialized that they only cover a small portion of the overall data architecture.
The big data market has grown to power entire industries built on the ability to quickly harvest and act upon data from sources as diverse as weather satellites to pictures of rare insects. Emerging technologies like machine learning and artificial intelligence rely on data that first have to be prepared for consumption. This data preparation phase is another key driver in the increase in the number of query-based workloads making their way into production environments.
Traditional schemaless datastores won’t support the speed at which these emerging technologies need to operate. Hive, Impala, SparkSQL, Presto, and standard SQL are the languages of data engineers and data scientists. Ensuring that the queries they create and rely on every day are efficient and performant is no longer just a “nice to have” for IT. Each query needs to perform at the highest level possible.
Learn more about how to tune and debug these big data queries for true success in the Improve Performance with Real Insights Into How Queries are Executing eBook.