This is the 3rd blog in our series on Snowflake Resource Optimization. In parts 1 and 2 of this blog series, we showed you how Snowflake’s unique architecture allows for a virtually unlimited number of compute resources to be accessed near-instantaneously. We also provided best practices for administering these compute resources to optimize performance and reduce credit consumption.
So, you’ve made a cool People Analytics App, filled it with data from your HCM, Finance, Talent Review System, and Recognition System (I could go on). You’ve also governed access to it to specific people and can use it to tell compelling stories.
So, you’ve made a cool People Analytics App, filled it with data from your HCM, Finance, Talent Review System, and Recognition System (I could go on). You’ve also governed access to it to specific people and can use it to tell compelling stories.
We’re excited to share that after adding ANSI SQL, secondary indices, star schema, and view capabilities to Cloudera’s Operational Database, we will be introducing distributed transaction support in the coming months. The ACID model of database design is one of the most important concepts in databases. ACID stands for atomicity, consistency, isolation, and durability. For a very long time, strict adherence to these four properties was required for a commercially successful database.
Across nearly every sector working with complex data, Spark has quickly become the de-facto distributed computing framework for teams across the data and analytics lifecycle. One of most awaited features of Spark 3.0 is the new Adaptive Query Execution framework (AQE), which fixes the issues that have plagued a lot of Spark SQL workloads. Those were documented in early 2018 in this blog from a mixed Intel and Baidu team.
