When Kudu was first introduced as a part of CDH in 2017, it didn’t support any kind of authorization so only air-gapped and non-secure use cases were satisfied. Coarse-grained authorization was added along with authentication in CDH 5.11 (Kudu 1.3.0) which made it possible to restrict access only to Apache Impala where Apache Sentry policies could be applied, enabling a lot more use cases.
With automation becoming more user-friendly and streamlined than ever before, it's understandable organizations across sectors are examining how it can enhance their analytics capability and accelerate their business shift toward digital transformation.
Many BigQuery users ask for database triggers—a way to run some procedural code in response to events on a particular BigQuery table, model, or dataset. Maybe you want to run an ELT job whenever a new table partition is created, or maybe you want to retrain your ML model whenever new rows are inserted into the table. In the general category of “Cloud gets easier”, this article will show how to quite simply and cleanly tie together BigQuery and Cloud Run.
In Part 1 we learned how to set up our Xplenty pipeline to work with Chartio and prepared the data source. In Part 2, we will focus on using the data Xplenty provides in the Chartio platform. If you're new to Chartio, you can read through their QuickStart docs (shouldn't take more than 5-10 minutes) to gain some familiarity.
Snowflake met with Jan Doumen, Head of Expertise for Allianz Benelux, and Naveed Memon, Program Director, Data and Analytics for Emirates, at Data Cloud Summit 2020. Read excerpts from the conversation to learn how capturing data insights in the Data Cloud brings value to their businesses. Data’s value in the 21st century is often compared to oil’s value in the 18th century. It can transform organizations, opening doors to unprecedented opportunities.
Democratization of data within an organization is essential to help users derive innovative insights for growth. In a big data environment, traceability of where the data in the data warehouse originated and how it flows through a business is critical. This traceability information is called data lineage. Being able to track, manage, and view data lineage helps you to simplify tracking data errors, forensics, and data dependency identification.
