The transportation industry has reached an inflection point – one in which nearly all forms of travel have been met with unprecedented challenges. Transit and airport revenues have been decimated with the lack of passengers, while freight and shipping companies have been overwhelmed with demand from an explosion of e-commerce orders. Despite facing unprecedented challenges, the industry is facing an equally unprecedented opportunity to innovate.

As the owner of Analytics, Monetization and Growth Platforms at Yahoo, one of the core brands of Verizon Media, I'm entrusted to make sure that any solution we select is fully tested across real-world scenarios. Today, we just completed a massive migration of Hadoop and enterprise data warehouse (EDW) workloads to Google Cloud’s BigQuery and Looker.

According to a recent press release by the National Retail Federation, “nearly seventy-three percent of consumers celebrating Valentine’s Day this year feel it’s important to do so given the current state of the pandemic.” The release also states that “consumers still feel it’s important to spoil their loved ones in light of the pandemic.” We couldn’t agree more on the importance of celebrating the day.

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.

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.

The importance of effective data analytics within an organization is widely accepted by business leaders at this point. With use cases for data analysis spanning every department—from IT management, financial planning, marketing analytics, and so on—the right data analytics tools can have a significant impact on a company’s profitability and growth.

Five differences between Stitch, Talend, and Xplenty: Organizations store data in many destinations, making that data difficult to analyze. Legacy systems, SaaS locations, in-house databases, apps, you name it — by storing data in all kinds of places, companies can complicate data analytics considerably. Storing data in a warehouse or a lake makes more sense.