In this blog post, I’ll set up and run a couple of experiments to demonstrate the effects of different kinds of partition pruning in Spark.

Over the past several years, there has been an explosion of different terms related to the world of IT operations. Not long ago, it was standard practice to separate business functions from IT operations. But those days are a distant memory now, and for good reason.

Since the start of the pandemic nearly a year ago, there's been one word on the lips of every business leader, analyst, and investor around the world: cloud. COVID-19 fundamentally changed the way businesses operate. In response, organizations went all in on cloud, betting on the unmatched scale, speed, and security of SaaS applications to help them weather the storm. Nowhere was this shift more pronounced that in our own data and analytics industry.

Data integration has been around for decades in some form or fashion, as organizations are always looking for ways to combine their enterprise data and collect it in a centralized location. The most commonly used and dominant type of data integration is ETL (extract, transform, load). ETL first extracts data from one or more source systems, transforms it as necessary, and then loads it into a target warehouse or data lake.

What’s the fastest and easiest path towards powerful cloud-native analytics that are secure and cost-efficient? In our humble opinion, we believe that’s Cloudera Data Platform (CDP). And sure, we’re a little biased—but only because we’ve seen firsthand how CDP helps our customers realize the full benefits of public cloud.

You’ve probably heard it more than once: Machine learning (ML) can take your digital transformation to another level. It’s a pie-in-the-sky statement that sounds great, right? And while you’d be forgiven for thinking that it might sound too good to be true, operational ML is, in fact, achievable and sustainable. You can get the very kind of ML you need to increase revenue and lower costs. To help teams work smarter and do things faster.

Trying to integrate your marketing data sources? Here are the fundamental differences between Fivetran and Supermetrics as marketing ETL tools.

When it comes to data storage, there is almost as much diversity in the types of databases as there is in the data that they contain. Designing and implementing a strong enterprise data strategy means that you need to be aware of the different databases and how you might best apply them within your organization. In IT, the term "flat file" means something very different from the heavy-duty steel construction file cabinets that you might buy from Safco.