Every large enterprise organization is attempting to accelerate their digital transformation strategies to engage with their customers in a more personalized, relevant, and dynamic way. The ability to perform analytics on data as it is created and collected (a.k.a. real-time data streams) and generate immediate insights for faster decision making provides a competitive edge for organizations.

We are excited to announce that Cloudera is named as a 2022 Gartner Peer Insights Customers’ Choice for Cloud Database Management Systems (DBMS). Peer Insights is a user review site, the technology professional’s “go-to” destination for information on customer experience. Gartner Peer Insights collects anonymous customer reviews on select product categories. To date, Gartner has collected over 450,000 reviews for 18,000 products in over 425 categories.

Here at Cloudera, we’re committed to helping make the lives of data practitioners as painless as possible. For data scientists, we continue to provide new Applied Machine Learning Prototypes (AMPs), which are open source and available on GitHub. These pre-built reference examples are complete end-to-end data science projects. In Cloudera Machine Learning (CML), you can deploy them with the single click of a button, bringing data scientists that much closer to providing value.

In the first blog of the Universal Data Distribution blog series, we discussed the emerging need within enterprise organizations to take control of their data flows. From origin through all points of consumption both on-prem and in the cloud, all data flows need to be controlled in a simple, secure, universal, scalable, and cost-effective way.

We live in a hybrid data world. In the past decade, the amount of structured data created, captured, copied, and consumed globally has grown from less than 1 ZB in 2011 to nearly 14 ZB in 2020. Impressive, but dwarfed by the amount of unstructured data, cloud data, and machine data – another 50 ZB.

Data scientists and machine learning engineers in enterprise organizations need to fully understand their data in order to properly analyze it, build models, and power machine learning use cases across their business. Due to the lack of tooling specifically designed for data discovery, exploration, and preliminary analysis, this presents a significant challenge for these teams.

Since 2015, the Cloudera DataFlow team has been helping the largest enterprise organizations in the world adopt Apache NiFi as their enterprise standard data movement tool. Over the last few years, we have had a front-row seat in our customers’ hybrid cloud journey as they expand their data estate across the edge, on-premise, and multiple cloud providers.

At Cloudera we’re building the world’s only hybrid data platform that’s founded on open source and truly hybrid. What do we mean by truly hybrid? Well, not only does it seamlessly support on-premises and cloud-based deployments alike, but uniquely, it is cloud vendor agnostic, allowing multi-cloud strategies to thrive.

Deciding to adopt an AI-first strategy is the easy part. Figuring out how to implement it takes a little more effort. It requires a clear-eyed vision built around well-defined goals and a realistic execution plan. Being AI-first means setting up your organization for the future. By leveraging data, analytics, and automation, a company can gain a better understanding of where it is and where it needs to go.

Tuning Hive on Tez queries can never be done in a one-size-fits-all approach. The performance on queries depends on the size of the data, file types, query design, and query patterns. During performance testing, evaluate and validate configuration parameters and any SQL modifications.