Quite often, the digital natives of the family — you — have to explain to the analog fans of the family what PDFs are, how to use a hashtag, a phone camera, or a remote. Imagine if you had to explain what machine learning is and how to use it. There’s no need to panic. Cloudera produced a series of ebooks — Production Machine Learning For Dummies, Apache NiFi For Dummies, and Apache Flink For Dummies (coming soon) — to help simplify even the most complex tech topics.

According to Domo, on average, every human created at least 1.7 MB of data per second in 2020. That’s a lot of data. For enterprises the net result is an intricate data management challenge that’s not about to get any less complex anytime soon. Enterprises need to find a way of getting insights from this vast treasure trove of data into the hands of the people that need it. For relatively low amounts of data, public cloud is a possible path for some organizations.

At the end of May, we released the second version of Cloudera SQL Stream Builder (SSB) as part of Cloudera Streaming Analytics (CSA). Among other features, the 1.4 version of CSA surfaced the expressivity of Flink SQL in SQL Stream Builder via adding DDL and Catalog support, and it greatly improved the integration with other Cloudera Data Platform components, for example via enabling stream enrichment from Hive and Kudu.

Modak, a leading provider of modern data engineering solutions, is now a certified solution partner with Cloudera. Customers can seamlessly automate migration to Cloudera’s cloud-based enterprise platform CDP from on-prem deployments and dynamically auto-scale cloud services with Cloudera Data Engineering (CDE)’s integration with Modak Nabu™.

Apache Impala is a massively parallel in-memory SQL engine supported by Cloudera designed for Analytics and ad hoc queries against data stored in Apache Hive, Apache HBase and Apache Kudu tables. Supporting powerful queries and high levels of concurrency Impala can use significant amounts of cluster resources. In multi-tenant environments this can inadvertently impact adjacent services such as YARN, HBase, and even HDFS.

In this blog, I will demonstrate the value of Cloudera DataFlow (CDF), the edge-to-cloud streaming data platform available on the Cloudera Data Platform (CDP), as a Data integration and Democratization fabric. Within the context of a data mesh architecture, I will present industry settings / use cases where the particular architecture is relevant and highlight the business value that it delivers against business and technology areas.

If your organization is using multi-tenant big data clusters (and everyone should be), do you know the usage and cost efficiency of resources in the cluster by tenants? A chargeback or showback model allows IT to determine costs and resource usage by the actual analytic users in the multi-tenant cluster, instead of attributing those to the platform (“overhead’) or IT department. This allows you to know the individual costs per tenant and set limits in order to control overall costs.

Cloudera Data Platform (CDP) supports access controls on tables and columns, as well as on files and directories via Apache Ranger since its first release. It is common to have different workloads using the same data – some require authorizations at the table level (Apache Hive queries) and others at the underlying files (Apache Spark jobs). Unfortunately, in such instances you would have to create and maintain separate Ranger policies for both Hive and HDFS, that correspond to each other.

Our recent blog discussed the four paths to get from legacy platforms to CDP Private Cloud Base. In this blog and accompanying video, we will deep dive into the mechanics of running an in-place upgrade from CDH5 or CDH6 to CDP Private Cloud Base. The overall upgrade follows a seven-step process illustrated below. In the video below we walk through a complete end to end upgrade of CDH to CDP Private Cloud Base.