Analytics

Apache Spark is now widely used in many enterprises for building high-performance ETL and Machine Learning pipelines. If the users are already familiar with Python then PySpark provides a python API for using Apache Spark. When users work with PySpark they often use existing python and/or custom Python packages in their program to extend and complement Apache Spark’s functionality. Apache Spark provides several options to manage these dependencies.

Data science and big data are essential in today’s world of marketing. You’ve probably already seen multiple instances of both being used for advertising and sales purposes, but you may not realize just how useful they are. If you own a business, you need to know how to use data for your own marketing programs.

Your data now resides in the cloud, and you’ve chosen SaaS providers that use their own products (or drink their own champagne, as I like to say). Does that mean you’re getting the full value from your data? No. Chances are high your data is still siloed. This time, the culprits are your SaaS providers who collect and store your data, thus limiting the analytics you can perform on it.

Cloudera released a lot of things around Apache NiFi recently! We just released Cloudera Flow Management (CFM) 2.1.1 that provides Apache NiFi on top of Cloudera Data Platform (CDP) 7.1.6. This major release provides the latest and greatest of Apache NiFi as it includes Apache NiFi 1.13.2 and additional improvements, bug fixes, components, etc. Cloudera also released CDP 7.2.9 on all three major cloud platforms, and it also brings Flow Management on DataHub with Apache NiFi 1.13.2 and more.

New capabilities, including a custom-branded OEM option, enable product teams to blend data integration into their apps.