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.
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.
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.
The mission statement is so direct and uncomplicated. SU Queensland, a non-profit organization based in Australia, is all about “bringing hope to a young generation.” The realities of delivering on this charter, of course, are multi-dimensional and complex.
Harnessing the power of big data is increasingly important not just for business intelligence (BI)—a descriptive model that reveals to enterprises the current state of their companies—but also for data analytics. Data analytics offer predictive models with insight into where a business might head under different scenarios. Your organization's data gives you the opportunity to collect dynamic business intelligence.
