Apache Spark has been lauded for its versatility and strengths as a distributed computing framework. It’s attractive, in part, because it can deliver analytics, do data processing, and handle machine learning workloads.

That’s great, especially since Hadoop distributions and the public cloud include Apache Spark. It means organizations don’t have to buy anything new.

What businesses using Apache Spark do need, however, is the talent to leverage Apache Spark. And those skills can be very hard to find.

But in the upcoming webinar “Apache Spark: The New Enterprise Backbone for ETL, Batch and Real-time Streaming,” industry experts will discuss how to address that gap. They’ll also offer details on cloud-based and on-premises scenarios using Apache Spark.

The cloud discussion will address IoT use cases with event time, late arrival, and watermarks. It will also include conversation about Python-based predictive analytics running on Spark. And it will offer information regarding visual interactive development of Apache Spark Structured Streaming pipelines.

Advanced monitoring of Spark pipelines will be part of the on-premises discussion. So will a discussion about data quality and ETL with Apache Spark using pre-built operators.

The industry experts presenting this information will include Anand Venugopal, associate vice president and business head for StreamAnalytix, and Punit Shah, solution architect for StreamAnalytix.

In a recent blog, Shah wrote that top use cases of Apache Spark in the enterprise today include ETL (at 39 percent), real-time processing (at 38 percent), ingest (at 29 percent), other (at 27 percent), and machine learning (at 23 percent). He added that “Enterprise grade tools like StreamAnalytix offer a visual integrated development environment for Apache Spark, and serve all streaming and batch data processing and analytics needs.”

The webinar noted above will take place Oct. 17 at 10am PT/ 1pm ET. To register, visit this link.