Spark has become an essential tool in organizations’ data engineering and data science solutions. How can we make the most of Spark in modern data architectures in Google Cloud Platform?

Spark is an open-source compute framework focused on distributed data processing. Today, it is increasingly used in big data and machine learning workloads. So as organizations design and build data and analytics solutions, it is valuable to understand where and how Spark can fit in.

Recognizing the value of Spark, GCP has developed a number of Spark-centric data and analytics offerings which support a variety of business needs, ranging from data ingestion and transformation to data warehousing to data science, and more. In this recorded webcast Thorogood Data and Analytics consultants John Miller and Scott Stieritz take a look at the range of technical possibilities for Spark within GCP through the lens of a modern data architecture and consider how these may best fit into your data workloads today.

What to expect

This webcast includes a presentation and demo, focusing on the following topics:

• Introduction to Spark
• Discussion of Google Cloud Platform offerings for Spark compute
• Exploration of a sample data architecture highlighting how these offerings can be used
• Consideration of serverless Spark within GCP

Is it for you?

• Would you like to explore how Spark can contribute to data engineering and data science?
• Are you using GCP today and looking for opportunities to optimize your data processing activities?
• Are you pursuing a multi-cloud strategy and want to understand what Spark-centric architectures may look like in GCP?