Data Pipelines for Complex Data: Spark Platforms vs Upsolver

Apache Spark is a popular open-source framework for large-scale distributed data processing. Cloud-based platforms that offer a managed distribution of Spark are widely used for transforming data in cloud storage into analytics-ready data – but are they the right tool for the job? Can you simplify your data pipelines with modern SQL-based tools?

In this detailed guide we’ll take a close look at Apache Spark and Spark-based platforms, and understand the pros and cons of these tools compared to Upsolver’s cloud-native data lake pipeline solution.

Get the white paper now to learn:

– Common use cases for Apache Spark and Spark-based platforms

– Is it possible to achieve self-service with a Spark-based solution?

– The Upsolver data pipeline approach and how it differs from Apache Hadoop and Spark

– Detailed comparison matrix between Spark and alternatives – features, pricing, functionality