Spark

Spark is an open-source solution for processing large volumes of data by parallelizing computations. Before 2009, the standard method for data transformation in the Hadoop ecosystem was MapReduce. Spark was introduced in 2009 and quickly established itself as a faster alternative, eventually evolving into an independent distributed computing system beyond Hadoop.

The main strength of Spark lies in its execution model. Users define the transformations they want to apply to their data using a domain-specific language (DSL), and at runtime, Spark automatically constructs an optimized execution plan. For example, Spark can push a filter operation higher up in the transformation chain to load only the necessary data, improving efficiency.

However, this strength is also a limitation, as it requires learning a specific DSL. For deeper optimizations, a solid understanding of how Spark builds its execution plan is necessary.

One of the key challenges is the shuffle mechanism, which moves data between cluster nodes before processing it. Additionally, Spark’s distributed nature makes it relevant only for large-scale datasets, where parallel execution across multiple machines is beneficial. For instance, processing a CSV file with a few thousand rows can take several minutes with Spark, whereas pandas would handle it in just a few seconds.

Theodo’s point of view

We recommend choosing Spark only if you require high performance for complex transformations involving large data volumes (several terabytes). If this is not the case, it is better to use the SQL query engine of your data warehouse, avoiding the need to develop expertise in a complex technology.