Elements of a Spark Project

A Spark project comprises different elements such as:

  • Spark Core and Resilient Distributed Datasets or RDDs
  • Spark SQL
  • Spark Streaming
  • Machine Learning Library or MLlib
  • GraphX

Let us now speak in-depth about each element.

Spark Core and RDDs:

The basis of the overall Spark project is Spark Core and RDDs. They offer required functions for Input/output, distributed task deploying, and planning. Spark Training is the best choice to operate in the Spark project. 

RDDs are the fundamental abstraction of programming and are a set of data that is logically partitioned through computers. By implementing coarse-grained transformations to existing RDDs or by referencing external datasets, RDDs can be generated.

The instances are decreased, combine, filter, and charts for these transforms.

Like in-process and localized arrays, the abstraction of RDDs is revealed via a language-integrated Application Programming Interface or API in Python, Java, and Scala.

As a consequence, the abstraction of RDD simplifies the programming complexity, as the way programs modify RDDs is analogous to modifying local data collections.

Spark SQL:

At the top of Spark Core, Spark SQL resides. It introduces a new data abstraction, SchemaRDD, which supports semi-structured and structured data.

SchemaRDD can be exploited by Spark SQL in any of the domain-specific offered, such as Java, Scala, and Python. Spark SQL also supports Open Database Connectivity or Java Database Connectivity, SQL, usually referred to as ODBC or JDBC database and command-line frameworks.

Spark Streaming:

Spark Streaming leverages Spark Core’s fast scheduling capability to stream analytics, Ingest small batches of data and perform RDD transformations on them.

On a single-engine for streaming analytics, the same framework code set written for batch data analysis can be used with this design.

Machine Learning Library:

The Machine Learning Library also referred to as MLlib, lies on top of Spark and is a distributed platform for machine learning.

MLlib applies various general algorithms for statistical and machine learning. It is nine times faster than the Apache Mahout Hadoop disk-Based version with its shared memory architecture.

The library even performs much better than VW or Vowpal Wabbit. A fast out-of-core learning framework supported by Microsoft is the VW project.


GraphX is also located on top of Spark and is a distributed system for processing graphs. It offers an API and an efficient Pregel abstraction runtime for the measurement of graphs.

Pregel is a framework for Graph processing on a wide scale. The API can model the abstraction of Pregel as well. We discussed earlier that Spark offers a few apps with in-memory primitives with up to 100 times better results.

Let’s address the implementation of in-memory processing using column-centric databases in the next section.

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