Analysis Of Big Data Using Spark And Scala

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Last updated on
06th Jun, 2022
10th Jan, 2017
Analysis Of Big Data Using Spark And Scala

The use of Big Data over a network cluster has become a major application in multiple industries. The wide use of MapReduce and Hadoop technologies is proof of this evolving technology, along with the recent rise of Apache Spark, a data processing engine written in Scala programming language. Before diving into this topic, let's see what is big data, and the types of big data

Introduction to Scala

Scala is a general purpose object-oriented programming language, similar to Java programming. Scala is an acronym for “Scalable language” meaning its capabilities can grow along the lines of your requirements & also there are more technologies built on scala.

The capabilities of Scala programming can range from a simple scripting language to the preferred language for mission-critical applications.

Scala has the following capabilities:

  • Support for functional programming, with features including currying, type interference, immutability, lazy evaluation, and pattern matching.
  • An advanced type system including algebraic data types and anonymous types.
  • Features that are not available in Java, like operator overloading, named parameters, raw strings, and no checked exceptions.

Scala can run seamlessly on a Java Virtual Machine (JVM), and Scala and Java classes can be freely interchanged or can refer to each other.

Scala also supports cluster computing, with the most popular framework solution, Spark, which was written using Scala.

Introduction to Apache Spark

Apache Spark is an open-source Big Data processing framework that provides an interface for programming data clusters using data parallelism and fault tolerance. Apache Spark is widely used for fast processing of large datasets.

Apache Spark is an open-source platform, built by a wide set of software developers from over 200 companies. Since 2009, more than 1000 developers have contributed to Apache Spark.

Apache Spark provides better capabilities for Big Data applications, as compared to other Big Data technologies such as Hadoop or MapReduce. Listed below are some features of Apache Spark:

1. Comprehensive framework

Spark provides a comprehensive and unified framework to manage Big Data processing, and supports a diverse range of data sets including text data, graphical data, batch data, and real-time streaming data.

2. Speed

Spark can run programs up to 100 times faster than Hadoop clusters in memory, and 10 times faster when running on disk. Spark has an advanced DAG (directed acrylic graph) execution engine that provides support for cyclic data flow and in-memory data sharing across DAGs to execute different jobs with the same data.

3. Easy to use

With a built-in set of over 80 high-level operators, Spark allows programmers to write Java, Scala, or Python applications in quick time.

4. Enhanced support

In addition to Map and Reduce operations, Spark provides support for SQL queries, streaming data, machine learning, and graphic data processing.

5. Can be run on any platform

Apache Spark applications can be run on a standalone cluster mode or in the cloud. Spark provides access to diverse data structures including HDFS, Cassandra, HBase, Hive, Tachyon, and any Hadoop data source. Spark can be deployed as a standalone server or on a distributed framework such as Mesos or YARN.

6. Flexibility

In addition to Scala programming language, programmers can use Java, Python, Clojure, and R to build applications using Spark.

Comprehensive library support

As a Spark programmer, you can combine additional libraries within the same application, and provide Big Data analytical and Machine learning capabilities.

The supported libraries include:

  • Spark Streaming, used for processing of real-time streaming data.
  • Spark SQL, used for exposing Spark datasets over JDBC APIs and for executing SQL-like queries on Spark datasets.
  • Spark MLib, which is the machine learning library, consisting of common algorithms and utilities.
  • Spark GraphX, which is the Spark API for graphs and graphical computation .
  • BlinkDB, a query engine library used for running interactive SQL queries on large data volumes.
  • Tachyon, which is a memory-centric distributed file system to enable file sharing across cluster frameworks.
  • Spark Cassandra Connector and Spark R, which are integration adapters. With Cassandra Connector, Spark can access data from the Cassandra database and perform data analytics.

Compatibility with Hadoop and MapReduce

Apache Spark can be much faster as compared to other Big Data technologies.

Apache Spark can run on an existing Hadoop Distributed File System (HDFS) to provide compatibility along with enhanced functionality. It is easy to deploy Spark applications on existing Hadoop v1 and v2 cluster. Spark uses the HDFS for data storage, and can work with Hadoop-compatible data sources including HBase and Cassandra.

Apache Spark is compatible with MapReduce and enhances its capabilities with features such as in-memory data storage and real-time processing.


The standard API set of Apache Spark framework makes it the right choice for Big Data processing and data analytics. For client installation setups of MapReduce implementation with Hadoop, Spark and MapReduce can be used together for better results.

Apache Spark is the right alternative to MapReduce for installations that involve large amounts of data that require low latency processing



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