Using Apache Spark For Efficient Data Science Projects

Using Apache Spark For Effiecient
Data Science Projects

Apache Spark is one of the top open-source data processing and analytics, data science
projects. It offers a common language to program distributed storage systems and provides
high-level libraries for network programming and scalable cluster computing. With its own
cluster manager and scheduler, it can easily be enabled on your existing Hadoop or big data
platform.

The Apache Software Foundation introduced Spark to speed up Hadoop's computational
computing software. Contrary to popular assumption, Spark is not a modified version of
Hadoop and is not truly dependent on Hadoop due to its own cluster management.

This article will dive into some of the key elements of Spark and how it's used for data
science.

What is Apache Spark?

Apache Spark is a powerful, general-purpose cluster computing engine. It was first released
by the Apache Software Foundation (ASF) and has since become one of the most popular
open-source products for big data processing. With Apache Spark, data scientists can use
familiar tools from their favorite languages to perform massively parallel processing tasks in
seconds.

It is an open-source cluster-based data processing engine for general-purpose big data
computing. It has great potential in many businesses, including traditional enterprises and
internet companies. Spark provides high-level APIs for applications to access large amounts
of structured and unstructured data that live in memory, on disk or in NoSQL stores like
Kafka. Spark also includes libraries for machine learning algorithms like MLlib and MLlib
GraphX.

Why is Apache Spark Popular over Hadoop?

Spark was initially developed as a solution to some challenges faced by Hadoop
MapReduce frameworks in recent years. These challenges include:

● The need for greater scalability
● The need for higher throughput
● The need for faster execution speed

Apache Spark is a framework for distributed computing that cuts across the MapReduce
paradigm in Hadoop. It has advantages over Hadoop in terms of functional programming
interfaces and memory management, but it also comes with its own data storage layer and
support tools. Due to its in-memory processing and use of MLib for computations, Spark is

significantly faster. The appeal of Apache Spark lies in its ability to process data faster and
more efficiently than Hadoop, which makes it popular among data scientists.

Benefits of Apache Spark:

● Speed — Spark makes it possible to execute Hadoop cluster applications up to 100
times quicker in memory and 10 times faster in storage. This can be accomplished by
limiting the number of disk reads and writes. Storage of intermediary processing data
is done in memory.

● Support multiple languages — Java, Scala, and Python- directly supported by
Spark. As a result, a variety of programming languages are available for use in
developing applications. For interactive querying, Spark provides 80 high-level
operators.

● Advanced analytics — Not only can you perform a "map" or "reduce" in Spark. The
platform supports SQL queries, streaming data, machine learning (ML), and graph
algorithms.

Components of Spark:

1. Apache Spark Core

As a foundation for all other features, Spark Core is the basic execution engine.
It is in charge of the following:

● Memory management and faulty error handling
● Scheduling, distributing and maintaining work in a cluster
● Interacting with storage devices and infrastructure

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2. Spark SQL

Structured and semi-structured data are supported by SchemaRDD, which is a new data
abstraction introduced by Spark SQL.

3. Spark Streaming

Spark Streaming can process streaming data in real time, such as web server log files (such
as Apache Flume and HDFS/S3) and social media posts like those from Twitter. Spark
Streaming takes data streams and splits the data into batches. These findings are compiled
into a final stream in batches using Spark. RDD transformations are performed on the
ingested data, which is ingested in mini-batches.
The Spark Streaming API closely resembles the Spark Core API, making it easy for
developers to deal with batch and streaming data.

4. MLlib (Machine Learning Library)

MLlib, a library of machine learning algorithms, is part of Spark's ML framework.
Classification, regression, clustering, collaborative filtering, and other machine learning
techniques can all be implemented using MLlib. Some methods, such as linear regression
using ordinary least squares or k-means clustering, can also operate with streaming data.
Apache Mahout, a machine learning framework for Hadoop, has already deserted
MapReduce and joined forces with Spark MLlib.

5. GraphX:

GraphX is a distributed graph-processing platform built on the Spark programming
framework. It provides an API for Pregel abstraction that may represent user-defined graphs
to express graph processing. Additionally, it offers a faster time to execution for this
abstraction. For example, it has a library of standard algorithms for network modification,
such as PageRank.

6. SparkR

Data scientists use SparkR to analyze massive data in the R shell. It makes use of R's
usability as well as its scalability.

Data Science with Apache Spark

● Text Analytics is one of the key aspects of Apache Spark used in Data Science.
Apache Spark excels at dealing with unstructured data.

This unstructured data is gathered primarily via conversations, phone calls, tweets, posts,
etc. For analyzing this data, Spark offers a scalable distributed computing platform.

Some of the many methods that Spark supports for text analytics include:
➔ Text Mining
➔ Entity Extraction
➔ Categorization
➔ Sentiment Analysis
➔ Deep Learning

● Distributed Machine Learning is another significant branch of data science
embraced by Spark. The MLlib subproject of Spark offers support for machine
learning operations. Within the MLlib project, some of the available algorithms are:

➔ Regression – Logistic Regression, linear SVM
➔ Classification – Regression Tree
➔ Collaborative filtering – Alternating Least Squares
➔ Clustering – k-means clustering
➔ Optimization techniques – Stochastic Gradient Descent

Summary

In a nutshell, Apache Spark is a powerful open-source cluster computing framework for big
data processing. With the big data ecosystem growing, it's no surprise that Spark has picked
up significant steam, with over 26% of Hadoop users leveraging Spark as a component of
their big data stack. This blog was meant to give you an overall introduction and overview of
this popular tool to help you get started leveraging Apache Spark in your own big data
projects. Join the IBM-accredited data science course in Mumbai to learn more about
Apache Spark and implement them in real-world projects with the assistance of trainers.