How To Use Apache Spark For 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:

• 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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• Spark SQL

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

• 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.

• 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.

• 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.

• 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

Summing Up!

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 canada to learn more about Apache Spark and implement them in real-world projects with the assistance of trainers.