Everything You Need to Know About Big Data, Hadoop, Spark, and Pyspark Tech nologies

Everything You Need to Know About Big Data, Hadoop, Spark, and Pyspark Technologies

Big Data is the current buzzword in tech circles. Not just because of its application in businesses but also due to the number of employment opportunities that come with it.

Like every other industry, along with the applications and opportunities come the challenges. Therefore, it’s wise to do some research about the industry before diving in. 

And that’s where this article steps in. If you’re planning to switch career paths or are someone who’s just looking to get into this field, this article is for you. We’ll discuss what each of the technologies means and their benefits.

What is Big Data?

Big Data is simply a large set of data.

Unlike the usual large sets of data, Big Data is more complex. The data sets are so huge that they cannot be managed by traditional data processing software. But there’s a reason for the size. Big Data has enough information for businesses to tackle just about any problem.

Big Data comprises three V’s.

Volume: Volume refers to the amount of data that needs to be processed. The data is unstructured, has low density, and can take up as much as a few hundred petabytes of space.

Velocity: Velocity is the speed at which data is received and processed. Usually, faster data goes straight into memory over written on a disk.

Variety: Variety is the different types of data that need to be processed. Unlike traditional data, which is structured, big data types can be anything, like text, audio, and video. Also, different data types required additional processing phases to derive meaningful results.

Big Data is utilized in multiple sectors. Some of them are mentioned below.

• ◉ Product Development 

• ◉ Predictive Maintenance

• ◉ Customer Experience

• ◉ Fraud and Compliance

• ◉ Machine Learning

• ◉ Operational Efficiency

• ◉ Drive Innovation

What is Hadoop?

Created in 2005, Hadoop is an open-source framework that stores and analyzes big data, irrespective of size. Hadoop makes use of multiple computers in a network to effectively analyze huge data sets simultaneously.

Hadoop comes with 4 modules.

• ◉ Hadoop Distributed File System (HDFS): HDFS offers better data throughput than traditional file systems as it operates on standard low-end hardware. This distributed data file system has high fault tolerance and native support for large datasets.

• ◉ Yet Another Resource Negotiator (YARN): A job and task scheduler. It manages and supervises cluster nodes and resource usage.

• ◉ MapReduce: MapReduce is a map task that converts input data into a data set to be computed in key value pairs. This framework assists programs with parallel data computation.

• ◉ Hadoop Common: Contains Java libraries for use across all libraries.

Benefits of Hadoop

• ◉ Cost: Compared to traditional relational databases, Hadoop is cost-effective as it is open source and uses commodity hardware.

• ◉ Scalability: As a scalable model, Hadoop sends the data to be processed across machines in a cluster. The number of machines in a cluster can be increased or decreased as per enterprise requirements.

• ◉ Flexibility: Processing any type of data is easy with Hadoop, as it processes all kinds of datasets, like MySQL Data, XML, JSON, images, and videos.

• ◉ Speed: Hadoop usually breaks a large data set into smaller pieces and then distributes them to all computers in a cluster for faster and more accurate data processing. 

• ◉ Fault Tolerance: In the event of a crash, the data can be accessed from any machine in a cluster, as data is usually replicated.

• ◉ High Throughput: Throughput refers to a task completed per unit of time. Since each task (data set) is shared with multiple nodes, there is considerably less traffic in a Hadoop cluster.

What is Spark?

Created by the Apache Software Foundation, Spark is a super-fast computing technology aimed at quick computation. Spark is one of the ways in which Hadoop is implemented. Spark even uses Hadoop for storage purposes.

Spark Deployment

• ◉ Standalone: In a standalone deployment, Spark uses the place at the top of HDFS. It will work in tandem with MapReduce to complete all Spark jobs on a cluster.

• ◉ Hadoop Yarn: A Hadoop Yarn deployment is when Spark operates on Yarn minus root access or installation. This method allows other components to run at the top of the stack and blend Spark into the Hadoop ecosystem or Hadoop stack.

• ◉ Spark in MapReduce (SIMR): An SIMR deployment is when a user can start Spark and access its shell without the need for administrator access.

Spark Components

Spark has 5 components. They are listed below.

1. 1. Apache Spark Core: This is the general execution engine that offers in-memory computing and referencing datasets in external storage systems. All other features are built on the Spark Core.

2. 2. Spark SQL: This component is responsible for Schema RDD, a type of data abstraction that supports structured and semi-structured data.

3. 3. Spark Streaming: This component performs streaming analytics using Spark Core’s fast scheduling property. It takes in data in small batches and runs Resilient Distributed Datasets (RDD) transformations.

4. 4. MLib (Machine Learning Library): This component is Spark’s machine learning library that comprises high-quality algorithms that utilize iteration and produce better results than MapReduce.

5. 5. GraphX: GraphX is a graph-processing framework that is used to analyze and process large graphs.

Benefits of Spark

Considering how fast Apache Spark is, it comes with a multitude of benefits. Some are mentioned below.

• ◉ Speed: When it comes to processing speed, Spark is 100 times faster than Hadoop, as Spark uses in-memory (RAM) to store data.

• ◉ Easy to use: Spark comes with a host of different Application Programming Interfaces (API) to operate on large datasets.

• ◉ Advanced Analytics: In addition to offering support for MAP and reduce, Spark also provides support for Machine Learning (ML), graph algorithms, streaming data, and more. 

• ◉ Dynamic: The 80 high-level operators in Apache Spark allow you to build parallel applications.

• ◉ Open Source: There’s always someone to help with issues, as Apache Spark is open source.

Career Opportunities

Taking the numerous benefits into consideration, the opportunities for Spark and Hadoop only seem to be growing, especially in the retail, finance, banking, and IT sectors. An individual with a degree in computer programming and expertise in the basics of computer programming (particularly Java) can flourish in the field of Spark.

The following job roles are apt for professionals who’ve mastered Spark.

• ◉ Software Developer

• ◉ Apache Spark Application Developer

• ◉ Spark Developer

• ◉ Data Engineer

• ◉ Apache Spark Expert

What is PySpark?

Written for Spark libraries, Pyspark is used to perform large-scale data processing in a distributed environment. It is a Python API that supports all of Spark’s features and allows data processing of any size.

Big Data with Pyspark utilizes a master-slave architecture, where the master is the driver and the slave is the worker.

PySpark Modules:

• ◉ PySpark RDD

• ◉ PySpark DataFrame and SQL

• ◉ PySpark Streaming

• ◉ PySpark MLib

• ◉ PySpark GraphFrames

• ◉ PySpark Resources

Benefits of PySpark

• ◉ Easy to Integrate: The PySpark framework supports languages like Scala, Java, and more, making it easy to integrate with other languages.  

• ◉ Speed: PySpark is faster compared to traditional data processing frameworks. 

• ◉ Dynamic: Utilizing over 80 high-level operators, PySpark allows you to develop parallel applications.

Career Opportunities

The implementation of Python for Big Data has made PySpark an exciting career choice. The qualifications are similar to Spark: a graduation in the computer science stream and proficiency in a programming language. Additional certifications may help you get an edge over other candidates.

Individuals who are interested in a career in Big Data with PySpark can expect the following job roles:

• ◉ Software Engineer

• ◉ Apache PySpark Developer

• ◉ PySpark Application Developer

• ◉ Data Engineer

• ◉ PySpark Application Lead

• ◉ Python/PySpark Developer

• ◉ Business Analyst

• ◉ Data Engineer

• ◉ PySpark Glue Developer

• ◉ PySpark Databricks Developer

Final Thoughts

Big Data is an exciting industry to watch out for. With the number of businesses implementing Big Data rising each day, planning a career in the industry is a wise move. 

Although YouTube tutorials are great, nothing beats an expert with industry and teaching experience sharing their knowledge with aspirants. This is where specialized coaching centers step in.

With the right amount of training and effort, you, too, can build a rewarding careers in Big Data.