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Basics

PySpark collect

PySpark Create DataFrame from List

PySpark TimeStamp

PySpark FlatMap

PySpark withColumnRenamed

PySpark Sort

PySpark to_Date

What is Apache Spark?

Career in Spark

Spark Commands

How to Install Spark

Spark Versions

Apache Spark Architecture

Spark Tools

Spark Shell Commands

Spark Functions

RDD in Spark

Spark DataFrame

Spark Dataset

Spark Components

Apache Spark

Spark Stages

Spark Streaming

Spark Parallelize

Spark Repartition

Spark Shuffle

Spark Parquet

Spark Submit

Spark YARN

SparkContext

Spark Cluster

Spark SQL Dataframe

Join in Spark SQL

What is RDD?

Spark RDD Operations

Spark Broadcast

Spark Executor

Spark flatMap

Spark Thrift Server

Spark Accumulator

Spark web UI

Spark Interview Questions

PySpark

PySpark version

PySpark Select Columns

PySpark withColumn

PySpark foreach

PySpark when

PySPark Groupby

PySpark OrderBy Descending

PySpark GroupBy Count

PySpark Window Functions

PySpark Round

PySpark substring

PySpark Filter

PySpark Union

PySpark Map

PySpark SQL

PySpark Histogram

PySpark row

PySpark rename column

PySpark Coalesce

PySpark parallelize

PySpark read parquet

PySpark Join

PySpark Left Join

PySpark Alias

PySpark Column to List

PySpark structtype

PySpark Broadcast Join

PySpark Lag

PySpark count distinct

PySpark pivot

PySpark explode

PySpark Repartition

PySpark SQL Types

PySpark Logistic Regression

PySpark mappartitions

Spark Tutorial

Apache spark is one of the largest open-source projects used for data processing. Spark is a lightning-fast and general unified analytical engine used in big data and machine learning. It supports high-level APIs in a language like JAVA, SCALA, PYTHON, SQL, and R.It was developed in 2009 in the UC Berkeley lab now known as AMPLab. As spark is the engine used for data processing it can be built on top of Apache Hadoop, Apache Mesos, Kubernetes, standalone and on the cloud like AWS, Azure or GCP which will act as a data storage.

Apache spark has its own stack of libraries like Spark SQL, DataFrames, Spark MLlib for machine learning, GraphX graph computation, Streaming this library can be combined internally in the same application.

Why Do We Need to Learn Spark?

In today's era data is the new oil but data exists in different forms like structured, semi-structured and unstructured. Apache Spark achieves high performance for batch and streaming data. Big internet companies like Netflix, Amazon, yahoo, facebook have started using spark for deployment and uses a cluster of around 8000 nodes for storing petabytes of data. As day by day technology is moving ahead and to keep up with the same Apache spark is must and below are some reason to learn:

  1. Spark is 100 times faster in-memory than MapReduce and it can integrate with the Hadoop ecosystem easily hence use of spark is increasing in big and small companies. As it is the open-source most of the organizations have already implemented spark.

 

  1. As data is generated from mobile apps, websites, IOTs, sensors, etc. and this huge data is not easy to handle and process. spark provides real-time processing to this data. Spark has speed and ease of use with Python and SQL language hence most machine learning engineers and data scientists prefer spark.

 

  1. Spark programming can be done in Java, Python, Scala and R and most professional or college student has prior knowledge. Prior knowledge helps learners create spark applications in their known language. Also, the scala in which spark has developed is supported by java. Also, 100-200 lines of code written in java for a single application can be converted to

 

  1. Spark professional has a high demand in today's market and recruiter are ready to bend some rules by providing a high salary to spark developers. As there are high demand and low supply in Apache spark professionals It is the right time to get into this technology to earn big bucks.

Applications of Spark

Apache spark ecosystem is used by industry to build and run fast big data applications, here are some application of sparks:

  1. In the e-commerce industry:

To analyze the real-time transaction if a product, customers, and sales in-store. This information can be passed to different machine learning algorithms to build a recommendation model. This recommendation model can be developed based on customer comments and product review and industry can form new trends.

  1. In the gaming industry:

As a spark process, real-time data programmers can deploy models in a minute to build the best gaming experience. Analyze players and their behavior to create advertising and offers. Also, spark a use to build real-time mobile game analytics.

  1. In Financial Services:

Apache spark analysis can be used to detect fraud and security threats by analyzing a huge amount of archived logs and combine this with external sources like user accounts and internal information Spark stack could help us to get top-notch results from this data to reduce risk in our financial portfolio

Example (Word Count Example):

In this example we are counting the number of words in a text file:

Word Count Example

Output:

Word Count Output

Pre-requisites

To learn Apache Spark programmer needs prior knowledge of Scala functional programming, Hadoop framework, Unix Shell scripting, RDBMS database concepts, and Linux operating system. Apart from this knowledge of Java is can be useful. If one wants to use Apache PySpark then knowledge of python is preferred.

Target Audience

Apache spark tutorial is for the professional in analytics and data engineer field. Also, professionals aspiring to become Spark developers by learning spark frameworks from their respective fields like  ETL developers, Python Developers can use this tutorial to make a transition in big data.