Learn Apache Spark in 10 Minutes | Step by Step Guide

Summary

This script delves into the evolution of data processing with the advent of Big Data, highlighting Hadoop's role in distributed data processing and its limitations. It introduces Apache Spark as a solution, detailing its in-memory processing via RDD for speed and versatility across languages. The script explains Spark's components, architecture, and execution model, emphasizing its efficiency and real-time data processing capabilities. It concludes with a practical guide on using Spark for data engineering projects, encouraging further exploration.

Takeaways

• 📈 **Data Explosion**: 90% of the world's data was generated in the last two years, with exponential growth due to the internet, social media, and digital technologies.
• 🧩 **Big Data Challenges**: Organizations face challenges in processing massive volumes of data, leading to the emergence of Big Data concepts.
• 🛠️ **Hadoop's Role**: Hadoop, developed by Yahoo in 2006, introduced distributed data processing, inspired by Google's MapReduce and Google File System.
• 🔄 **Distributed Processing**: Hadoop allows for data processing across multiple computers, improving efficiency through parallel processing.
• 💾 **Hadoop Components**: Hadoop consists of HDFS for storage and MapReduce for processing, dividing data into chunks and processing them across different machines.
• 🚀 **Spark's Advantage**: Apache Spark, developed in 2009, addressed Hadoop's limitations by introducing in-memory data processing, making it significantly faster.
• 💡 **RDD - Resilient Distributed Dataset**: Spark's core is RDD, enabling faster data access and processing by storing data in memory.
• 🌐 **Spark Ecosystem**: Spark includes components like Spark Core, Spark SQL for SQL queries, Spark Streaming for real-time data, and MLlib for machine learning.
• 🔧 **Spark Architecture**: Spark manages task execution across a cluster, with a cluster manager, driver processes (boss), and executor processes (workers).
• 💻 **Spark Session**: To write Spark applications, one must first create a Spark session, which is the entry point for connecting with the cluster manager.

Q & A

What is the significance of the statement that 90% of the world's data was generated in just the last two years?

This statement highlights the exponential growth of data generation due to the widespread use of the internet, social media, and digital technologies, emphasizing the need for advanced data processing methods.

How does Big Data differ from traditional data sets in terms of processing?

Big Data refers to extremely large and complex data sets that are difficult to process using traditional methods due to their volume, variety, and velocity, requiring specialized technologies like Hadoop for efficient processing.

What inspired the development of Hadoop, and what problem was it designed to solve?

Hadoop was developed by engineers at Yahoo, inspired by Google's MapReduce and Google File System technology, to address the challenge of processing massive volumes of data that were difficult to handle with traditional methods.

What are the two main components of Hadoop and their functions?

The two main components of Hadoop are Hadoop Distributed File System (HDFS), which serves as a storage system for large datasets across multiple computers, and MapReduce, which is a programming model for processing large datasets in parallel.

Why was there a need for a technology like Apache Spark to overcome Hadoop's limitations?

Hadoop had limitations such as reliance on disk storage, which made data processing slower, and its batch processing nature, which didn't allow for real-time data processing. Apache Spark was developed to address these issues by introducing in-memory data processing and real-time data analytics.

What is RDD in Apache Spark, and how does it contribute to faster data processing?

RDD stands for Resilient Distributed Dataset, which is the backbone of Apache Spark. It allows data to be stored in memory, enabling faster data access and processing by avoiding the need to repeatedly read and write data from disk.

How does Apache Spark's in-memory processing make it significantly faster than Hadoop?

Apache Spark's in-memory processing allows it to process data directly from RAM, which is much faster than disk-based processing in Hadoop. This approach makes Spark up to 100 times faster than Hadoop for certain operations.

What are the different components of the Apache Spark ecosystem mentioned in the script?

The components of the Apache Spark ecosystem include Spark Core for general data processing, Spark SQL for SQL query support, Spark Streaming for real-time data processing, and MLlib for large-scale machine learning on Big Data.

Can you explain the role of the driver and executor processes in a Spark application?

In a Spark application, the driver process acts as the manager, coordinating and tracking the application's tasks, while the executor processes are the workers that execute the code assigned by the driver and report back the computation results.

What is the concept of lazy evaluation in Apache Spark, and how does it impact the execution of code?

Lazy evaluation in Apache Spark means that the execution of transformations is deferred until an action is called. This allows Spark to optimize the execution plan based on the entire code written, leading to more efficient data processing.

How does Apache Spark handle the creation and manipulation of data frames, and what is the significance of partitioning?

Apache Spark creates data frames, which are distributed across multiple computers, to represent data in rows and columns. Partitioning is the process of dividing data into chunks to enable parallel processing, which is essential for efficient data manipulation and execution in Spark.

Outlines

00:00

📈 The Emergence of Big Data and Hadoop

The paragraph discusses the exponential growth of data in the early 2000s due to the internet, social media, and digital technologies. It introduces the concept of Big Data, which refers to large and complex data sets that are difficult to process using traditional methods. To address this, Hadoop was developed in 2006 by Yahoo engineers, inspired by Google's MapReduce and Google File System. Hadoop introduced distributed processing, allowing multiple computers to process data simultaneously. It has two main components: Hadoop Distributed File System (HDFS) for storage and MapReduce for parallel data processing. However, Hadoop faced limitations such as slow data processing due to reliance on disk storage and batch processing, which required waiting for one process to complete before starting another.

05:00

🔥 Introducing Apache Spark: Overcoming Hadoop's Limitations

This paragraph explains the need for a faster and real-time data processing solution, leading to the development of Apache Spark in 2009 by researchers at the University of California, Berkeley. Spark was designed to overcome Hadoop's limitations by introducing the Resilient Distributed Dataset (RDD), which allows data to be stored in memory for faster access and processing. Spark is significantly faster than Hadoop, with in-memory processing capabilities that can be 100 times quicker. It supports multiple programming languages and includes components like Spark Core for data processing, Spark SQL for SQL queries, Spark Streaming for real-time data processing, and MLlib for large-scale machine learning. The paragraph also outlines the basic architecture of Spark, emphasizing the need for a framework to coordinate data processing across multiple computers.

10:01

💻 Apache Spark's Architecture and Execution Process

The final paragraph delves into the architecture of Apache Spark, focusing on the cluster manager's role in resource allocation for Spark applications. It distinguishes between driver processes, which manage and coordinate tasks, and executor processes, which perform the actual data processing. The paragraph explains the process of writing Spark applications, starting with creating a Spark session to connect with the cluster manager. It discusses the creation of data frames, their partitioning, and the use of transformations and actions to process data. The concept of lazy evaluation in Spark is highlighted, where the execution of transformations is deferred until an action is called. The paragraph concludes with an example of reading a dataset, creating a temporary view for SQL queries, and demonstrating lazy evaluation with a filter transformation followed by an action to display results.

Keywords

💡Big Data

Big Data refers to extremely large and complex data sets that are difficult to process using traditional methods. In the video, Big Data is the central theme as it discusses the challenges organizations face in processing vast amounts of data generated by the internet, social media, and digital technologies. The video explains how Big Data has led to the development of new technologies like Hadoop and Apache Spark to handle and derive insights from this data.

💡Hadoop

Hadoop is a software framework introduced by Yahoo engineers in 2006, inspired by Google's MapReduce and Google File System technology. It is mentioned in the video as a solution to the challenge of processing Big Data. Hadoop introduced distributed processing, allowing multiple computers to process data simultaneously, which is likened to teamwork in the script. It has two main components: Hadoop Distributed File System (HDFS) for storage and MapReduce for processing data in parallel.

💡Distributed Processing

Distributed Processing is a method of data processing where multiple computers work together to process data simultaneously. The video explains that Hadoop introduced this concept, where instead of relying on a single machine, a cluster of machines can divide the data into chunks and process it in parallel. This approach is crucial for handling the massive volume of Big Data and is exemplified by the teamwork analogy used in the script.

💡Hadoop Distributed File System (HDFS)

HDFS is a component of the Hadoop framework that serves as a storage system for large datasets. As described in the video, it divides data into multiple chunks and stores them across different computers, allowing for efficient data management in a distributed computing environment. HDFS is integral to the functioning of Hadoop, enabling the storage of vast amounts of data that can be processed in parallel.

💡MapReduce

MapReduce is a programming model and an associated implementation for processing and generating large datasets, as explained in the video. It is a key component of Hadoop that facilitates parallel processing of data. The video uses the analogy of a team of friends working on a large puzzle to illustrate how MapReduce divides data into chunks, processes them in parallel, and then combines the results to get the final output.

💡Apache Spark

Apache Spark is an open-source, distributed computing system introduced in 2009 by researchers at the University of California, Berkeley. The video highlights Spark as a solution to the limitations of Hadoop, particularly its reliance on disk storage and batch processing. Spark introduces in-memory processing through its core concept of Resilient Distributed Dataset (RDD), which significantly speeds up data processing compared to Hadoop.

💡Resilient Distributed Dataset (RDD)

RDD is a fundamental concept in Apache Spark that allows data to be stored in memory, enabling faster data access and processing. The video explains that RDD is the backbone of Spark, which contrasts with Hadoop's disk-based processing. By processing data in memory, Spark achieves a speedup of 100 times compared to Hadoop, as mentioned in the script.

💡In-Memory Processing

In-memory Processing is a technique where data is processed directly from the computer's RAM (Random Access Memory) instead of reading and writing from disk storage. The video emphasizes that Apache Spark's use of in-memory processing through RDDs is what makes it significantly faster than Hadoop. This approach allows for quicker data manipulation and analysis, which is crucial for real-time data processing.

💡Spark SQL

Spark SQL is a component of the Apache Spark ecosystem that allows users to write SQL queries directly on their datasets. The video mentions Spark SQL as one of the features that make Spark versatile, enabling data scientists and analysts to leverage their SQL knowledge to query and analyze data stored in Spark.

💡Spark Streaming

Spark Streaming is another component of the Apache Spark ecosystem, designed for processing real-time data streams. The video gives examples like Google Maps or Uber, where real-time data processing is essential. Spark Streaming allows for the processing of data in real-time, which is a significant advancement over Hadoop's batch processing limitations.

💡MLlib

MLlib is a machine learning library in Apache Spark that is used for training large-scale machine learning models on Big Data. The video mentions MLlib as the component of Spark that facilitates machine learning, making it a powerful tool for data scientists to build and deploy machine learning models on Big Data.

Highlights

Ninety percent of the world's data was generated in just the last two years.

The amount of data being generated exploded exponentially with the use of the internet, social media, and various digital technologies.

Organizations faced a massive volume of data that was very hard to process.

Big Data refers to extremely large and complex data sets that are difficult to process using traditional methods.

Hadoop introduced a new way of data processing called distributed processing.

Hadoop Distributed File System (HDFS) is like the giant storage system for keeping our dataset.

MapReduce is a super smart way of processing all of this data together.

Apache Spark was developed to address the limitations of Hadoop.

RDD (Resilient Distributed Dataset) is the backbone of Apache Spark, allowing data to be stored in memory for faster processing.

Spark is 100 times faster than Hadoop due to its in-memory processing.

Spark allows writing code in various programming languages such as Python, Java, and Scala.

Spark Core helps with processing data across multiple computers.

Spark SQL enables writing SQL queries directly on datasets.

Spark Streaming allows processing real-time data, like in Google Maps or Uber.

MLlib is used for training large-scale machine learning models on Big Data using Spark.

Apache Spark manages and coordinates the execution of tasks on data across a cluster of computers.

The driver processes in Spark are like a boss, and the executor processes are like workers.

Spark uses lazy evaluation, waiting until the entire code is written before executing.

Actions in Spark trigger the execution of transformation blocks, such as the count action to get the total number of records.

The Spark session is the entry point for the Spark application, connecting with the cluster manager.

Transformations in Spark are instructions that tell how to modify the data and get the desired result.

Apache Spark can import data, convert it into a table, and write SQL queries on top of it.

Spark can convert a Spark data frame into a Pandas data frame for applying Pandas functions.

Transcripts

Ninety percent of the world's data was generated  in just the last two years. In the early 2000s,  

the amount of data being generated exploded  exponentially with the use of the internet,  

social media, and various digital  technologies. Organizations found  

themselves facing a massive volume of  data that was very hard to process.  

To address this challenge, the  concept of Big Data emerged.

Big Data refers to extremely large and complex  data sets that are difficult to process using  

traditional methods. Organizations across the  world wanted to process this massive volume  

of data and derive useful insights from it.  Here's where Hadoop comes into the picture.

In 2006, a group of engineers at Yahoo developed  a special software framework called Hadoop. They  

were inspired by Google's MapReduce and Google  File System technology. Hadoop introduced a new  

way of data processing called distributed  processing. Instead of relying on a single  

machine, we can use multiple computers to get  the final result. Think of it like teamwork:  

each machine in a cluster will get some part of  the data to process. They will work simultaneously  

on all of this data, and in the end, we will  combine the output to get the final result.

There are two main key components of Hadoop.  One is Hadoop Distributed File System (HDFS),  

which is like the giant storage system for  keeping our dataset. It divides our data  

into multiple chunks and stores all of this  data across different computers. The second  

part of Hadoop is called MapReduce, which is a  super smart way of processing all of this data  

together. MapReduce helps in processing all  of this data in parallel. So, you can divide  

your data into multiple chunks and process them  together, similar to a team of friends working  

to solve a very large puzzle. Each person in  the team gets a part of the puzzle to solve,  

and in the end, we put everything  together to get the final result.

So, with Hadoop, we have two things:  HDFS (Hadoop Distributed File System),  

which is used for storing our data across multiple  computers, and MapReduce, which is used to process  

all of this data in parallel. It allowed  organizations to store and process very large  

volumes of data. But here's the thing, although  Hadoop was very good at handling Big Data,  

there were a few limitations. One of the  biggest problems behind Hadoop was that it  

relied on storing data on disk, which made  things much slower. Every time we run a job,  

it would store the data onto the  disk, read the data, process it,  

and then store that data again through a disk.  This made the data processing a lot slower.  

Another issue with Hadoop was that it processed  data only in batches. This means we had to wait  

for one process to complete before submitting  any other job. It was like waiting for the whole  

group of friends to complete their puzzles  individually and then putting them together.

So, there was a need to process all of this  data faster and in real-time. Here's where  

Apache Spark comes into the picture. In 2009,  researchers at the University of California,  

Berkeley, developed Apache Spark as  a research project. The main reason  

behind the development of Apache Spark was  to address the limitations of Hadoop. This  

is where they introduced the powerful concept  called RDD (Resilient Distributed Dataset).

RDD is the backbone of Apache Spark. It allows  data to be stored in memory and enables faster  

data access and processing. Instead of reading  and writing the data repeatedly from the disk,  

Spark processes the entire data in just memory.  The meaning of memory here is the RAM (Random  

Access Memory) stored inside our computer. And  this in-memory processing of data makes Spark  

100 times faster than Hadoop. Yes, you heard it  right, 100 times faster than Hadoop. Additionally,  

Spark also gave the ability to write code in  various programming languages such as Python,  

Java, and Scala. So, you can  easily start writing Spark  

applications in your preferred language  and process your data on a large scale.

Apache Spark became very famous because  it was fast, could handle a lot of data,  

and process it efficiently. Here are the different  components attached to Apache Spark. One of the  

most important parts of the Spark ecosystem is  called Spark Core. It helps with processing data  

across multiple computers and ensures everything  works efficiently and smoothly. Another part is  

Spark SQL. So, if you want to write SQL queries  directly on your dataset, you can easily do that  

using Spark. Then there is Spark Streaming. If  you want to process real-time data that you see  

in Google Maps or Uber, you can easily do that  using Apache Spark Streaming. And at the end, we  

have MLlib. MLlib is used for training large-scale  machine learning models on Big Data using Spark.

With all of these components working together,  

Apache Spark became a powerful tool for  processing and analyzing Big Data. Nowadays,  

in any company, you will see Apache  Spark being used to process Big Data.

Now, let's understand the basic architecture  behind Apache Spark. When you think of a computer,  

a standalone computer is generally  used to watch movies, play games,  

or anything else. But when you  want to process large Big Data,  

you can't do that on a single computer. You need  multiple computers working together on individual  

tasks so that you can combine the output at  the end and get the desired result. You can't  

just take ten computers and start processing  your Big Data. You need a proper framework to  

coordinate work across all of these different  machines, and Apache Spark does exactly that.

Apache Spark manages and coordinates  the execution of tasks on data across  

a cluster of computers. It has something called a  cluster manager. When we write any job in Spark,  

it is called a Spark application. Whenever we  run anything, it goes to the cluster manager,  

which grants resources to all applications  so that we can complete our work.

In a Spark application, we have two  important components: the driver  

processes and the executor processes.  The driver processes are like a boss,  

and the executor processes are like workers. The  main job of the driver processes is to keep track  

of all the information about the Apache Spark  application. It will respond to the command and  

input from the user. So, whenever we submit  anything, the driver process will make sure  

it goes through the Apache Spark application  properly. It analyzes the work that needs to be  

done, divides our work into smaller tasks, and  assigns these tasks to executor processes. So,  

it is basically the boss or a manager who  is trying to make sure everything works  

properly. The driver process is the heart of the  Apache Spark application because it makes sure  

everything runs smoothly and allocates the  right resources based on the input that we  

provide. Executor processes are the ones that  actually do the work. They execute the code  

assigned by the driver process and report back  the progress and result of the computation.

Now, let's talk about how Apache Spark executes  the code in practice. When we actually write  

our code in Apache Spark, the first thing  we need to do is create the Spark session.  

It is basically making the connection with the  cluster manager. You can create a Spark session  

with any of these languages: Python, Scala,  or Java. No matter what language you use to  

begin writing your Spark application, the first  thing you need to create is a Spark session.

You can perform simple tasks, such  as generating a range of numbers,  

by writing just a few lines of code. For  example, you can create a data frame with  

one column containing a thousand rows with values  from 0 to 999. By writing this one line of code,  

you create a data frame. A data frame is simply  the representation of data in rows and columns,  

similar to MS Excel. The concept of a data frame  is not new to Spark. We also have the data frame  

concept available in Python and R. In Python, the  data frame is stored on a single computer, whereas  

in Spark, the data frame is distributed across  multiple computers. To ensure that all of this  

data is executed in parallel, you need to divide  your data into multiple chunks. This is called  

partitioning. You can have a single partition  or multiple partitions, which you can specify  

while writing the code. All of these things are  done using transformations. Transformations are  

basically the instructions that tell Apache Spark  how to modify the data and get the desired result.

For example, let's say you want to find  all the even numbers in a data frame. You  

can use the filter transformation function to  specify this condition. But here's the thing,  

if we run this code, we will not get the  desired output. In most programming languages,  

once you run the code, you get the output  immediately. But Spark doesn't work like  

that. Spark uses lazy evaluation. It waits  until you complete writing your entire code,  

and then it generates the proper plan  based on the code you have written. This  

allows Spark to calculate your entire  data flow and execute it efficiently.

To actually execute the transformation  block, we have something called actions.  

There are multiple actions available in Apache  Spark. One of the actions is the count action,  

which gives us the total number of records  in a data frame. We can run an action,  

and Spark will run the entire transformation  block and give us the final output.

Here's an example to understand all of these  concepts in a single project. The first thing  

we need to do is import the Spark session. You can  do that using the following code: from pyspark.sql  

import SparkSession. This creates the entry point  for the Spark application. Once you do that,  

you can use the sparkSession.builder.create  function. This creates the Spark application  

so that you can import the dataset  and start writing the query. You  

have all the details available, such  as versions, app name, and everything.

Now, let's see if we have this dataset called  "tips". If you want to read this data, you can use  

a simple function called spark.read.csv. If you  provide the path and set the header to 2, it  

will print the entire data from the CSV file. As  you can see, our data contains total bill, tips,  

sex, smoker, date, time, and size. All of this  data is being imported from the CSV file. If you  

print the type of this particular file, you will  understand that it is a pyspark.sql.dataframe.

Now, you can create a temporary view on top  of this data frame. If you use the function  

createOrReplaceTempView, it will create a table  inside Spark, and you can write SQL queries on top  

of it. For example, you can run the query SELECT  * FROM tips, and if you provide this query to  

spark.sql, you can easily run this particular SQL  query on top of our data frame. So, what we really  

did was import the data, convert the data into a  table, and then write SQL queries on top of it.

The same thing can be done to convert this Spark  data frame into a Pandas data frame. So, if you  

want to apply any Pandas function, you can also do  that inside Spark itself. Over here, if you want  

to understand lazy evaluation, where you are just  filtering the sex by female and the day as Sunday,  

once we run this particular statement, Spark does  not execute this entire thing. It waits for the  

action to be performed. The action over here  is the show action. So, once you run the show,  

then it will run this entire thing, and  then you will be able to see the results.

This is called a transformation  that we understood in the video,  

and this is the action that you were talking  about. Like this, you can do a lot of things.  

You can go to the Spark documentation and  understand it in detail. There are multiple  

functions available, and for each function,  you will get a detailed understanding.

I hope you understood everything about  Apache Spark and how it executes all of  

this code. If you want to do an entire data  engineering project involving Apache Spark,  

you can watch the video mentioned in the  transcript. It will give you a complete  

understanding of how a data engineering  project is built from start to end.

That's all from this video.  If you have any questions,  

let me know in the comments, and I'll  see you in the next video. Thank you.