Learn Apache Spark in 10 Minutes | Step by Step Guide
Summary
TLDRThis 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
📈 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.
🔥 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.
💻 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.
Mindmap
Keywords
💡Big Data
💡Hadoop
💡Distributed Processing
💡Hadoop Distributed File System (HDFS)
💡MapReduce
💡Apache Spark
💡Resilient Distributed Dataset (RDD)
💡In-Memory Processing
💡Spark SQL
💡Spark Streaming
💡MLlib
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.
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