Learn Apache Spark In-Depth with Databricks for Data Engineering

Summary

This comprehensive course on Apache Spark offers learners a deep dive into the framework with two major projects on AWS and Azure. It covers Spark's internal mechanisms, structured and lower-level APIs, and production deployment. The course includes detailed notes, a data engineering community for support, and a combo package with Python, SQL, and Snowflake basics. A certificate is provided upon completion, and a limited-time 50% discount is available for new enrolments.

Takeaways

• 📚 The course offers comprehensive learning on Apache Spark with two end-to-end projects on AWS and Azure.
• 🔍 It covers the internal workings of Apache Spark, including its architecture, APIs, and production deployment.
• 💻 Learners will gain practical experience by writing transformation code and working with different data types and file formats.
• 📈 The course includes detailed notes for easy reference and revision, especially useful for interview preparation.
• 🤝 Access to a private data engineering community is provided for shared learning and project collaboration.
• 🎓 Prerequisites for the course include a basic understanding of Python, SQL, and a data warehouse tool like Snowflake.
• 🏆 Successful completion of the course materials leads to a certificate.
• 🎥 The course is structured into multiple modules, starting from an introduction to Apache Spark to in-depth projects.
• 🚀 The course is designed to boost confidence in writing Spark code and understanding its execution.
• 🌐 Special focus is given to Databricks and its architecture, including the lakehouse approach and Delta Lake.
• 🛒 A limited-time 50% discount is offered for both the combo package and the Apache Spark course for new learners.

Q & A

What are the key benefits of learning Apache Spark for a data engineer?

Apache Spark is a crucial skill for data engineers as it is used by top companies for large-scale data processing. It allows for the writing of transformation code and is central to many data engineering projects.

What types of projects are included in the course?

The course includes three mini-projects and two end-to-end data engineering projects, specifically designed to enhance practical understanding and provide portfolio-worthy experiences.

What is the significance of the structured API in Apache Spark?

The structured API is significant as it forms 80 to 90% of the work in organizations, making it one of the most important sections to understand for effective Apache Spark usage.

How does the course address the learning of the lower-level API in Apache Spark?

The course dedicates a module to understanding the lower-level API, such as Resilient Distributed Datasets (RDD), including both theoretical knowledge and hands-on practice for a comprehensive understanding.

What are the production-ready aspects of Apache Spark applications covered in the course?

The course covers how to write, deploy, and debug Apache Spark applications, including understanding Spark's life cycle, deployment processes, monitoring through Spark UI, and troubleshooting common errors.

What is Databricks, and how does it relate to Apache Spark?

Databricks is a tool for Apache Spark that simplifies the process of working with Spark. The course covers Databricks architecture, lakehouse architecture, and the use of Delta Lake and Medallion architecture for effective data engineering.

What are the two end-to-end projects included in the course, and on which platforms are they based?

The two end-to-end projects are based on AWS and Azure. The AWS project involves a Spotify data pipeline, while the Azure project focuses on e-commerce data processing using Azure Data Lake Storage and Databricks.

What are the prerequisites for taking this Apache Spark course?

A basic understanding of Python, SQL, and a data warehouse tool like Snowflake is recommended before taking the course to ensure a solid foundation for learning Apache Spark.

What bonuses come with the course?

The course includes detailed notes for easy reference, access to a private data engineering community for support and collaboration, and a significant discount on future courses.

How does the course ensure a comprehensive learning experience?

The course combines theoretical knowledge with hands-on practice, including mini-projects and end-to-end projects, to ensure a thorough understanding of Apache Spark and its applications.

What is the format for accessing the course content after purchase?

After purchasing the course, learners get lifetime access to all course materials, which can be accessed through the website and a dedicated mobile application for on-the-go learning.

Outlines

00:00

📚 Introduction to Apache Spark Course

This paragraph introduces a comprehensive course on Apache Spark, emphasizing its importance in data engineering and mentioning top companies that utilize it. The course offers two end-to-end projects on AWS and Azure, covering internal workings of Apache Spark, structured API, and lower-level API. It also includes guidance on deploying code in production, monitoring UI, and handling common errors. The speaker shares their experience learning Apache Spark and introduces an in-depth course structured with mini-projects and a focus on Databricks.

05:02

🚀 In-Depth Course Content and Projects

The second paragraph delves into the specifics of the course content, highlighting the modules and projects included. It discusses the structured API's significance in organizations and introduces the concept of Spark SQL. The paragraph outlines mini-projects for practical learning and touches on the lower-level API's power. It also covers the importance of Databricks and its architecture, setting up environments, and the lakehouse architecture. The speaker describes two end-to-end projects on AWS and Azure, emphasizing the transformation from basic to high-quality projects and the comprehensive understanding of data engineering with Apache Spark on these platforms.

10:07

🎓 Prerequisites, Access, and Bonuses

The final paragraph addresses the prerequisites for the course, recommending a basic understanding of Python, SQL, and Snowflake. It reassures learners that the course starts from scratch and provides lifetime access to course materials. The speaker mentions frequently asked questions about course access and offers a limited-time 50% discount for both the combo package and the Apache Spark course. The paragraph concludes by encouraging viewers to subscribe to the channel and take the course, emphasizing the effort put into creating the course and its potential to help kickstart careers in data engineering.

Keywords

💡Apache Spark

Apache Spark is an open-source distributed general-purpose cluster-computing framework. It is widely used for large-scale data processing and analytics. In the video, it is emphasized as a crucial skill for data engineers, with companies like Google, Facebook, and Microsoft utilizing it for their data processing needs. The course offers in-depth knowledge of Apache Spark's architecture, API, and practical applications.

💡Data Engineers

Data Engineers are professionals who specialize in the infrastructure that supports the storage, management, and analysis of data. They work with systems like Apache Spark to process and transform data. The video positions Apache Spark as a central tool for data engineers, suggesting that proficiency in it is essential for career advancement in this field.

💡Data Breaks

Data breaks refer to issues or interruptions in the flow of data that can occur during processing or analysis. The video suggests that the course will teach how to handle such situations, which is an important aspect of working with large datasets and complex data pipelines.

💡Structured API

The Structured API in Apache Spark is a high-level interface for programming with structured data in Spark. It simplifies data processing tasks and is used extensively in organizations for data manipulation. The video highlights that understanding the Structured API is crucial for working with Apache Spark, as it forms the basis for most data engineering tasks.

💡Databricks

Databricks is a unified analytics platform that simplifies the use of Apache Spark for data engineering and data science tasks. It provides a collaborative environment for data engineers, data scientists, and business analysts. The video discusses the inclusion of Databricks in the course, emphasizing its growing importance in the industry and its role in the lakehouse architecture.

💡AWS

AWS, or Amazon Web Services, is a comprehensive cloud computing platform that offers a wide range of services. In the context of the video, AWS is mentioned as a platform where one of the end-to-end projects will be deployed, showcasing the practical application of Apache Spark in cloud environments.

💡Azure

Azure is a cloud computing service created by Microsoft for building, testing, deploying, and managing applications and services through Microsoft-managed data centers. The video mentions Azure as another platform for deploying the end-to-end project, highlighting its role in modern data engineering practices.

💡Spark SQL

Spark SQL is the module in Apache Spark used for handling structured data, providing a programming interface for SQL and DataFrame operations. It is a key component of the course, as it is essential for performing SQL queries and data manipulation tasks within the Spark ecosystem.

💡RDD

RDD stands for Resilient Distributed Dataset, which is the fundamental data structure in Apache Spark for fault-tolerant data processing. The video emphasizes understanding the lower-level API, such as RDDs, as a core aspect of mastering Apache Spark.

💡Lakehouse Architecture

The lakehouse architecture is a modern data management approach that combines the best of data lakes and data warehouses. It is designed to handle both structured and unstructured data, providing a scalable and flexible solution for data storage and analytics. The video positions Databricks as a key player in the adoption of this architecture within the industry.

💡End-to-End Projects

End-to-End Projects are comprehensive assignments that allow participants to apply the knowledge and skills they have learned throughout a course, from start to finish. In the context of the video, these projects are designed to provide hands-on experience with real-world scenarios on cloud platforms like AWS and Azure, thereby bridging the gap between theoretical knowledge and practical application.

Highlights

One of the best courses on Apache Spark, offering comprehensive learning and hands-on experience.

Includes two end-to-end projects on AWS and Azure, providing practical exposure to real-world data engineering scenarios.

Delves into the internal workings of Apache Spark, a crucial skill for data engineers working at top companies like Google, Facebook, and Microsoft.

Course creator shares personal experience of learning Apache Spark the hard way, emphasizing the value of this in-depth course.

Offers detailed notes for students, allowing for easy reference and revision of concepts learned throughout the course.

Provides a solid foundation in Apache Spark, covering everything from basics to advanced topics like structured API and lower-level API.

Teaches how to write production-ready Apache Spark applications, including deployment, debugging, and monitoring.

Includes a mini-project where students write basic Spark functions, building confidence in coding and adding to their portfolio.

Focuses on Spark SQL, an important and in-depth module where students learn through hands-on practice.

Explains the powerful lower-level API such as Resilient Distributed Datasets (RDD), a key reason behind Apache Spark's popularity.

Introduces Databricks, a leading tool for Apache Spark, and its lakehouse architecture, aligning with industry trends.

Presents a Spotify data pipeline project on AWS, demonstrating how to scale a smaller project into a high-quality, production-ready solution.

Includes an Azure Data Engineering project, exploring the different aspects of Apache Spark on Databricks and its integration with Azure services.

Course is designed for learners with a basic understanding of Python, SQL, and Snowflake, and offers a combo package discount for related courses.

Lifetime access to course material and resources, ensuring continuous learning and skill development.

Course completion comes with a certificate, validating the acquired skills and knowledge in Apache Spark and data engineering.

A limited-time 50% discount is available for both the combo package and the Apache Spark course, encouraging prompt enrollment.

Transcripts

One of the best courses on Apache Spark.  You will get two end-to-end projects  

on AWS and Azure. You will learn about  the internal workings of Apache Spark,  

write a lot of code, and get detailed  notes. Learn data breaks and many  

more things. Watch this video till  the end to understand everything.

Apache Spark is one of the most important skills  you can have as a data engineer. Top companies  

like Google, Facebook, and Microsoft use Apache  Spark to process their data on a large scale. In  

my career, I worked on so many different data  engineering projects, and Apache Spark was the  

center of it. We used Apache Spark to process  all of our data and write transformation code.  

I learned Apache Spark the hard way. I referred to  multiple books, watched so many different videos,  

blogs, and multiple courses just to understand  different parts of Apache Spark. There are so  

many things you need to understand, from  the internal workings, structured API,  

lower-level API, how to deploy the code in  production, how to monitor the UI, how to deal  

with common errors, and there are so many more  things that are associated with Apache Spark.

A few months back, I made this video on Spark,  "Learn Apache Spark in 10 Minutes." It has around  

200,000 views, and so many people love this video.  The reason I simplified everything is that all of  

you guys requested it. I built the in-depth  course on Apache Spark. So, I'm presenting  

Apache Spark for Data Engineers with Databricks.  Now, I request you to watch this video from start  

to end so that you understand everything about  this course. Even if you have a few questions,  

I've already answered them in this video. So  make sure you watch the video from start to end,  

and if you still have questions or doubts, you  can always ask them in the comment section.

This video is divided into the following sections.  Let's start this video by understanding the course  

structures and what you will get. I have divided  this course syllabus into multiple modules. You  

will get three mini-projects and two end-to-data  engineering projects. We will talk in detail  

about all of these projects in this video. The  first two modules of this course are just the  

basic introduction on how to access the course  resources, how to interact with the community,  

and the right mindset you should have by  learning Apache Spark. So, we will cover  

all of these in the first two modules. From  the third module, we will start deep diving  

into Apache Spark. We start with the basics  of Apache Spark, why do we need Apache Spark,  

understanding the key concepts such as lazy  evaluation, transformation, action. All of  

these things are important. And then we will  have an in-depth understanding of Apache Spark  

architecture. This module is completely  theoretical, and we will understand a lot  

of things about this. We are just trying to  build the foundation here. Once you do this,  

then you can start installing Apache Spark. So,  I have given a few guides where you can install  

Apache Spark and set up your environment.  Once you set up your Spark environment,  

then we will directly do one mini-project where  you will write different Spark functions to  

understand how to write basic Spark code. This  project will give you the confidence that you  

can write Spark code by yourself, and you will  have one mini-project added to your portfolio.

Once that is done, then we will start  understanding the different parts of  

Apache Spark. We will start by understanding the  structured API. 80 to 90% of your work is around  

the structured API in the organization, so this  section is very important, and this is one of the  

easiest sections to understand. In this module,  we will understand the basic structure, operation,  

working with different data types, understanding  the user-defined functions, different joins,  

understanding the internal working of the  joins, working with different file formats,  

how to partition and bucket your data, and  finally, we will understand about Spark SQL,  

one of the most important and in-depth modules of  the course. You will learn a lot of things here,  

so I urge you to do a lot of hands-on practice  while you're going through this module.

After this, we will do another mini-project where  we will apply everything that we have learned  

in our module Structured API. This is a common  project that we have done in all of our previous  

courses, Python, SQL, and Data Warehouse  course. So if you have taken that course,  

then you will know we do this project called  as the iPhone data analysis. But this time,  

we will do it using Apache Spark. This  project will again give you the confidence  

boost that you can write this Spark code  by yourself. We will do a lot of theories,  

and we will do a lot of hands-on practice  in this course. So, ready for that.

Once this is done, then we will deep dive into  the lower-level API. This is the bread and butter  

of Apache Spark, and this is the reason why Apache  Spark is so powerful and got so popular is because  

of the lower-level API such as RDD. So we will  start this module by understanding the basics  

of lower-level API, we will understand  Resilient Distributed Datasets (RDD),  

we will do hands-on practice and understand  the theoretical side of it, and then we  

will understand the distributed variables  like broadcast variables and accumulators.

Once you complete these sections, then you  will already know a lot of things about  

Apache Spark. But we don't stop here. Now we  will understand how to write production-ready  

Apache Spark applications, how do you deploy and  debug your code, we will understand everything  

about how Spark runs on large clusters,  the life cycle of Apache Spark application,  

how deployment happens, how to monitor Spark UI,  and how to debug common errors and solve them.

Once you finish all of these modules, then  you will have the solid foundations of  

Apache Spark. You'll understand the internal  workings, how codes are getting executed,  

and write the code by yourself. But  we don't stop here. You will learn  

one of the most important tools that are  available in the market for Apache Spark,  

that is called as Databricks. So we will  start understanding the different parts of  

the Databricks, we will understand what is  Databricks architecture of the Databricks,  

we will understand the lakehouse architecture,  this is where the industry is moving, so we need  

to understand what is happening there. How to set  up Databricks environment, workspace, clusters,  

Databricks file system, Delta Lake, Medallion  architecture. Understanding the inner workings of  

P-Fill, so many different videos on this, you will  become the master of Databricks and Apache Spark.

Then comes the best part of this course,  end-to-end projects on AWS and Azure. I have added  

two projects in this course, one on AWS, that  is the same project that we have used till now,  

Spotify data pipeline. The idea here is that  I want to show you how you can start with  

a smaller project and take that project and  make it one of the high-quality projects. So,  

in our Python for Data Engineering course, if  you have taken that course, you will understand  

that we built this pipeline using simple Python  language where we use AWS Lambda function and we  

use AWS Glue and Athena to write the queries  in our data warehouse Snowflake course. We  

replaced the load bar with the Snowpipe and the  Snowflake database. And in the Apache Spark code,  

we will replace the Lambda function where  we wrote our transformation logic using the  

simple Python script to the Apache Spark  AWS Glue environment. You will understand  

how to write Spark code on AWS Glue. We will  write everything from scratch so you will have  

understanding about everything, and then  we will automate this entire pipeline. You  

will have the complete understanding from  fetching data to getting directly uploaded  

onto the Snowflake database with all of these  transformations in between. This project is  

one of the most high-valued projects in the  market. You will learn a lot of things here.

But we don't stop here. We have one more project  available on Azure Data Engineering. Okay,  

you will learn AWS, and you will also  learn Azure in this project. We have  

taken a different approach just to explore  the different side of Apache Spark that is  

on the Databricks. The architecture of  this project is something like this,  

we will fetch the e-commerce data from this  website, then we will load that data onto Azure  

Data Lake Storage. Once we have our data available  in the CSV file format, we will trigger the Data  

Factory that is the ETL service provided by  the Azure and then we will convert our data  

back to the P-Fill format. Then once we have  our data converted into the proper file format,  

then we will write our Apache Spark code and  build the Medallion architecture, bronze, silver,  

and the gold layer. Well, we will write so many  transformation codes, we will mount the ADLS to  

our Databricks environment, we'll understand  the different parts of writing code, how do  

we optimize, and then if you want to analyze  the data, you can use the Synapse Analytics,  

you can also use the Databricks warehouse,  and again, if you want to visualize your data,  

you can use visualization tools like PowerBI,  Tableau to build your final visualization. I  

have provided some of the challenges at the end  of this project so that I don't spoon feed you  

everything. You will get to do a lot of things by  yourself so that it boosts your confidence that  

you can complete the challenge of that project  by yourself. This is really important, okay?  

I don't want to show you everything from start  to end. I will show you the 70% of the things,  

but 30% you have to complete it so that you  understand how to execute the project by yourself.

It took me around 5 months to build this entire  

course. I had to refer to so many different  resources and put everything in one place.

Now let's talk about the bonuses you  will get in this course. The best bonus  

that you will get in this course is the  notes that I have created for you. You  

will get everything at one place. These  are the detailed notes with the theory,  

architecture at one place properly organized  for you to refer at any time. This is very  

important because once you complete watching the  videos and in future while you are preparing for  

the interview or you just want to revise the  concept instead of going through the videos  

again you can just refer the notes. These notes  are quite handy so even if you are traveling or  

if you want to access this note anytime you  can just go to the URL and you will be able  

to get that. The best part about these notes  is that you will get the connections of the  

similar topic so if you want to jump from topics  to topic or understand how one topic is connected  

with another topic you can do that easily.  I have built all of these notes by myself.

Second bonus you will get is the access to  the data engineering community. This is the  

private Community we have where we share our  learnings we ask questions we help each other.  

So once you make the purchase of this course  you will get the Discord link where you can  

join the data with the community learn with the  other people and create the projects together.

Third bonus is the huge discount that you  will get in the future courses so all of my  

existing students already got like 50% of the  discount on this Apache Spark course so if you  

are a part of the existing course you will  automatically get the 50% discount on this.

Now I want to talk about the prerequisite  required for this course. I'm building my  

courses step by step in proper sequence manner  so first we build the python then we did the  

SQL and then we did the data warehouse with the  snowflake all of these courses are in the sequence  

order because it is important that you learn  all of these things one by one okay so if you  

are planning to take this Apache Spark course  then I recommend at least you have the basic  

understanding of the Python SQL and the snowflake  if you don't have then I highly recommend you to  

take all of these courses and I will give you  the combo package discount so don't worry about  

it but if you have the basic understanding of  it then you don't have to buy everything you  

can just directly start with the Apache Spark  but you should understand the basics of python  

understand how to write the SQL queries and have  the basic understanding of one data warehouse tool  

snowflake is recommended but if you know any other  tool that is all good other than that you don't  

really need to worry about about anything else  I will teach you everything from the scratch.

Now how to access the course and some of the  frequently asked questions once you purchase  

the course then you will directly get the email  and the WhatsApp notification on how you can  

access the course material you can access the  course on the website you can also use the data  

with the application if you want to watch videos  on your mobile these are some of the frequently  

asked questions and answers to it first of all you  will get the lifetime access to all of the course  

material and the resources so once you purchase  the course you can directly start watching it all  

of the course materials are recorded with the  high quality production I don't sell the zoom  

recording just like other people I sit record  and edit all of my videos just to give you the  

good viewing experience for are these four courses  are enough to become a data engineer the answer is  

yes and answer is also no these skills are the  foundation for becoming a data engineer till  

the Apache Spark so python SQL data warehouse  Apache Spark if you know all of this then you  

already know the 60 70% of the data engineering  because you already did lot of projects on this  

but there are few skills you might have to learn  such as understanding more different services on  

the cloud platforms understanding Apache airflow  Apache Kafka and we will have more courses in the  

future on this so don't worry about it will I  get the certificate at the end of the course  

the answer is yes you will get the certificate  once you complete all of the course material if  

you have more questions then feel free to comment  it I will be happy to answer them so here's the  

thing if you're interested in buying the combo  package or buying the Apache Spark course then  

I'm giving the 50% off for the limited time  period only I can't afford to give the 50%  

off for the longer period of time so if you're  completely new then you can directly buy the  

combo package where you will get the four courses  python SQL data warehouse with the snowflake for  

data engineering and the Apache Spark for data  engineering with the data brakes and if you just  

want to buy the Apache Spark course you can also  get the details in the description you'll find all  

of these information available in the website so  you can just check the link below and make your  

purchase I have worked really hard to build  all of these courses and these courses have  

helped more than 10,000 people to kickstart their  career in data engineering I hope to see you in  

the course thank you for watching this video  I will be publishing a lot of videos on this  

channel so if you're new here then don't forget  to hit the Subscribe button and like this video  

if you found this video insightful thank you  for watching I'll see you in the next video.