Part 1- End to End Azure Data Engineering Project | Project Overview

Summary

This video tutorial offers a comprehensive guide to a real-time data engineering project using Azure technologies. It covers the end-to-end process, from data ingestion with Azure Data Factory to transformation with Azure Databricks, and storage in Azure Data Lake. The project demonstrates the Lake House architecture, including bronze, silver, and gold data layers, culminating in analysis with Azure Synapse Analytics and reporting with Power BI. It also addresses security and governance with Azure Active Directory and Azure Key Vault, providing a complete understanding of building and automating a data platform solution.

Takeaways

• 📊 This video covers a complete end-to-end data engineering project using Azure technologies.
• 🔧 The project demonstrates how to use various Azure resources like Azure Data Factory, Azure Synapse Analytics, Azure Databricks, Azure Data Lake, Azure Active Directory, Azure Key Vault, and Power BI.
• 💻 The use case involves migrating data from an on-premise SQL Server database to the cloud using Azure services.
• 🏞️ The project implements the lakehouse architecture, which includes organizing data into bronze, silver, and gold layers in Azure Data Lake.
• 🚀 Azure Data Factory is used to connect to the on-premise SQL Server and copy data into Azure Data Lake Gen2.
• 🔄 Azure Databricks is utilized for data transformation tasks, converting raw data into curated formats stored in different layers.
• 📚 Azure Synapse Analytics is employed to replicate the database and tables from the on-premise SQL Server and load the curated data for further analysis.
• 📈 Power BI is used for creating reports and visualizations from the data stored in Azure Synapse Analytics.
• 🔒 Security and governance are managed using Azure Active Directory and Azure Key Vault for identity management and storing sensitive information.
• 🧩 The project is structured into multiple parts: environment setup, data ingestion, data transformation, data loading, data reporting, and end-to-end pipeline testing.

Q & A

What is the main focus of the video?

The main focus of the video is to demonstrate a complete end-to-end data engineering project using Azure technologies.

What is the purpose of using Azure Data Factory in this project?

Azure Data Factory is used for data ingestion, connecting to the on-premise SQL Server database, copying tables, and moving the data to the cloud.

What is the role of Azure Data Lake in the project?

Azure Data Lake is used as a storage solution to store the data copied from the on-premise SQL Server database by Azure Data Factory.

How does Azure Databricks contribute to the project?

Azure Databricks is used for data transformation, allowing data engineers to write code in SQL, PySpark, or Python to transform the raw data into a more curated form.

What is the concept of lake house architecture mentioned in the script?

Lake house architecture refers to the organization of data in layers within the data lake, such as bronze, silver, and gold layers, each representing different levels of data transformation.

What transformations occur in the bronze layer?

The bronze layer in the data lake holds an exact copy of the data from the data source without any changes in format or data types, serving as the source of truth.

What is the purpose of the silver and gold layers in the data lake?

The silver layer is for the first level of data transformation, such as changing column names or data types, while the gold layer is for the final, most curated form of data after all transformations are completed.

How does Azure Synapse Analytics relate to the on-premise SQL Server database?

Azure Synapse Analytics serves a similar purpose to the on-premise SQL Server database, allowing the creation of databases and tables to store and manage the transformed data.

What is the role of Power BI in the project?

Power BI is used for data reporting, allowing data analysts to create various types of reports and visualizations based on the data loaded into Azure Synapse Analytics.

What security and governance tools are mentioned in the script?

Azure Active Directory for identity and access management, and Azure Key Vault for securely storing and retrieving secrets like usernames and passwords are mentioned as security and governance tools.

What is the main task of data engineers in automating the data platform solution?

The main task of data engineers is to automate the entire data platform solution through pipelines, ensuring that any new data added to the source is automatically processed and reflected in the end reports.

Outlines

00:00

🚀 Introduction to End-to-End Data Engineering with Azure

The video script introduces an end-to-end data engineering project using Azure technologies. It guarantees to provide a clear understanding of using various Azure resources for data engineering. The project will demonstrate a common use case in companies, which involves migrating an on-premise SQL Server database to the cloud. The presenter will cover the use of Azure Data Factory, Azure Synapse Analytics, Azure Data Bricks, Azure Data Lake, Azure Active Directory, Azure Key Vault, and Power BI. The project will be beneficial for viewers to include in their resumes and for clearing data engineering interviews.

05:01

🔧 Data Ingestion and Transformation with Azure Tools

This paragraph delves into the specifics of data ingestion and transformation using Azure tools. It starts with the use of Azure Data Factory for extracting data from an on-premise SQL Server database and storing it in Azure Data Lake Gen 2. The script then explains the use of Azure Data Bricks for data transformation, adhering to the lake house architecture, which involves organizing data into bronze, silver, and gold layers. The bronze layer acts as the source of truth, while the silver and gold layers represent different levels of data transformation. The paragraph also touches on the use of Azure Synapse Analytics for creating a cloud-based database similar to the on-premise model and Power BI for data reporting.

10:03

🛠️ Setting Up the Data Engineering Environment with Azure

The final paragraph outlines the agenda for the data engineering project, which includes environment setup, data ingestion, transformation, loading, reporting, and end-to-end pipeline testing. It emphasizes the importance of automating the data platform solution and mentions the use of Azure Active Directory for security and Azure Key Vault for storing secrets. The paragraph concludes by stating that the project will begin with setting up the environment in the Azure portal, indicating the start of the practical implementation of the discussed concepts.

Keywords

💡Data Engineering

Data Engineering refers to the practice of designing, building, and maintaining the systems and processes that enable the collection, storage, and transformation of raw data into more usable forms for analytics. In the video, the theme revolves around a complete end-to-end data engineering project, demonstrating how to use various Azure tools to build a data engineering solution.

💡Azure

Azure is a cloud computing service created by Microsoft, offering a range of cloud services, including analytics, computing, database, and networking. The video focuses on using Azure technologies to complete a data engineering project, showcasing how different Azure resources are utilized in the process.

💡Azure Data Factory

Azure Data Factory is a cloud-based data integration service that allows you to create, schedule, and manage data workflows. In the script, it is mentioned as an ETL (Extract, Transform, Load) tool used for data ingestion, connecting to an on-premise SQL Server database and moving data to the cloud.

💡Azure Synapse Analytics

Azure Synapse Analytics is an analytics service that brings together enterprise data warehousing and big data analytics. It is used in the video to create a database similar to an on-premise SQL Server and to load the transformed data into it, providing a familiar data warehousing model in the cloud.

💡Azure Databricks

Azure Databricks is a big data analytics platform optimized for Azure. It's used in the video for high-end data analytics workloads, allowing data engineers to write code for data transformation using SQL, PySpark, or Python within the platform.

💡Azure Data Lake

Azure Data Lake is a hyperscale, secure data lake service that allows you to store vast amounts of unstructured and structured data at a low cost. The script describes using Azure Data Lake Gen 2 to store the raw data copied from the on-premise SQL Server database before transformation.

💡Lake House Architecture

The Lake House Architecture is a concept that combines the best of data lakes and data warehouses, organizing data into layers for different transformation stages. In the video, the architecture is explained with bronze, silver, and gold layers, where data is progressively transformed and cleaned before being used for analytics.

💡Power BI

Power BI is a business analytics service by Microsoft that provides interactive visualizations and business intelligence capabilities. In the context of the video, Power BI is used to create reports and visualizations from the data loaded into Azure Synapse Analytics, allowing for data analysis and reporting.

💡ETL

ETL stands for Extract, Transform, Load, which is a process in database usage that involves pulling data from different sources, converting it into a suitable format, and loading it into a database. The video script discusses the use of Azure Data Factory as an ETL tool for data ingestion and transformation.

💡Security and Governance

Security and governance in the context of the video refer to the practices and tools used to ensure data protection, access control, and compliance with regulations. Azure Active Directory and Azure Key Vault are mentioned as tools for identity access management and secure storage of secrets, respectively.

💡Automation

Automation in the video refers to the process of setting up the data engineering solution in such a way that it can run without manual intervention. This includes the use of pipelines to automatically handle new data from the source, transform it, and update the reports in Power BI.

Highlights

Introduction to a complete end-to-end data engineering project using Azure technologies.

Guarantee of a clear understanding of using Azure resources for data engineering after the video.

Use case of migrating an on-premise SQL Server database to the cloud, a common scenario for companies.

Overview of tools used: Azure Data Factory, Synapse Analytics, Data Bricks, Data Lake, Azure Active Directory, Key Vault, and Power BI.

Explanation of Azure Data Factory as an ETL tool for data ingestion from on-premise databases.

Utilization of Azure Data Lake Gen 2 for storing data in the cloud at a low cost.

Role of Azure Data Bricks in transforming raw data into curated data for analytics.

Introduction to the Lake House architecture with bronze, silver, and gold data layers.

Details on the bronze layer as the source of truth with an exact copy of the data source.

Transformation process from bronze to silver layer for basic changes like column names and data types.

Final transformation to the gold layer for the cleanest form of data using Azure Data Bricks.

Use of Azure Synapse Analytics to create databases and tables similar to on-premise SQL Server.

Loading transformed data into Azure Synapse Analytics for analysis and reporting.

Application of Power BI for creating reports and visualizations from the data in Synapse Analytics.

Importance of automating the data platform solution through pipelines for real-time data updates.

Security and governance aspects covered with Azure Active Directory and Azure Key Vault.

Agenda for the project video, split into multiple sections for comprehensive learning.

Starting with environment setup in Azure Portal for the data engineering project.

Transcripts

hello everyone welcome back to my

channel in today's video we are going to

see about a complete entry and real-time

data engineering project so this data

installing project is completely done

using issue Technologies so I can pretty

much guarantee that after watching this

complete end-to-end data project video

you'll be having a clear understanding

about how to use different Azure

resources to build your data engineering

project so this engineer is going to be

a complete demo of different resources

so the use case that is going to be

covered in this video is one of the very

popular use case and most of the

companies uses this use case to build

the data engine steering project I'm

pretty much sure that it is going to be

really useful and you can also include

this project in your resume and it will

be really helpful for you guys to clear

any kind of issue data engineering

interviews so without wasting further

time let's get started so I would like

to start this by introducing the

different tools that is going to be used

in this project so let's see what are

those so we'll be using issue data

Factory

issue synapse Analytics

issue data bricks

issue data Lake

Azure active directory

Azure key Vault and finally power bi so

as you can see here there are a lot of

different resources so these are the

most commonly used Resources by any data

Engineers to build any kind of data

engineering projects cool so now let's

see how we can use these tools to build

this data in steering project with an

example of an architecture of the

project let's check it out

so as I mentioned before the use case

that we are going to do in this project

is pretty common use case so what I mean

by this common use cases if I introduce

the data source then you'll be getting a

clear idea about what I'm talking about

so the data source that we are going to

use in this particular use cases

on-premise SQL Server database so we all

know that one of the main reasons for

different companies to move to cloud is

to migrate their on-premise traditional

database to Cloud so this is one of the

most common use case right so I thought

like let's take the same use case for

this project so it will be really useful

for you to actually understand this

whole process so inside the database

we're having like six or seven tables so

we are going to migrate this database

completely to Cloud so as part of this

the first step would be using a tool

called assured refractory so Azure data

Factory is a ETL tool I have created a

separate playlist for sure data Factory

where I have covered all the basic

concepts uh like what is social data

Factory in general or what are the

different things that we could do with

it so if you really wanted to get an

understanding about that you can check

that out but if you ask me that if it is

really mandatory to watch those videos

before actually understanding this

project I would say no because all the

concepts will be covered in this video

as well so as I mentioned before

actually data Factory is an ETL tool

which is mainly used for data ingestion

so what we are going to do is the issue

data Factory will be used to connect

with this on-premise SQL Server database

to copy all the tables from the database

and move all the tables to the cloud and

now where the tables will be stored in

the cloud so for that we are using Azure

data Lake gen 2.

Gen 2 is the storage solution in Azure

and the data which is stored in Azure

analyte is pretty cheap so we'll be

using Azure data Factory to connect to

the on-premise SQL database and put all

the data into the Azure data like gento

so once a data has been added into the

Azure data Lake we'll be using a tool

called Ashu databricks to transform all

the draw data into the most curated data

Azure databricks is a big data analytics

tool which is mainly used for high-end

data analytics workloads so in simpler

words we can say that using Azure data

breaks we can actually write the code

either it is SQL or Pi spark or python

all the action development work will be

done inside Azure databricks so which is

mainly used for actually transforming

the data and the next thing is one of

the most important topics that I have

covered in this project is using issue

databricks and issue data like there is

a concept called lake house architecture

so what is mean by Lakers architecture

is say for example Azure data Lake will

be divided into multiple layers so what

I mean by layers is we have different

layers such as bronze layer

silver layer

and gold layer what are the difference

between these lasers for example let me

start with the bronze layer so as

discussed before Aishwarya Factory

connects to the on-premise server

database copies the data and put the

data into the issue data lake so inside

issue data Lake the issue data Factory

puts the data first into the bronze

layer which means that the bronze layer

has an exact copy of what the data looks

like in this data source in this project

we are not going to touch any data

inside the bronze layer and we are not

going to change the format or anything

inside the bronze layer so this is going

to be the source of Truth what are the

main advantages of this is for example

in the subsequent data Transformations

that we are going to do if something

goes wrong you can come back to this

bronze layer and get all the data to try

out which is going to be the same as the

data source so once the data has been

copied to this bronze layer by Azure

data Factory we then use assure data

breaks to connect to this bronze layer

do some data transformation and load the

transform data into the silver layer so

this silver layer and gold layer is kind

of here different levels of

transformation for example the first

level of transformation is silver layer

and the next layer of transformation is

a goal layer so in bronze to Silver

layer the transformation might be kind

of simple like changing the column names

or changing the data types because lot

of times the on-premise kind of data

types and the cloud data types is not

really compatible so we may need to

change few things based on how it is

going to support it into the cloud so

those kind of minimal transformation can

be done in this silver layer and once

the data has been transformed using

Azure databricks and the finer data has

been loaded into the silver layer we

then do an another set of transformation

using same usual data breaks and this

data is going to be loaded finally to

this this gold layer so this gold layer

is the final cleanest form of data so we

all need to have a clean data right so

one of the main tasks of data Engineers

is to clean the raw data into the most

curated data which means that all these

data transformation is done by the data

Engineers using assured outbreaks in

different zones so that's called lake

house architecture so once the data has

been loaded to the goal layer now we are

using another tool called assure synapse

analytics so you can think like Azure

synapse analytics is kind of similar to

what is on-premise SQL Server database

so we can also create the same kind of

database similar to how we can create

using the on-premise SQL database we can

also create all these database and

tables using a synapse analytics and

once these database and tables have been

set up in Azure synapse analytics all

the data that is present in the goal

layer will be loaded to all the tables

that we have created in Asus synapse

analytics so now at the end of this step

the associate snaps analytics will have

a kind of a similar data virusing model

of how it looks like in on-premise SQL

database so you can consider now the

data has been completely migrated so we

are not going to stop here so we are

going to do further analysis on the data

that has been stored inside the SEO

synapse analytics using a tool called

Power bi so this power bi will get all

the data that has been loaded into the

Asus snaps analytics and we could create

a different kind of reports like uh the

charts bar charts or whatever the

reports that we want in power ba so

basically data analyst is the one who

will be creating reports in power bi and

most of the companies where the data

Engineers will also do it so it's better

to understand the full end-to-end flow

of a data engineering project that's the

reason I have covered power bi as part

of this data engine steering project so

apart from this we are also using the

security and governance tools and some

of the tools like we are using Azure

active directory which is an identity

access management tool so all the

security related things and the further

things like creating the service

principle and all of the steps can be

done in the selective directory and all

the other details will be covered in the

subsequent sections and we'll be also

using the Azure key wall to store all

the secrets for example the username and

password can be stored in Azure key

Vault and it can be used to safely

retrieve this information which is kind

of the safest way how the data Engineers

do in real time project so I also wanted

to include this so it will give you an

complete understanding of the overall

data engineering project

cool so now I think you have an

understanding about the use case that we

are going to do in this data engineering

project so this complete entry and data

engineering project will start with data

ingestion and once the data has been

ingested and stored into the Azure data

like then we'll be using Azure

databricks to transform all the data to

the most cleanest form and once the data

has been transformed and loaded into the

gold layer in Australia like we then use

the Asus snaps analytics to create the

database and tables and load the final

gold layer data into these tables and

once the data has been loaded into the

Asus snaps analytics will be then using

power bi to create reports and one of

the main tasks of data Engineers is to

automate all the interior data platform

solution which is pretty much done via

pipelines so what I mean by this is for

example once we have configured this

end-to-end data engineering solution and

say for example if a new row has been

added to any of the tables in the

on-premise SQL database and once we have

run this pipeline Line This pipeline

should get this latest Row from the

on-premise SQL database do all the data

transformation and load that data into

the database and finally this power BH

should reflect the new row that has been

added into the source cool I think you

have pretty much understood the use case

of this project and it will be really

helpful for you guys to understand how

all these data sources work together how

these data sources can be brought

together to build this data engineering

project since there are a lot of

different things to be covered in this

video I have splitted this video into

multiple section so the agenda for this

project would be I'll be starting with

the part one which is the environment

setup and once that has been done we'll

be moving to the part 2 which is the

data ingestion that we have discussed

now and the part three would be data

transformation using data breaks and

part 4 will be data loading and part 5

will be data reporting using power ba

and once that has been done the final

part six will be the end to end pi

testing cool this is the agenda for this

project and we'll be starting with the

part one which is environment setup for

now and for part one environment setup

let's go to the issue portal and see

what are the different resources that we

need to create and what are the other

environment setup that we have to do

let's check it out