Azure Data Engineer Roadmap: How to Become Azure Data Engineer in 2024

Roadmap for Azure Data Engineering

1. Become Azure Data Engineer

Think of Azure Data Engineering as the art of creating, handling, and making the most of data solutions using Microsoft’s Azure cloud magic. It is like a toolkit for working with information, from gathering it, storing it, to shaping it into something useful, checking it out, and presenting it in a way that makes sense.

In simpler words, it’s how tech wizards use Azure’s special tools to build super-smooth, super-secure data highways, fancy data storage spaces, and smart systems for understanding information better. Azure Data Engineering refers to the practice of designing, implementing, and managing data solutions using Microsoft’s Azure cloud platform.”

“The demand for tech experts who can craft, construct, and look after the digital ‘data homes’ is booming! If you are curious about becoming an Azure Data Engineer, Then you are on right Blog Post. In this blog post, we will map out the whole journey to becoming a star Azure Data Engineer. However, before proceeding further make sure you know the prerequisites, for that you can visit our Blog post.

2. Azure Services Introduction:

To become an Azure Data Engineer, you will want to become buddies with some important Azure services. Discover how data has transformed and how the sky-high power of cloud tech is opening up thrilling new possibilities for businesses. We will show you the amazing data tools out there and how, as a Data Engineer, you can use these tech wonders to supercharge your organization’s success.

Get the lowdown on Azure cloud services.
Discover why using Azure services is a game-changer for data engineering.
To get the lowdown on these services, dive into Microsoft’s official documentation. It is your key to knowing these tools inside out!

A. Azure Data Factory:

To learn Activities, Datasets, Linked services, Data Flows, Integration Runtimes, how to create data pipelines with Data Factory and Master scheduling and monitoring data pipelines like a pro, begin with Microsoft’s official documentation for comprehensive insights.
Supplement your learning with video tutorials for practical demonstrations.
Get hands-on experience by creating your data pipelines within your Azure Data Factory instance.
Participate in community forums for expert guidance and shared experiences.

B. Azure Synapse Analytics:

To master Azure Synapse Analytics, start by delving into Microsoft’s official documentation.
It is your guide to understanding SQL Pool, Copy Job, Data Flow, Pipeline, SQL Scripts, Spark Notebooks, Quick Reads from Storage, Run ML Projects, Visualization, Access Control, data warehousing.
Then, explore Synapse Studio, a powerful tool for data exploration and analysis.
Combining learning resources, hands-on practice, and community engagement will help you become proficient in this data analytics platform.

C. Azure Databricks:

To learn Azure Databricks, begin by exploring Microsoft’s official documentation to understand the platform’s capabilities.
Then, dive into concepts Workspaces, Notebook, Lakehouse, Dashboard, Library, Repo, Experiment, Databricks File System (DBFS), Database, Table, Delta table, Metastore, Visualization, Computation management, Cluster, Pool, Workflows, Workload, Feature Store and there are many more.
Harness the potential of notebooks, powerful tools for data exploration and analysis, and leverage online tutorials and community support for a well-rounded learning experience.

D. Azure Storage:

Azure Storage, a complete toolbox of storage services in the Microsoft cloud.
Whether you need to store objects, blocks, or files, Azure has got you covered. With Azure Storage, you can easily boost your data’s performance, ensure it is always available, and keep it super secure.
You can manage all your data in one place, making your life as a data pro a whole lot easier!
Hands-on practice, and community engagement will help you become proficient in better way.

E. Azure Key Vault:

Azure Key Vault is your digital vault for keeping secret keys and other vital security codes safe from prying eyes. It is like a fortress for your sensitive data, offering enhanced security and control.
You get the added bonus of FIPS 140-2 Level 2 and Level 3 validated Hardware Security Modules (HSMs) for top-notch encryption. In addition, with Azure’s cloud-scale power and global reach, you will experience faster data access and rock-solid redundancy.
Your applications do not need to get their hands dirty with the keys, and it all becomes simpler and more automated when managing SSL/TLS certificates.
In a nutshell, Azure Key Vault is your security superhero!”

F. Azure Fabric:

Azure Fabric empowers data engineers to craft, build, and upkeep the backbone of systems that enable organizations to collect, stash, crunch, and decipher heaps of data.
In Microsoft Fabric, data engineering means building and managing the systems that help organizations gather, store, process, and analyze massive amounts of data. With a suite of tools, you can create data lakehouses, design efficient data pipelines, execute Spark jobs for data processing, and use interactive notebooks for data tasks.
Data engineering is the art of orchestrating the data flow, transforming raw information into insights and decisions.
It is like the superhero toolkit for data wizards! With Microsoft Fabric, you are equipped with an array of data engineering superpowers to ensure your data is well organized, accessible, and top-notch.

Azure offers a multitude of services, each tailored to specific business needs. You can choose and use these Azure services according to your unique business requirements. Whether you need data storage, computation, security, or any other capability, Azure’s got a service designed to fit the bill.

3. Azure Data Engineering Project Example!

Imagine ‘TechTrend Solutions’ as a streaming company that provides a variety of content, including movies and TV shows, to its subscribers. We have developed a robust data engineering pipeline for TechTrend Solutions to analyze their data and efficiently classify their customers. This solution aims to provide TechTrend Solutions with valuable insights into their customers’ preferences and requirements, ultimately enhancing their understanding of their audience.

Azure Technologies and services we used are

SQL Server
Azure Data Factory
Azure Data Lake Storage (ADLS Gen 2)
Azure Databricks
Azure Synapse Analytics.

A. Data Ingestion:

We had the task of transferring data from an on-premises SQL server to the cloud, specifically ADLS (Azure Data Lake Storage), and we chose to employ Azure Data Factory (ADF) for this purpose.
Migrating data from an on-premises SQL server to the cloud can be intricate, but we simplified the process by utilizing the Self-Hosted Integration Runtime feature within ADF, providing scalability as needed.
Using ADF, we efficiently copied all the tables, employing a single copy activity within a “for each” loop. This approach guaranteed that all accessible tables were iterated through and copied to the intended destination.

B. Data Transformations Using Databricks:

Following data ingestion, we proceeded by creating a Transformed Data container in ADLS (Azure Data Lake Storage) and a corresponding database in Databricks.
To ensure secure data access, we established a robust connection between the landing container and Databricks. This security was achieved through the use of SAS tokens, a trusted method for accessing files. We implemented this secure connection via the Secret Scope feature.
Within the Databricks platform, we initiated a series of data transformations, including tasks such as data cleaning, null value removal, outlier handling, and fundamental validation checks.
In addition to basic transformations, we conducted more advanced data processing to uncover valuable trends and insights within the dataset, aligning with specific business requirements.
All the data that underwent processing within Databricks was then systematically written back to the Transformed container in ADLS. These processed datasets were also saved in the form of Delta tables for future reference and analysis.

C. Synapse Analytics:

We employed Azure Synapse Analytics as our central Data Warehouse system.
With this platform, we could easily execute queries using plain SQL language by creating SQL pools, simplifying data analysis.
Moreover, Azure Synapse Analytics supported the creation of data pipelines and the execution of Spark applications, all of which could be seamlessly managed through Spark pools or clusters. This allowed us to harness the power of distributed computing for complex data processing tasks.

D. Azure Key Vault:

To enhance security, we made effective use of Azure Key Vault.
Azure Key Vault served as a secure storage repository for secret keys, ensuring the confidentiality and integrity of sensitive data throughout the duration of the project.

E. Flow of the Project:

SQL server data transfer to ADLS initiated the process.
Subsequent data transformations in Databricks produced Delta tables.
The processed data was then written back to ADLS.
Finally, the data was analyzed within Azure Synapse Analytics.
In essence, our solution facilitated smooth data management and analysis, empowering TechTrend Solutions to provide superior customer service and customize their offerings to align with their customers’ preferences and needs.

4. Data Engineering Certification: Azure Data Engineer Associate DP-203

DP-203, or “Data Engineering on Microsoft Azure,” is an important certification that showcases your proficiency in the field of data engineering using Microsoft Azure.
It covers a wide range of skills and knowledge, from designing data storage solutions to data processing and transformation.
‘With DP-203, you validate your ability to create data solutions that are not only efficient but also secure and compliant.
Earning this certification not only enhances your professional credibility but also opens up new career opportunities in the ever-evolving world of data engineering on the Azure cloud platform. It’s a valuable asset for those who want to stay ahead in the world of data engineering.

In the vast world of data engineering, Azure is your trusted guide. This roadmap has unraveled the essential Azure Data Engineering components, from Data Factory to Databricks and Synapse Analytics. Learning is a journey; keep exploring official docs, videos, and practice. Data is the lifeblood of the digital era, and you, as an Azure Data Engineer, are the pioneers. Transform data into insights and spark innovation. Your voyage is a story of discovery and transformation, a journey uniquely yours.

March 18, 2024