Complete Roadmap to become Azure Data Engineer

As the amount of data generated by businesses continues to grow exponentially, the need for skilled data engineers who can design, build, and maintain complex data infrastructure is becoming increasingly important. If you’re interested in pursuing a career as an Azure Data Engineer, there are several skills that you will need to master to be successful. This blog post will discuss Complete Roadmap to becoming an Azure Data Engineer.

1. Knowledge of SQL

As an Azure Data Engineer, you’ll be working with large datasets and need to know how to write and optimize SQL queries. SQL (Structured Query Language) is used to extract and manipulate data from relational databases, and you need to be familiar with it to be an Azure Data Engineer. You should know how to write complex queries that join multiple tables, use subqueries, and aggregate data. In addition, you should be able to optimize queries by creating indexes and designing efficient data structures.

Topics to Learn :

A. SQL basics

• Introduction to SQL and its uses
• Basic SQL syntax
• Creating tables
• Inserting and updating data

B. Advanced SQL concepts

• Joins and subqueries
• Aggregate functions
• Indexing and optimization

C. SQL and data modeling

• Normalization
• Indexing
• Referential integrity

you can refer following blog to learn more about Most Important SQL topics for data engineers

2. Data Modeling

Data modeling is the process of designing a logical and physical data model for a system. As an Azure Data Engineer, you’ll need to understand data modeling concepts, including entity-relationship diagrams, data normalization, and data integrity. You should be able to design and develop a data model that is optimized for performance and scalability.

Topics to Learn:

A. Conceptual modeling

• Entity-relationship diagrams
• Data dictionaries

B. Logical modeling

• Normalization and denormalization
• Object-oriented data modeling

C. Physical modeling

• Implementation of a data model in a database management system
• Indexing
• Partitioning

3. Programming skills

You should know programming languages like Python, Scala, or Java and have experience with coding in these languages to build data processing pipelines. You should be able to write efficient and scalable code that can handle large datasets.

Python (Recommended)

I recommend learning Python as a programming language as it’s the most used programming language in the data engineering field.

Python is a popular programming language used in data engineering due to its versatility, ease of use, and wide range of data manipulation libraries. As a data engineer, you may need to use Python to develop ETL pipelines, automate data processing tasks, and perform data analysis. Here are some key concepts and libraries to be familiar with as a data engineer using Python:

1. Basic Python syntax and data structures: As a data engineer, you should be familiar with the basic syntax and data structures in Python. This includes variables, data types (e.g., strings, integers, floats), loops, conditionals, and functions.
2. NumPy: NumPy is a library used for numerical computing in Python. It provides an efficient way to work with arrays and matrices, making it useful for data manipulation tasks such as reshaping, slicing, and indexing data.
3. Pandas: Pandas is a library used for data manipulation and analysis in Python. It provides data structures such as data frames and series that make it easy to work with tabular data.
4. Python libraries for ETL: As a data engineer, you may need to develop ETL pipelines using Python. Some useful libraries for this include:
5. Apache Airflow: Apache Airflow is an open-source platform used for creating, scheduling, and monitoring workflows. It provides a way to define ETL pipelines as directed acyclic graphs (DAGs) in Python.
6. PySpark: PySpark is the Python API for Apache Spark, a popular big data processing framework. PySpark can be used to develop ETL pipelines that scale to handle large datasets.
7. SQLAlchemy: SQLAlchemy is a Python library used for working with databases. It provides a way to write database-agnostic code, making it useful for ETL tasks that involve multiple databases.
8. Python libraries for data analysis: In addition to manipulating and processing data, data engineers may need to perform data analysis using Python. Some useful libraries for this include:
9. Matplotlib: Matplotlib is a library used for creating visualizations in Python. It provides a way to create bar charts, line plots, scatter plots, and more.

In summary, Python is a versatile programming language that can be used for a wide range of data engineering tasks. By learning the basic syntax and data structures, as well as popular data manipulation, ETL, and data analysis libraries, you can become proficient in using Python for data engineering tasks.

You can learn more about Important Python topics for data engineers in following blog

4. Data Warehousing

You should have a strong understanding of data warehousing concepts, such as star and snowflake schema, and how to design and develop a data warehouse. This includes understanding how to load data into a data warehouse, how to manage data partitions, and how to optimize queries for performance.

Topics to Learn:

A. Introduction to data warehousing

• Data warehousing concepts
• Advantages and disadvantages of data warehousing

B. Designing a data warehouse

• Star schema
• Snowflake schema
• Data mart

C. ETL process

• Extracting data from source systems
• Transforming data to conform to the data warehouse schema
• Loading data into the data warehouse

5. ETL

ETL (Extract, Transform, Load) is the process of extracting data from various sources, transforming and cleaning it, and loading it into a data warehouse. As an Azure Data Engineer, you’ll need to know how to work with tools like Azure Data Factory to build ETL pipelines, as well as how to write custom code to extract and transform data.

Topics to Learn :

A. Introduction to ETL

• What is ETL and why is it important?
• Different types of ETL processes

B. ETL tools and techniques

• Using Azure Data Factory for ETL
• Building custom ETL pipelines with Python or .NET

C. ETL best practices

• Error handling and logging
• Performance tuning
• Monitoring and management

6. Cloud Computing

You should have a strong understanding of Cloud Computing concepts like scalability, elasticity, and security in the context of Azure cloud services. You should know how to design and implement solutions that are optimized for the cloud.

Topics to Learn:

A. Introduction to cloud computing

• Definition of cloud computing
• Advantages and disadvantages of cloud computing

B. Cloud computing services

• Overview of cloud computing services offered by Azure
• Advantages of using Azure for cloud computing

C. Cloud computing security

• Overview of cloud computing security
• Best practices for securing data in the cloud

7. Azure Services

As an Azure Data Engineer, you’ll need to have a strong understanding of various Azure services such as Azure Data Factory, Azure Databricks, Azure Synapse Analytics, Azure Analysis Services, Azure Stream Analytics, and Azure Data Lake Storage. This includes knowing how to configure and manage these services, as well as how to integrate them with other Azure services.

Azure Services to Learn:

A. Introduction to Azure services

• Overview of Azure cloud services
• Advantages of using Azure services for data engineering

B. Azure Data Factory

• Creating data pipelines with Data Factory
• Scheduling and monitoring data pipelines

C. Azure Synapse Analytics

• Building data warehouses and data marts with Synapse Analytics
• Using Synapse Studio for data exploration and analysis

D. Azure Databricks

• Creating Spark clusters for data processing and machine learning
• Using notebooks for data exploration and analysis

E. Azure Analysis Services

• Building and deploying analytical models
• Integrating with other Azure services

8. Data Analytics and Visualization tools

You should have experience with data analytics and visualization tools like Power BI, Tableau, or other BI tools to build dashboards and reports. You should be able to create compelling visualizations that help stakeholders understand the data and make data-driven decisions.

Topics to Learn:

A. Introduction to data analytics and visualization

• Overview of data analytics and visualization tools
• Advantages of using Power BI or Tableau for data visualization

B. Power BI

• Creating reports and dashboards with Power BI
• Data modeling and transformation with Power Query

9. Big Data Technologies

Familiarity with Big Data technologies like Hadoop, Spark, and NoSQL databases is crucial. You should know how to work with these technologies to manage and process large datasets.

A. Introduction to big data

• Definition of big data
• Characteristics of big data

B. Big data technologies

Apache Hadoop :

1. Introduction to Hadoop: This includes understanding the architecture, components, and features of Hadoop.
2. HDFS (Hadoop Distributed File System): HDFS is the file system used by Hadoop for storing and processing large datasets. Learning how to work with HDFS is essential for data engineers working with big data.
3. MapReduce: MapReduce is a programming model used for processing large datasets in parallel. Understanding how MapReduce works are essential for building applications in Hadoop.
4. YARN (Yet Another Resource Negotiator): YARN is the resource management system used by Hadoop. Understanding how YARN works are important for managing resources and scheduling jobs in Hadoop.
5. Hadoop ecosystem: Hadoop has a rich ecosystem of tools and technologies that can be used for data processing, storage, and analysis. Learning about some of these tools, such as Pig, Sqoop, and Flume, is important for data engineers working with big data.

Apache Spark:

1. Overview of Spark: This includes understanding the architecture, components, and features of Spark.
2. RDDs (Resilient Distributed Datasets): RDDs are the fundamental data structure in Spark. Understanding how RDDs work is essential for building applications in Spark.
3. Spark SQL: Spark SQL provides a way to work with structured data using SQL queries. Learning how to use Spark SQL is important for data analysts and data engineers alike.
4. Spark Streaming: Spark Streaming is used for processing real-time data streams. Learning how to use Spark Streaming is important for data engineers working with data in motion.

Apache Hive:

1. Introduction to Hive: This includes understanding what Hive is, how it works, and why it is used.
2. HiveQL: HiveQL is a SQL-like language used for querying data stored in Hive. Learning how to use HiveQL is essential for data analysts and data engineers working with big data.
3. Hive Metastore: The Hive Metastore is a central repository that stores metadata about Hive tables and partitions. Understanding how the Hive Metastore works is important for managing data in Hive.
4. Data serialization and deserialization: Hive uses a serialization and deserialization (SerDe) framework to read and write data from and to Hadoop Distributed File System (HDFS). Understanding how SerDe works is essential for data engineers working with data in Hive.

10. Soft Skills

Excellent communication, problem-solving, and project management skills are crucial for a successful Azure Data Engineer. You must work closely with stakeholders to understand their requirements and design solutions that meet their needs. You should be able to manage projects effectively and be able to work collaboratively with other members of the team.

Conclusion

Overall, to become an Azure Data Engineer, you need to have a strong foundation in SQL, data modeling, data warehousing, ETL, Azure services, data analytics, and visualization

I hope you like this blog.

Follow for more such content for data engineering

Best of luck with your journey!!!

Follow for more such content on Data Engineering and Data Science.

Resources used to write this blog:

• Learn from Youtube Channels: Darshil Parmar, e-learning bridge, data engineering, GeekCoders, Ankit Bansal, Data Savvy, TechTFQ
• I used Google to research and resolve my doubts
• From my Experience
• I used Grammarly to check my grammar and use the right words.

if you enjoy reading my blogs, consider subscribing to my feeds. also, if you are not a medium member and you would like to gain unlimited access to the platform, consider using my referral link right here to sign up.