“Data Engineering 101: A Beginner’s Guide”

Data Engineering: The Backbone of Data-Driven Decisions

What Does a Data Engineer Do?

There are still so many people who don’t know what a data engineer does. Don’t worry—you’re reading the right article!

Have you ever wondered who manages the massive amount of data that drives your favorite applications? Think about how Instagram curates content tailored to your interests, or how Alibaba shows you just the right product to buy. Even while reading this article, you’re receiving recommendations for what to read next. Do you think all this is magic? No, it’s not magic. Data engineers are the librarians that manage all this digital data.

Data engineering is one of the most promising careers, projected to breach the $100 billion mark by 2028, signaling robust expansion in the field. A data engineer’s job is to manage and maintain all this data so that data scientists or business analysts can make informed decisions.

The Need for Data Engineering

Imagine using Instagram and not finding the content you like—would you still use the application? Probably not. Companies want to understand their users’ behavior to improve their products and services and increase profits. By analyzing data, they can determine what users want and make data-driven decisions to enhance their offerings and profitability.

A Day in the Life of a Data Engineer

1. Calls and Meetings: Collaborating with business stakeholders or data scientists to understand their data needs and processing requirements.
2. Monitoring Data Pipelines: Ensuring data pipelines are running smoothly, much like maintaining a water pipe but for data.
3. Data Integration: Combining data from multiple sources into a structured format for analysis.
4. Data Integrity: Preventing data leaks and ensuring smooth data flow.
5. Building and Maintaining Pipelines: Adding new data sources and creating new data pipelines.
6. Data Modeling and Documentation: Developing new data models and documenting processes.
7. Data Cleaning and Standardization: Writing algorithms to clean and standardize data, making it usable for analysis.

Essential Technologies for Data Engineers

• Microsoft Azure Services:
  • Data Ingestion: Azure Function, Datahub, Azure Data Factory
  • Data Catalogs, Azure Data Lake
  • Data Transformation: Azure Databricks (Apache Spark hosted environment)
  • Data Analysis: Azure Synapse, Snowflake, BigQuery

Challenges Faced by Data Engineers

• Data Inconsistencies: Different data formats across sources.
• Scalability: Ensuring the system can handle data growth.
• Privacy: Maintaining data privacy and security.

ETL – Extract, Transform, Load

ETL involves extracting data from multiple sources (RDBMS, third-party APIs, sensors), transforming it based on business logic, and loading it into a target location like a data warehouse.

High-Demand Tools for Building Data Pipelines

1. Apache Airflow: Facilitates the ETL process using Python or SQL.
2. Cloud Computing Services: Amazon Web Services, Microsoft Azure, Google Cloud Platform (GCP).

Data Engineering Roadmap

1. Computer Science Fundamentals: Understanding code compilation, execution, data structures, algorithms, and programming basics.
   • Resource: Harvard CS50 – Computer Science Fundamentals
2. Programming Languages: Learning Python, Scala, or Java to automate workflows and design pipelines.
3. SQL Proficiency: Communicating with and manipulating databases using SQL.
4. Core Foundations of Data:
   • Data Warehousing: Understanding OLAP, OLTP systems, ETL processes, ER modeling, and dimensional modeling.
   • Resources: “The Data Warehouse Toolkit,” tools like Snowflake, BigQuery, Amazon Redshift
5. Data Processing: Understanding batch and real-time data processing (e.g., Apache Kafka, Apache Spark).
6. Workflow Management Tools: Apache Airflow
7. Cloud Platforms: AWS, Microsoft Azure, Google Cloud Platform (GCP)
8. Data Lakes: Centralized data repositories for querying and selecting data chunks (e.g., Iceberg, Hudi).
9. Data Observability Tools: Tools like DataDog for monitoring the modern data stack.

Conclusion

Next time you go shopping on your favorite online platform, remember there is a data engineer working behind the scenes, making your digital life smoother and smarter. Pretty cool, right? There are so many different roles and responsibilities data engineers fulfill.

Will AI Replace Data Engineers?

If you’re worried about artificial intelligence taking your job, remember AI completely depends on the right data for training. Data engineers are crucial for ensuring the right data gets processed. AI models rely heavily on data engineers, who are the backbone of these models.