- Job Category:
Entry level
- What is a Data Engineer?
- A professional responsible for building, managing, and optimizing data pipelines
foranalytics and machine learning systems.
- A professional responsible for building, managing, and optimizing data pipelines
- Key Responsibilities:
- Design and maintain scalable data architecture.
- Build and manage ETL processes.
- Ensure data quality, reliability, and security.
- Why Data Engineering?
- Increasing demand for data-driven decision-making across industries.
- Critical for enabling advanced analytics and AI systems.
Programming is essential for automating data workflows, building pipelines, and integrating tools.
- Python Basics:
- Syntax, variables, loops, and conditionals.
- Data structures: Lists, tuples, dictionaries, sets.
- Libraries:
- NumPy: Numerical computing.
- Pandas: Data manipulation and cleaning.
- Polars: High-performance DataFrames.
- Working with SQL Databases:
- Connect and query databases using
SQLAlchemyorpsycopg2.
- Connect and query databases using
- Official Python Docs
- Python Playlist
- Pandas Tutorials
- Module - Python for Data Engineering
- Become a Python Developer
Scala is an optional but valuable skill for data engineers working with distributed data systems like Apache Spark. Its concise syntax and compatibility with the JVM ecosystem make it a preferred choice for high-performance data engineering tasks.
- Native Language for Apache Spark: Scala is the original language of Apache Spark, offering better performance and compatibility.
- Functional and Object-Oriented Paradigm: Combines functional programming features with object-oriented principles for concise and robust code.
- JVM Compatibility: Integrates seamlessly with Java libraries and tools.
- Overview of Scala and its use in data engineering.
- Setting up the Scala environment.
- Syntax and structure: Variables, Data Types, and Control Flow.
- Higher-order functions.
- Immutability and working with immutable data.
- Closures, Currying, and Partially Applied Functions.
- Lists, Sets, Maps, and Tuples.
- Transformation operations:
map,flatMap,filter. - Reductions and Aggregations:
reduce,fold,aggregate.
- Futures and Promises.
- Introduction to Akka for building distributed systems.
- Setting up Spark with Scala.
- Working with RDDs, DataFrames, and Datasets.
- Writing Spark jobs in Scala.
- Pattern Matching and Case Classes.
- Traits and Abstract Classes.
- Type System and Generics.
SQL is critical for querying and managing relational databases efficiently.
- Basics: SELECT, INSERT, UPDATE, DELETE.
- Intermediate: Joins (INNER, OUTER), subqueries.
- Advanced: Window functions, CTEs, query optimization.
- PostgreSQL, MySQL Workbench.
- SQL Learning Playlist
- Programming with Mosh - SQL Playlist
- Module - SQL for Data Engineers
- Practice SQL on platforms like LeetCode or HackerRank.
Data warehousing is vital for storing and analyzing structured data at scale.
- Data Warehousing:
- Concepts: OLAP vs. OLTP.
- Schemas: Star and Snowflake.
- Fact and Dimension Tables.
- ETL vs. ELT:
- Extract, Transform, and Load processes.
- Tools: Apache Airflow, Talend.
Automates data workflows and ensures scalability of pipelines.
- Directed Acyclic Graphs (DAGs) for task scheduling.
- Task dependencies, operators, monitoring pipelines.
- Automating ETL workflows.
It is essential for processing and analyzing large datasets effectively.
- Hadoop Ecosystem:
- HDFS (distributed storage).
- MapReduce (data processing).
- Apache Spark:
- Spark with Python (PySpark).
- Databricks:
- Delta Lake, data versioning.
To handle unstructured and semi-structured data effectively, especially when relational databases aren't the best fit.
- Basics of NoSQL:
- Understand the types of NoSQL databases: Key-value, Document-based, Column-family, and Graph databases.
- Learn their use cases and differences from relational databases.
- MongoDB:
- CRUD operations (Create, Read, Update, Delete).
- Query operators and expressions.
- Aggregation pipelines for data processing.
- Document-oriented data model: Collections and Documents.
- MongoDB for document-based NoSQL.
- DynamoDB for key-value stores (AWS).
Cloud platforms are widely used for data storage, processing, and analytics.
- Cloud Computing Basics:
- Types of clouds: Public, private, hybrid.
- Google BigQuery:
- Querying and analyzing datasets.
- Integrating BigQuery with other tools.
Hands-on experience with end-to-end data engineering workflows.
- Extract data from a public API.
- Preprocess and clean the data using Python.
- Load data into a warehouse (BigQuery).
- Schedule workflows using Apache Airflow.
- Use SQL for data extraction.
- Preprocess and transform data using Python.
- Store data in data warehouses or NoSQL databases.
- Automate workflows with Apache Airflow.
- Process large datasets with big data tools like Spark.
- Visualize and analyze data for insights.
- Many modern applications require real-time data streaming.
- Enables real-time data ingestion, processing, and event-driven architectures.
- Essential for applications like fraud detection, recommendation systems, and IoT analytics.
- Kafka Architecture – Topics, partitions, brokers, producers, consumers.
- Kafka Streaming – Stream processing with Kafka Streams and KSQL.
- Integration – Kafka with Spark, Flink, and Data Lakes.
- Automates infrastructure provisioning and deployment of scalable data pipelines.
- Ensures reliability, version control, and security in cloud environments.
- Infrastructure as Code (IaC) – Automating cloud setup with Terraform.
- CI/CD Pipelines – Automating data workflow deployments (GitHub Actions, Jenkins).
- Monitoring & Security – Observability with Prometheus, Grafana, and cloud logging.
By following this roadmap step-by-step, you’ll be well-prepared to excel as a Data Engineer. Let me know if you'd like further guidance on any step! Please write an email to me.
Note: We suggest these premium courses because they are well-organized for absolute beginners and will guide you step by step, from basic to advanced levels. Always remember that T-shaped skills are better than i-shaped skill. However, for those who cannot afford these courses, don't worry! Search on YouTube using the topic names mentioned in the roadmap. You will find plenty of free tutorials that are also great for learning. Best of luck!
Hazrat Ali
- 🌐 LinkedIn Profile
- 🎓 Programmer | Software Engineering