Data Engineering

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What Will You Learn?

  • The prerequisites for a Data Engineering course generally cover foundational knowledge in programming, databases, and basic data handling, as well as familiarity with cloud platforms

Course Content

Prerequisites for a Data Engineering
Preparing for a Data Engineering boot-camp can enhance your experience and success. Here are the core prerequisites:

How is Data Engineer different from other Roles

Data Ingestion, Storage & Processing
Introduction to Data Engineering Overview of Data Engineering in modern architectures. Data lifecycle and pipelines. Key technologies and trends (e.g., ETL, ELT, Batch Processing, Streaming). Activity: Discuss a real-world data pipeline use case.

Data Ingestion Techniques
Understanding structured, semi-structured, and unstructured data. Batch ingestion: Using Apache Sqoop, Talend. Streaming ingestion: Using Apache Kafka.

Data Storage Solutions
Relational databases (e.g., MySQL, PostgreSQL) vs. NoSQL databases (e.g., MongoDB, Cassandra). Cloud-based data storage (AWS S3, Azure Blob Storage). Choosing the right storage based on use cases.

Batch Processing with Apache Spark
Understanding Spark architecture. Loading and transforming data using Spark. Difference between RDDs, DataFrames, and Datasets. Activity: Run a sample batch processing job using Spark on a dataset.

Data Transformation, Orchestration & Monitoring
Data Transformation & ETL Tools Understanding ETL vs ELT. Using ETL tools: Talend, Apache Nifi, or Airflow. Data cleansing and transformation concepts. Activity: Create a data pipeline with Talend/Airflow for a simple ETL process.

Data Orchestration
Introduction to orchestration tools: Apache Airflow, AWS Step Functions. Creating workflows to manage complex pipelines. Managing dependencies and retries in workflows.

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