Prerequisites for a Data Engineering
Preparing for a Data Engineering boot-camp can enhance your experience and success. Here are the core prerequisites:
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How is Data Engineer different from other Roles
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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.
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Data Ingestion Techniques
Understanding structured, semi-structured, and unstructured data.
Batch ingestion: Using Apache Sqoop, Talend.
Streaming ingestion: Using Apache Kafka.
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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.
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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.
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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.
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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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