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:
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Preparing for a Data Engineering boot-camp
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Roadmap to become Data Engineer
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How is Data Engineer different from other Roles
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The Difference
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Data engineer and Data architect same or different???
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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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Overview of Data Engineering in modern architectures.
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Data lifecycle and pipelines
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Key technologies and trends (e.g., ETL, ELT, Batch Processing, Streaming).
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Activity: Discuss a real-world data pipeline use case.
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Assignment 1
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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Understanding structured, semi-structured, and unstructured data.
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Batch vs Stream
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Batch ingestion: Using Apache Sqoop, Talend.
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Streaming ingestion: Using Apache Kafka.
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Activity: Set up a simple data ingestion pipeline using 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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Relational databases (e.g., MySQL, PostgreSQL) vs. NoSQL databases (e.g., MongoDB, Cassandra).
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Cloud-based data storage (AWS S3, Azure Blob Storage).
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Choosing the right storage based on use cases.
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Activity: Hands-On Setup for Storing Ingested Data Using AWS S3 or Azure Blob Storage
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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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Understanding Spark Architecture
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Loading and Transforming Data Using Apache Spark
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Difference between RDDs, DataFrames, and Datasets.
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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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Understanding ETL vs ELT.
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Using ETL tools: Talend, Apache Nifi, or Airflow.
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Data cleansing and transformation concepts.
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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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Data orchestration process and benifits
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Data Engineering
About Lesson

Cloud-Based Data Storage: AWS S3 and Azure Blob Storage

Cloud storage provides scalable and cost-effective solutions for storing data with global accessibility and built-in redundancy.

  • Amazon S3 (Simple Storage Service):
    • Object-based storage service designed for scalability, security, and durability.
    • Ideal for storing any type of data: text, images, videos, backups, etc.
    • Key Features:
      • Pay-as-you-go pricing: Only pay for what you use.
      • Integrated with AWS services: Easy integration for data pipelines.
      • Lifecycle policies: Automatically transition data to cheaper storage tiers or delete after a set time.

  • Azure Blob Storage:
    • Microsoft’s object storage solution for storing unstructured data.
    • Designed for large-scale storage needs with access via HTTP/HTTPS.
    • Key Features:
      • Tiers of storage: Hot, cool, and archive tiers for cost-effective data management.
      • Scalability and high availability: Built-in redundancy across regions.
      • Security: Role-based access control (RBAC) and encryption options.

Use Cases:

  • Storing backups, large datasets, multimedia files, and log data; perfect for use with data pipelines or ETL processes.

 

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