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Data Lake Architecture

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About the Course

This course covers the design, implementation, and management of data lakes for storing, processing, and analyzing large-scale data. Participants will learn about data lake architecture, best practices, and essential tools, including data ingestion, cataloging, governance, and query optimization. This practical course is ideal for professionals looking to leverage data lakes to handle diverse and complex data ecosystems.

Curriculum

  • Module 1: Introduction to Data Lake Architecture

    Overview:

    This module provides an introduction to data lakes, explaining their purpose, benefits, and how they differ from data warehouses. It also covers data lake design principles and considerations.

    Topics to Cover:

    • What is a data lake, and why it’s important
    • Key differences between data lakes and data warehouses
    • Data lake architecture components (e.g., storage, compute, metadata)

  • Module 2: Data Ingestion and Storage Layers

    Overview:

    This module covers methods for ingesting diverse data types into a data lake, including real-time and batch ingestion techniques, as well as storage options.

    Topics to Cover:

    • Ingesting structured, semi-structured, and unstructured data
    • Real-time vs. batch ingestion (e.g., Kafka, AWS Kinesis, Apache Flume)
    • Choosing storage layers: object storage, file systems, HDFS

  • Module 3: Metadata Management and Data Cataloging

    Overview:

    Participants will learn about managing metadata and cataloging data in a data lake to improve data discoverability and governance.

    Topics to Cover:

    • Introduction to metadata and its importance
    • Using data catalogs for data discovery (e.g., AWS Glue, Apache Atlas)
    • Data lineage and data provenance tracking

  • Module 4: Data Lake Governance and Security

    Overview:

    This module explores best practices for securing data lakes, ensuring compliance, and managing access to sensitive data.

    Topics to Cover:

    • Data governance frameworks for data lakes
    • Access control, role-based permissions, and encryption
    • Regulatory compliance in data lakes (e.g., GDPR, HIPAA)

  • Module 5: Data Processing and Analytics in Data Lakes

    Overview:

    This module covers tools and methods for transforming and analyzing data stored in a data lake, enabling efficient querying and data processing.

    Topics to Cover:

    • Data transformation using Spark, EMR, and Databricks
    • Querying data lakes with SQL engines (e.g., Presto, Trino, Athena)
    • Optimizing data lake performance for large-scale analytics

  • Module 6: Real-Time Data Lake Architecture

    Overview:

    Participants will learn how to design a real-time data lake architecture, focusing on stream processing, event-driven data, and latency-sensitive use cases.

    Topics to Cover:

    • Stream processing with Spark Streaming, Flink, or Kinesis
    • Event-driven architectures and use cases (e.g., IoT, real-time dashboards)
    • Handling low-latency data requirements

  • Module 7: Real-World Applications and Project

    Overview:

    In this capstone module, participants will apply their knowledge to design and implement a data lake for a specific use case, demonstrating the skills learned in the course.

    Topics to Cover:

    • Project planning: defining requirements and architecture design
    • Implementing data ingestion, governance, and analytics layers
    • Presenting a functional data lake model with best practices

Learning Outcomes

By the end of this course, participants will be able to:

  • Design scalable data lake architectures for large and varied data sets.
  • Ingest and catalog data for enhanced accessibility and governance.
  • Apply security and compliance best practices within data lake environments.
  • Use data processing and analytics tools to perform transformations and queries.
  • Implement a complete data lake project for a real-world application.

Tools

Data lake platforms: AWS S3, Azure Data Lake Storage, Google Cloud Storage
Metadata and cataloging tools: Apache Atlas, AWS Glue, Informatica
Processing tools: Apache Spark, Presto, Apache Flink, AWS EMR
Data ingestion tools: Apache Kafka, AWS Kinesis, Apache NiFi
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FAQ

Frequently asked questions about the bootcamp

The course is structured into weekly modules, each containing video lectures, reading materials, assignments, and quizzes. You can complete the modules at your own pace, but we recommend following the weekly schedule to stay on track.

You can get support in multiple ways:

  • TA Support on Slack: Our teaching assistants are available on Slack to answer your questions and provide guidance.
  • Peer Community on Discord: Join our Discord community to discuss course topics, share ideas, and collaborate with fellow students.

TAs are available on Slack from 9 AM to 6 PM (ET) Monday to Friday. Outside these hours, you can still post your questions, and TAs will respond as soon as they are back online.

After enrolling in the course, you will receive an invitation link to join the Discord community. Follow the link to create an account or log in to your existing account.

The Discord community offers peer-to-peer support, where you can discuss course topics, share resources, collaborate on projects, and network with fellow learners

The optional mentoring service includes one-on-one sessions with an experienced mentor who can provide personalized guidance, feedback on your progress, and help you set and achieve your learning goals.

Please talk to our Program Advisors to sign up for Mentorship services for an additional cost

Yes, you will have lifetime access to the course materials, including any updates made to the content in the future.

We accept all major credit cards, PayPal, and bank transfers. You can choose your preferred payment method at checkout

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