Skip to content

Data Engineering Study Plan.md

Latest commit

 

History

History
205 lines (179 loc) · 8.88 KB

Data Engineering Study Plan.md

File metadata and controls

205 lines (179 loc) · 8.88 KB

Comprehensive Data Engineering Study Plan and POC Outline

Overview

This study plan is designed to enhance C++ skills while building data engineering capabilities, culminating in the development of a Proof of Concept (POC) for a lakehouse architecture. The plan is structured to be completed within one month.

Week 1-2: Foundations and C++ Enhancement

C++ Skills Enhancement (2-3 hours daily)

  • Review and practice advanced C++ concepts:
    • Polymorphism (compile-time and runtime)
    • Virtual functions and pure virtual functions
    • Copy constructors and assignment operators
    • Smart pointers (shared_ptr, unique_ptr)
    • Memory management and debugging
    • STL containers (vector, map, unordered_map)
  • Practice problem-solving with C++ on platforms like LeetCode or HackerRank
  • Specific C++ libraries to focus on:
    • Boost libraries for advanced C++ programming
    • Google Test for unit testing C++ code
    • JSON for Modern C++ (nlohmann/json) for JSON handling

Python for Data Engineering (2-3 hours daily)

  • Python basics review
  • Advanced Python concepts:
    • List comprehensions, generators, decorators
    • Object-oriented programming in Python
    • File I/O operations, including JSON handling
  • Introduction to NumPy and Pandas
    • Data manipulation with Pandas
    • Basic data analysis and visualization
  • Specific Python libraries and topics:
    • Pandas: DataFrame operations, groupby, merging, and aggregation
    • NumPy: Array operations, vectorization, and mathematical functions
    • Matplotlib and Seaborn for data visualization
    • Requests library for API interactions
    • PyYAML for YAML file handling
    • Python logging module for proper logging practices

Version Control and Collaboration (1 hour daily)

  • Git fundamentals
  • GitHub/GitLab workflows
  • Collaborative coding practices
  • Specific topics:
    • Branching strategies (e.g., Git Flow)
    • Pull requests and code review practices
    • Git hooks for automated checks
    • Using .gitignore effectively

Week 3: Big Data Technologies and Cloud Basics

Apache Spark and PySpark (3-4 hours daily)

  • Spark architecture and core concepts
  • RDDs, DataFrames, and Datasets
  • Spark SQL
  • PySpark basics and data processing
  • Specific topics and libraries:
    • Parquet file format for efficient data storage
    • ORC file format as an alternative to Parquet
    • Delta Lake for implementing ACID transactions on data lakes
    • Spark Streaming for real-time data processing
    • MLlib for machine learning tasks in Spark
    • Spark catalyst optimizer for query optimization
    • Implementing Change Data Capture (CDC) using Spark
    • Spark window functions for advanced analytics

Cloud Fundamentals (2-3 hours daily)

  • Introduction to cloud computing concepts
  • Overview of major cloud providers (AWS, Azure, GCP)
  • Basic cloud services relevant to data engineering:
    • Storage (S3, Azure Blob Storage, Google Cloud Storage)
    • Compute (EC2, Azure VMs, Google Compute Engine)
    • Database services (RDS, Azure SQL Database, Cloud SQL)
  • Specific services and concepts:
    • AWS Glue for serverless ETL
    • Azure Data Factory for data integration
    • Google Cloud Dataflow for stream and batch processing
    • Implementing data lakes using cloud storage services
    • Understanding and implementing data partitioning in cloud storage
    • Cloud IAM (Identity and Access Management) basics

ETL Concepts (1-2 hours daily)

  • Understanding ETL processes
  • Data warehousing concepts
  • Data quality and data cleansing techniques
  • Specific topics:
    • Slowly Changing Dimensions (SCD) types and implementation
    • Data profiling techniques and tools (e.g., Great Expectations library)
    • Implementing data lineage in ETL processes
    • Handling data schema evolution in ETL pipelines
    • Techniques for incremental data loading

Week 4: Lakehouse Architecture and POC Development

Lakehouse Architecture (2-3 hours daily)

  • Concept and benefits of lakehouse architecture
  • Key components: data lake, data warehouse, and metadata layer
  • Comparison with traditional data warehouses and data lakes
  • Specific technologies:
    • Apache Hudi for hybrid transactional/analytical processing
    • Iceberg for table formats in data lakes
    • Presto or Trino for SQL queries on data lakes
    • Understanding and implementing data skipping and indexing in lakehouses

POC Development: Implementing Lakehouse Architecture (4-5 hours daily)

  1. Design Phase (Day 1-2)

    • Define POC objectives based on the problem statement
    • Design the lakehouse architecture with Bronze, Silver, and Gold layers
    • Plan the ETL workflow incorporating Apache Airflow
    • Tools: Draw.io or Lucidchart for architecture diagrams

  2. Setup Phase (Day 3-4)

    • Set up a local development environment
    • Initialize a Git repository for version control
    • Set up a mock cloud environment (using LocalStack or Minio for S3-like storage)
    • Install and configure Apache Airflow for workflow orchestration
    • Tools: Docker for containerization, Poetry for Python dependency management

  3. Development Phase (Day 5-15)

    a. Ingestion Phase (Bronze Layer)

    • Implement Apache Airflow DAGs for orchestrating data ingestion tasks
      • Library: apache-airflow
    • Develop scripts to read and ingest data from CSV and JSON files
      • Libraries: pandas, pyspark
    • Implement web scraping techniques for data collection
      • Libraries: beautifulsoup4, scrapy
    • Store ingested data in Parquet format in the bronze layer
      • Libraries: pyarrow, fastparquet
    • Implement strategy to handle large files (up to 1GB)
      • Technique: Chunked reading with pandas or PySpark

    b. Transformation Phase (Silver Layer)

    • Develop PySpark scripts for data cleaning and transformation
      • Remove nulls and duplicates
      • Implement data type conversions and standardizations
    • Implement Change Data Capture (CDC) using upsert techniques
      • Library: delta lake for ACID transactions
    • Store transformed data in Parquet format in the silver layer

    c. Aggregation and Joining Phase (Gold Layer)

    • Develop PySpark scripts for data aggregation and joining
    • Implement advanced analytics techniques
      • Libraries: scikit-learn for machine learning tasks
    • Store aggregated data in Parquet format in the gold layer

    d. Data Governance and Quality

    • Implement data quality checks using Great Expectations
      • Library: great_expectations
    • Set up data lineage tracking
      • Tool: Apache Atlas or a custom solution using metadata tables
    • Implement schema enforcement and evolution handling
      • Library: pyspark.sql.types for schema definition

    e. Data Consumption Layer

    • Set up a connection interface for visualization tools (Power BI or Tableau)
    • Implement sample queries and dashboards for demonstration
    • If possible, create a simple Flask API to serve data to front-end applications
      • Libraries: flask, flask-restful

  4. Testing and Documentation (Day 16-18)

    • Write unit tests for critical components
      • Use pytest for Python testing
    • Perform integration testing of the entire pipeline
    • Document the architecture, code, and processes
      • Use Sphinx for Python documentation
    • Create a data dictionary and lineage documentation

  5. Presentation Preparation (Day 19-20)

    • Prepare a technical presentation of the POC
    • Create a demo script showcasing the entire data flow
    • Prepare sample visualizations in Power BI or Tableau
    • Practice presenting the POC
    • Tools: Jupyter Notebooks for interactive code demonstrations

Additional Learning for POC (Throughout the week)

  • Apache Airflow concepts and best practices
  • Web scraping techniques and ethics
  • Performance optimization for large dataset processing
  • Data modeling for lakehouse architecture
  • Best practices for data governance in a lakehouse environment

Daily Schedule

  • 2-3 hours: Core learning (focused on POC-related technologies)
  • 3-4 hours: POC development
  • 1 hour: Problem-solving and coding practice related to POC challenges
  • 1 hour: Review and reinforcement

Resources

  • Online platforms: Coursera, edX, Udacity for structured courses
  • Documentation: Apache Spark, Python, Pandas, Cloud provider docs
  • Books: "Designing Data-Intensive Applications" by Martin Kleppmann
  • Practice platforms: LeetCode, HackerRank for coding challenges
  • Community: Stack Overflow, GitHub discussions for problem-solving
  • Apache Airflow documentation
  • PySpark documentation
  • Web scraping tutorials (BeautifulSoup, Scrapy)
  • Delta Lake documentation
  • Great Expectations documentation
  • Power BI / Tableau tutorials for big data connectivity

Notes

  • Adjust the pace and focus areas based on progress and specific challenges encountered during the learning process and POC development.
  • Regularly review and update the plan as needed to ensure it aligns with the learner's progress and any changing requirements.
  • Encourage hands-on practice and real-world application of concepts throughout the learning process.