data-engineering-material

My study material to the Data Engineering class by DataTalks.Club.

Syllabus

Module 1: Introduction & Prerequisites

• Course overview
• Introduction to GCP
• Docker and docker-compose
• Running Postgres locally with Docker
• Setting up infrastructure on GCP with Terraform
• Preparing the environment for the course
• Homework

Module 2: Workflow Orchestration

• Data Lake
• Workflow orchestration
• Workflow orchestration with Mage, Kesta
• Homework

Workshop 1: Data Ingestion

• Reading from apis
• Building scalable pipelines
• Normalising data
• Incremental loading
• Homework

Module 3: Data Warehouse

• Data Warehouse
• BigQuery
• Partitioning and clustering
• BigQuery best practices
• Internals of BigQuery
• BigQuery Machine Learning

Module 4: Analytics engineering

• Basics of analytics engineering
• dbt (data build tool)
• BigQuery and dbt
• Postgres and dbt
• dbt models
• Testing and documenting
• Deployment to the cloud and locally
• Visualizing the data with google data studio and metabase

Module 5: Batch processing

• Batch processing
• What is Spark
• Spark Dataframes
• Spark SQL
• Internals: GroupBy and joins

Technologies used

• Google Cloud Platform (GCP): Cloud-based auto-scaling platform by Google
  • Google Cloud Storage (GCS): Data Lake
  • BigQuery: Data Warehouse
• Terraform: Infrastructure-as-Code (IaC)
• Docker: Containerization
• SQL: Data Analysis & Exploration
• Mage: Workflow Orchestration
• dbt: Data Transformation
• Spark: Distributed Processing
• Kafka: Streaming

Tools used

• Docker and docker-compose
• Python3
• Google Cloud SDK
• Terraform