Data Engineering Zoom Camp

• Module 1: Containerization and Infrastructure as Code
• Module 2: Workflow Orchestration
• Workshop 1: Data Ingestion
• Module 3: Data Warehouse
• Module 4: Analytics Engineering
• Module 5: Batch processing
• Module 6: Streaming
• Workshop 2: Stream Processing with SQL
• Project

Overview

Syllabus

Module 1: Containerization and Infrastructure as Code

• 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

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Module 2: Workflow Orchestration

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

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Workshop 1: Data Ingestion

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

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Module 3: Data Warehouse

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

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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

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Module 5: Batch processing

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

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Module 6: Streaming

• Introduction to Kafka
• Schemas (avro)
• Kafka Streams
• Kafka Connect and KSQL

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Workshop 2: Stream Processing with SQL

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Project