/data-engineering-zoomcamp

Code for Data Engineer Zoomcamp course

Data Engineering Zoomcamp

Syllabus

Note: This is preliminary and may change

Week 1: Introduction & Prerequisites

Duration: 1h

Week 2: Data ingestion + data lake + exploration

  • Data ingestion: 2 step process
    • Download and unpack the data
    • Save the data to GCS
  • Data Lake (20 min)
    • What is data lake?
    • Convert this raw data to parquet, partition
    • Alternatives to gcs (S3/HDFS)
  • Exploration (20 min)
    • Taking a look at the data
    • Data fusion => Glue crawler equivalent
    • Partitioning
    • Google data studio -> Dashboard
  • Terraform code for that

Duration: 1h

Week 3 & 4: Batch processing (BigQuery, Spark and Airflow)

  • Data warehouse (BigQuery) (25 minutes)
    • What is a data warehouse solution
    • What is big query, why is so fast (5 min)
    • Partitoning and clustering (10 min)
    • Pointing to a location in google storage (5 min)
    • Putting data to big query (5 min)
    • Alternatives (Snowflake/Redshift)
  • Distributed processing (Spark) (40 + ? minutes)
    • What is Spark, spark cluster (5 mins)
    • Explaining potential of Spark (10 mins)
    • What is broadcast variables, partitioning, shuffle (10 mins)
    • Pre-joining data (10 mins)
    • use-case ?
    • What else is out there (Flink) (5 mins)
  • Orchestration tool (airflow) (30 minutes)
    • Basic: Airflow dags (10 mins)
    • Big query on airflow (10 mins)
    • Spark on airflow (10 mins)
  • Terraform code for that

Duration: 2h

Week 5: Analytics engineering

  • Basics (15 mins)
    • What is DBT?
    • ETL vs ELT
    • Data modeling
    • DBT fit of the tool in the tech stack
  • Usage (Combination of coding + theory) (1:30-1:45 mins)
    • Anatomy of a dbt model: written code vs compiled Sources
    • Materialisations: table, view, incremental, ephemeral
    • Seeds
    • Sources and ref
    • Jinja and Macros
    • Tests
    • Documentation
    • Packages
    • Deployment: local development vs production
    • DBT cloud: scheduler, sources and data catalog (Airflow)
  • Extra knowledge:
    • DBT cli (local)

Duration: 1.5-2h

Week 6: Streaming

  • Basics
    • What is Kafka
    • Internals of Kafka, broker
    • Partitoning of Kafka topic
    • Replication of Kafka topic
  • Consumer-producer
  • Streaming
    • Kafka streams
    • spark streaming-Transformation
  • Kafka connect
  • KSQLDB?
  • streaming analytics ???
  • (pretend rides are coming in a stream)
  • Alternatives (PubSub/Pulsar)

Duration: 1-1.5h

Upcoming buzzwords

  • Delta Lake/Lakehouse
    • Databricks
    • Apache iceberg
    • Apache hudi
  • Data mesh

Duration: 10 mins

Week 7, 8 & 9: Project

  • Putting everything we learned to practice

Duration: 2-3 weeks

Architecture diagram

Instructors

FAQ

  • Q: At what time of the day will it happen? A: Most likely on Mondays at 17:00 CET. But everything will be recorded, so you can watch it whenever it's convenient for you
  • Q: Will there be a certificate? A: Yes, if you complete the project
  • Q: I'm 100% not sure I'll be able to attend. Can I still sign up? A: Yes, please do! You'll receive all the updates and then you can watch the course at your own pace.
  • Q: Do you plan to run a ML engineering course as well? A: Glad you asked. We do :)