/data-engineering-zoomcamp

Code for Data Engineer Zoomcamp course

Primary LanguageHCL

Data Engineering Zoomcamp

Syllabus

Note: This is preliminary and may change

Week 1: Introduction & Prerequisites

  • Big Picture (Everyone, live call)

    • Introduction to all instructors
    • What to expect in this course
    • Architecture / Data Flow
    • What do we want to build (DE Concepts using Taxi Rides, and end results)

  • GCP (Ankush)

    • Intro to GCP - Concepts: IAM, Cloud Storage, BigQuery (relevant components)
    • What GCP is, why we need it

  • Terraform (Sejal)

    • Intro to Terraform - Concepts
    • Setting up GCP with TF: Storage, BigQuery

  • Docker (Alexey)

    • What docker is, why we need it
    • Running postgres locally with docker
    • Putting some data for testing to local postres with Python
    • Packaging this script in docker
    • Running postgres and the script in one network
    • Docker compose and running pgadmin and postres together with docker-compose

  • SQL (Alexey, Victoria)

    • Relevant SQL Queries (Refresher): group by, joins, window function, union
    • Dataset: Taxi Rides NY dataset
    • Experimentation: Taking a first look at the data
    • (Alexey and Victoria will coordinate)

Duration: 1-1.5h

Week 2: Data ingestion

Goal: Orchestrating a job to ingest web data to a Data Lake in its raw form.

Instructor: Sejal

  • Data Lake (GCS) -- 10 mins

    • Basics, What is a Data Lake
    • ELT vs. ETL
    • Alternatives to components (S3/HDFS, Redshift, Snowflake etc.)

  • Orchestration (Airflow) -- 15 mins

    • Basics
    • What is an Orchestration Pipeline
    • ...

  • Demo:

    • Setup: (15 mins)
      • Docker pre-reqs (refresher)
      • Airflow env with Docker
    • Data ingestion DAG - Demo (30 mins):
      • Extraction: Download and unpack the data
      • Pre-processing: Convert this raw data to parquet, partition (raw/yy/mm/dd)
      • Load: Raw data to GCS
      • Exploration: External Table for BigQuery -- Taking a look at the data
      • Further Enhancements: Transfer Service (AWS -> GCP)

Duration: 1.5h

Week 3: Data Warehouse

Goal: Structuring data into a Data Warehouse

Instructor: Ankush

  • Data warehouse (BigQuery) (25 minutes)
    • What is a data warehouse solution
    • What is big query, why is it so fast, Cost of BQ, (5 min)
    • Partitoning and clustering, Automatic re-clustering (10 min)
    • Pointing to a location in google storage (5 min)
    • Loading data to big query & PG (10 min) -- using Airflow operator?
    • BQ best practices
    • Misc: BQ Geo location, BQ ML
    • Alternatives (Snowflake/Redshift)

Duration: 1-1.5h

Week 4: Analytics engineering

Goal: Transforming Data in DWH to Analytical Views

Instructor: Victoria

  • 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)
  • Google data studio -> Dashboard
  • Extra knowledge:
    • DBT cli (local)

Duration: 1.5-2h

Week 5: Batch processing

Goal:

Instructor: Alexey

  • 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)
  • Extending Orchestration env (airflow) (30 minutes)
    • Big query on airflow (10 mins)
    • Spark on airflow (10 mins)

Duration: 1-1.5h

Week 6: Streaming

Goal:

Instructor: Ankush

  • 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

Prerequisites

To get most out of this course, you should feel comfortable with coding and command line, and know the basics of SQL. Prior experience with Python will be helpful, but you can pick Python relatively fast if you have experience with other programming languages.

Prior experience with data engineering is not required.

Instructors

FAQ

  • Q: I registered, but haven't received a confirmation email. Is it normal? A: Yes, it's normal. It's not automated. But you will receive an email eventually
  • Q: At what time of the day will it happen? A: Office hours will happen 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 :)