Data Engineering 101: The Beginner’s Guide

سرفصل های دوره

Master Modern Data Engineering Fundamentals

1. Introduction to data engineering

1. Introduction

2. What is data engineering and why is it important

3. Overall archiecture of end-to-end data pipeline

4. Section 1 - Quiz 1.html

5. Some historical context

6. Data maturity

7. Data engineers place within a data team

8. Section 1 - Quiz 2.html

2. End-to-end data pipeline in-depth

1. Generation (source systems) Intro

2. Generation (source systems) Types of databases

3. Section 2 - Quiz 1.html

4. Generation (source systems) Third-party systems and event streams

5. Storage Intro

6. Storage Serialization (storage file formats), compression, and caching

7. Storage Distributed storage

8. Storage Types of storage systems

9. Storage Row-based vs columnar storage (or OLTP vs OLAP)

10. Storage Data warehouse, data lake, and data lakehouse

11. Section 2 - Quiz 2.html

12. Ingestion Batchmicro-batchstreaming ingestion and ETL vs ELT

13. Ingestion Streaming ingestion

14. Ingestion Scalability and upstream schema changes

15. Ingestion Different ways to ingest data

16. Transformation Queries

17. Transformation Data modeling

18. Transformation Queries and transformations

19. Transformation Events vs states and batch vs streaming

20. Serving

21. Section 2 - Quiz 3.html

3. Undercurrents

1. DataOps

2. Orchestration

3. Security, privacy, data quality

4. Development workflow

5. Section 3 - Quiz 1.html

4. Data pipeline architecture examples

1. What is a good data architecture

2. What factors should you consider when designing your data architecture

3. BI stack (data warehouse) Storage

4. BI stack (data warehouse) DuckDB

5. BI stack (data warehouse) Orchestrator

6. BI stack (data warehouse) Ingestion

7. BI stack (data warehouse) Transformation and visualization

8. BI stack (data lakehouse) Storage - Apache Iceberg

9. BI stack (data lakehouse) Compute engine - Trino

10. Section 4 - Quiz 1.html

11. Streaming stack Event ingestion

12. Streaming stack Event processing

13. ML stack The basics of ML and AI

14. ML stack Feature store

15. ML stack Model training

16. ML stack Model serving (a.k.a. model inference)

17. Deep learning stack Intro

18. Deep learning stack Labeling, training, and inference

19. Other considerations to building a data pipeline

20. Section 4 - Quiz 2.html

5. Whats next

1. Future of data engineering Our opinion

2. Whats next

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تولید کننده: Udemy