This week’s topics
Data Ingestion and Pipeline Architecture
- ETL and ELT workflows
- Silo extraction and data warehouse staging
- Feature engineering and transformation pipelines
- What is Data Integration? Unlocking AI with ETL, Streaming & Observability
- Feature Engineering in ML
- Machine Learning Pipeline
- AI and Machine Learning Data Sources
- Querying and Exporting Relational Data (SQL)
What you will be able to do
- 2.1 Evaluate analytical model data preparation processes.
Activity
Start with this week’s 5 videos, then: Seminar, guided reading and a short written task: evaluate analytical model data preparation processes.
Study materials
Videos
- What is Data Integration? Unlocking AI with ETL, Streaming & Observability · YouTube · IBM Technology · 7 min · for registered students
- Feature Engineering in ML · YouTube · Neso Academy · 18 min · for registered students
- Machine Learning Pipeline · YouTube · Neso Academy · 17 min · for registered students
- AI and Machine Learning Data Sources · YouTube · TU Delft · 20 min · for registered students
- Querying and Exporting Relational Data (SQL) · YouTube · TU Delft · 19 min · for registered students
- Stream All Things - Patterns of Modern Data Integration • Gwen Shapira • GOTO 2017 · YouTube · GOTO Conferences · 49 min · for registered students
- Kafka Connect: Build & Run Data Pipelines • Kate Stanley, Mickael Maison & Danica Fine · YouTube · GOTO Conferences · 50 min · for registered students
- Observability for Data Pipelines: Monitoring, Alerting & Tracing Lineage • Jiaqi Liu • GOTO 2020 · YouTube · GOTO Conferences · 19 min · for registered students
Further resources
- Foundations of Information Systems, ch. 8: Data Analytics and Modeling · PDF · OpenStax, textbook chapter; pp. 297–338 · for registered students
Materials marked “for registered students” open in the student portal, which is set up as soon as you receive final acceptance.
