Data Quality: Transactions, Ingestions, and Storage

linkedin.com/learning/data-quality-transactions-ingestions-and-storage

1,725 learners | 49m | Advanced | Released Nov 2025 | 4.7/5 stars

Hands-on course covering the essentials of data collection, transactions, ingestion, and storage. Learn data ingestion techniques using PostgreSQL, manage database transactions and concurrency, and implement the Write-Audit-Publish (WAP) pattern — all in a GitHub Codespaces sandbox environment.

What You'll Learn

  • Data ingestion techniques using PostgreSQL
  • Database transaction management and concurrency issues
  • Data replication and the Write-Audit-Publish (WAP) pattern
  • Identifying and resolving common data quality problems at the source

Course Sections

  1. Data Ingestion — Initial ingestion, handling dirty data, CHECK constraints
  2. Transactions — Transaction pitfalls, the lost update problem, isolation
  3. Replication — Data lakehouses, WAP pattern implementation
  4. Final Project — Writing a product requirement document for data quality

Integrated with GitHub Codespaces — no local setup required.

1 # Data Quality: Transactions, Ingestions, and Storage
2
3 [linkedin.com/learning/data-quality-transactions-ingestions-and-storage](https://www.linkedin.com/learning/data-quality-transactions-ingestions-and-storage)
4
5 **1,725 learners** | 49m | Advanced | Released Nov 2025 | 4.7/5 stars
6
7 ---
8
9 Hands-on course covering the essentials of data collection, transactions, ingestion, and storage. Learn data ingestion techniques using PostgreSQL, manage database transactions and concurrency, and implement the Write-Audit-Publish (WAP) pattern — all in a GitHub Codespaces sandbox environment.
10
11 ## What You'll Learn
12
13 - Data ingestion techniques using PostgreSQL
14 - Database transaction management and concurrency issues
15 - Data replication and the Write-Audit-Publish (WAP) pattern
16 - Identifying and resolving common data quality problems at the source
17
18 ## Course Sections
19
20 1. **Data Ingestion** — Initial ingestion, handling dirty data, CHECK constraints
21 2. **Transactions** — Transaction pitfalls, the lost update problem, isolation
22 3. **Replication** — Data lakehouses, WAP pattern implementation
23 4. **Final Project** — Writing a product requirement document for data quality
24
25 ---
26
27 *Integrated with GitHub Codespaces — no local setup required.*

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