Data Engineering ²⁶

2026

Stateful Stream Processing: What Your Pipeline Actually Remembers 04-13

The Streaming Stack: Anatomy of a Pipeline That Never Sleeps 04-06

The Midnight Job: Why Batch Processing Has an Expiry Date 03-30

PyFlink II: Windows, Watermarks, and Late Events 03-17

PyFlink I: Architecture, Checkpoints, and Pass-Through Jobs 03-16

Streaming Foundations II: Python Consumers and PostgreSQL 03-14

Streaming Foundations I: Redpanda and Python Producers 03-13

Running Spark in the Cloud: GCS, Standalone Clusters, and Dataproc 03-07

Spark Internals: Clusters, Shuffles, Joins, and RDDs 03-06

Spark SQL: Running SQL Queries on DataFrames 03-05

Getting Started With PySpark: Sessions, DataFrames, and Transformations 03-04

Batch Processing Fundamentals & Introduction to Apache Spark 03-03

From API to Warehouse in Minutes: Building a Dlt Pipeline With AI 02-27

Understanding Dlt: The Modern Python Library for Data Ingestion 02-27

Advanced Bruin Workflows: AI Agents & Cloud Deployment 02-23

Building an End-to-End Data Pipeline With Bruin 02-19

Introduction to Modern Data Platforms & Bruin Basics 02-15

Dbt Deployment: Commands and CI/CD 02-14

Dbt Testing, Documentation, and Packages 02-13

Dbt Models: Subqueries, CTEs, and Macros 02-12

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