Discover and discuss topics related to modern technology.
Learn about efficient strategies to handle growing data volumes and complexity in your ETL processes. We'll cover parallel processing, distributed systems, and performance tuning techniques.
A deep dive into the features, pricing, and use cases of two leading cloud-based ETL services. Which one is right for your project?
How to build robust ETL pipelines that prepare and transform raw data into features suitable for machine learning models. Includes examples with Python libraries.
Discussing the best tools and strategies for scheduling, monitoring, and managing complex ETL workflows. Apache Airflow, Prefect, and Dagster insights.
Strategies for designing ETL processes that can gracefully handle errors, retry operations, and provide clear logging for debugging.
Exploring the challenges and solutions for building ETL pipelines that process data in near real-time using technologies like Kafka and Spark Streaming.