A data pipeline is a series of actions that combine data from multiple sources for analysis or visualization.
A recent survey from Wakefield Research finds that when enterprises build their own data pipelines, decision-making and revenue suffer.
When you build a data warehouse, the important question is how to ingest data from the source system to the data warehouse. If the table is small you can fully reload a table on a regular basis, however, if the table is large a common technique is to perform incremental table updates. This post demonstrates how you can enhance incremental pipeline performance when you ingest data into BigQuery.
At Airflow Summit 2021, Unravel’s co-founder and CTO, Shivnath Babu and Hari Nyer, Senior Software Engineer, delivered a talk titled Lessons Learned while Migrating Data Pipelines from Enterprise Schedulers to Airflow. This story, along with the slides and videos included in it, comes from the presentation.