Authors: Prashant Atri & Prabhjot Kaur Welcome to Version 1 of the ultimate Data Factory Pipeline Mind Map! This one-page view is packed with guidelines to help you navigate Data Factory pipelines on your Data Factory journey to build a successful Data Integration project. Starting from the left-hand side, you will find broad feature areas … Continue reading “A Data Factory Pipeline Navigator Map to a Successful POC”
Sean Mirabile | Sr. Program Manager | Microsoft Fabric CAT Special thanks to all of the technical reviewers: Kevin Lee, Bret Myers, Sundar Easwaran, and Mallikarjun Appani Content Introduction Blog 1: ADLS Gen2 Event Triggers (on create) Data Pipeline Settings The Get Metadata Activity YouTube Video Complete Data Pipeline JSON Introduction Welcome to the blog … Continue reading “Fabric Data Pipelines – Advanced Scheduling Techniques (Part 1)”
Introduction / Recap Welcome to the final entry of our 3-part series on improving performance for historical data loads! In the first two entries we dove deep into the technical weeds to demonstrate the capabilities of Data Pipeline Expression Language. Part 1: Data Pipeline Performance Improvements Part 1: How to convert a time interval (dd.hh:mm:ss) … Continue reading “Data Pipeline Performance Improvement Part 3: Gaining more than 50% improvement for Historical Loads”
Welcome back to Part 2 of this 3-part series on optimizing Data Pipelines for historical loads. In the first two parts, we are introducing two technical patterns. Then in Part 3, we will bring everything together, covering an end-to-end design pattern. To recap, in Part 1 we covered how to parse a time interval (dd.hh:mm:ss) … Continue reading “Data Pipeline Performance Improvements Part 2: Creating an Array of JSONs”
Series Overview Welcome to this short series where we’ll be discussing the technical methods used to improve Data Pipeline Copy activity performance through parallelization by logically partitioning any source. Often, we see solutions leveraging a single Copy Activity to move large volumes of data. While this works great, you might face a scenario where you … Continue reading “Data Pipeline Performance Improvements Part 1: How to convert a time interval (dd.hh:mm:ss) into seconds”
