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Microsoft Fabric Updates Blog

  1. Updates
  2. Sean Mirabile

Fabric Data Pipelines – Advanced Scheduling Techniques (Part 2: Run a Pipeline on a Specific Day)

Author: Kyungchul (Kevin) Lee | LinkedIn Introduction Welcome back to the blog series on Advanced Scheduling techniques in Microsoft Fabric Data pipelines. In our first blog we covered how to replicate event driven scheduling by polling a storage location (Fabric Data Pipelines – Advanced Scheduling Techniques (Part 1) | Microsoft Fabric Blog | Microsoft Fabric). … Continue reading “Fabric Data Pipelines – Advanced Scheduling Techniques (Part 2: Run a Pipeline on a Specific Day)”

A Data Factory Pipeline Navigator Map to a Successful POC

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”

Fabric Data Pipelines – Advanced Scheduling Techniques (Part 1)

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)”

Data Pipeline Performance Improvement Part 3: Gaining more than 50% improvement for Historical Loads

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”

Data Pipeline Performance Improvements Part 2: Creating an Array of JSONs

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”