Chopping Off Data
An ETL issue with Excel causes issues, because the users didn't realize a limitation.
2026-01-23 (first published: 2020-10-19)
460 reads
ETL (Extract Transform and Load)
Incremental Data loading through ADF using Change Tracking
Learn how you can use Change Tracking to incrementally load data with Azure Data Factory
2022-04-18 (first published: 2020-10-13)
16,115 reads
Access external data from Azure Synapse Analytics using Polybase
Learn how you can use Polybase technology in Azure Synapse to load data into your warehouse.
2020-07-28
27,984 reads
Provisioning ADF - Level 2 of the Stairway to Azure Data Factory
In this next level of the Stairway to Azure Data Factory, learn how to build your first ADF environment.
2020-01-15 (first published: 2019-09-04)
4,286 reads
Data Transfer Strategies between MongoDB and SQL Server
There are a variety of days to move data between MongoDB and SQL Server. This article covers some of your options and gives you ideas on which method might work best for you.
2019-07-22
14,002 reads
Preventing Invalid Data Early In An ETL Flow
In this post, Tim Smith will look at some of the considerations for both data rules and logic to prevent invalid data early in the ETRL process.
2018-05-02
3,324 reads
3 Solutions For Auditing Long SQL Server Extracts or Loads
In this post, Tim Smith looks at the different options you can use to audit your SQL Server extracts and loads during the ETL process.
2017-07-04
3,755 reads
Bash for ETL pre-processing
People were doing ETL long before ETL packages were invented. Some of those facilities still have worth today
2016-08-15
6,449 reads
Simplify SQL Server ETL Processes with T-SQL Stored Procedures
Jeremy Kadlec introduces the Enhanced Data Integration Suite (EDIS), a SQL Server T-SQL stored procedure based solution to automate, manage, and audit ETL processes so you no longer have to manually build SSIS Packages.
2016-02-03
6,164 reads
How to get ETL Horribly Wrong
ETL ( Extract, transform, load) doesn't have to be like a spell on hell. To make a success of ETL systems, you need the freedom and ability to make graceful U-turns when you detect a mistake in architecture or configuration: to fix the root problem rather than to merely tackle the symptoms. Feodor lists the eight most common root causes of failure in ETL systems, and how to fix them.
2015-01-30
13,243 reads
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Is it uncommon for Dev's to have a data model of the source data
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Question of the Day
Creating JSON I
On SQL Server 2025, what happens when I run this code:
SELECT JSON_OBJECTAGG( 'City':'Denver') GOSee possible answers