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)
463 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)
16,130 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
27,999 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)
4,287 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
14,004 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
6,475 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
Blogs
Identity Columns Can’t Be Updated: #SQLNewBlogger
By Steve Jones
I’m not sure I knew identity column values could not be updated. I ran...
Rolling Back a Broken Release
By Steve Jones
We had an interesting discussion about deployments in databases and how you go forward...
A bespoke reporting solution doesn’t have to cost the earth
By ChrisJenkins
You could be tolerating limited reporting because there isn’t an off the shelf solution...
Forums
Can someone please explain what happens?
I have mentioned this several times over several years. Can someone please help me...
SELECT COUNT(DISTINCT) returns null when nothing is found instead of 0
SELECT COUNT(DISTINCT Component) AS Found FROM tblComponents WHERE(Component NOT LIKE '%[a-z]%') AND(LTRIM(RTRIM(Component)) = 'GM13622')...
Remotely Engineer Fabric Lakehouse objects: The Fabric Modern Data Platform
Comments posted to this topic are about the item Remotely Engineer Fabric Lakehouse objects:...
Question of the Day
Creating JSON III
In a SQL Server 2025 table, called Beer, I have this data:
BeerIDBeerName 1Becks 2Fat Tire 3Mac n Jacks 4Alaskan Amber 8KirinI run this code:
SELECT JSON_OBJECTAGG(
BeerID: BeerName )
FROM beer;
What are the results? See possible answers