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)
466 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)
16,147 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
28,014 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)
4,288 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
14,009 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
6,520 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
Overcoming Challenges: Navigating Common Pitfalls in FinOps Adoption
By HeyMo0sh
Working in DevOps, I’ve seen FinOps do amazing things for cloud cost control, but...
Why your data still can’t answer a simple question
Every organization I talk to has the same problem dressed up in different clothes....
T-SQL Tuesday #197 Invitation – An impactful session or two from a conference
By DataOnWheels
I am delighted to host this month’s T-SQL Tuesday invitation. If you are new...
Forums
The day-to-day pressures of a DBA team, and how we can work smarter with automation and AI
Comments posted to this topic are about the item The day-to-day pressures of a...
Daily aggregation of Azure Blob Storage by tier (created/tier-change/deleted)
Hello all, I’m looking for advice on how to derive a daily snapshot table...
SQL 2017 to SQL 2025. Good to Go ?
We need to replace our Windows server running SQL 2017. Any reason not to...
Question of the Day
Using OPENJSON
I have some data in a table that looks like this:
BeerID BeerName brewer beerdescription 1 Becks Interbrew Beck's is a German-style pilsner beer 2 Fat Tire New Belgium Toasty malt, gentle sweetness, flash of fresh hop bitterness. 3 Mac n Jacks Mac & Jack's Brewery This beer erupts with a floral, hoppy taste 4 Alaskan Amber Alaskan Brewing Alaskan Brewing Amber Ale is an "alt" style beer 8 Kirin Kirin Brewing Kirin Ichiban is a Lager-type beerIf I run this, what is returned?
select t1.[key]
from openjson((select t.* FROM Beer AS t for json path)) t1 See possible answers