AWS offers a robust suite of tools designed to manage big data effectively. This article explores focusses on how SQL is leveraged within Amazon Redshift and Amazon Athena. We will delve into the technical features, practical applications, and step-by-step examples for using these services, highlighting how they can transform raw data into actionable insights for your business.
Algorithms rule the world and Steve talks about the impact those have on the world.
This next article looks at how a Logic App in Azure can start and stop various database services and save money.
Learn how to get started with Elasticsearch with data in your SQL Server database.
Steve has a few thoughts on how AIs change the role of databases.
Create an SQL Server backup for an Azure Managed Instance that you can download and restore on another SQL Server instance.
In Azure SQL Database serverless edition, we get an upgrade to our configuration.
Freshly minted MCM Wayne Sheffield fills a log shipping crack that could otherwise cause you to re-initialize your log shipping.
The executives at CrowdStrike testify before the US Congress, noting their software development process needs work.
Companies of all sizes and across industries are struggling to cope with an explosion of data never before seen in the short history of computing. As applications reach new levels of sophistication and become deeply interconnected, these companies find themselves increasingly overworked, overheated, and at their wits’ end, desperately trying to squeeze just a bit more performance and availability out of their aging database architectures.
By Patrick
SQL Server Audit is an efficient way to track and log events that occur...
I presented at SQL Saturday Pittshburgh this past weekend about populating your data warehouse...
By Steve Jones
A customer was asking recently about the RPO for their estate, and I showed...
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I have this data in my Customer table:
CustomerID CustomerName 1 Steve 2 Andy 3 Brian 4 Allen 5 DevinI run this code:
SELECT
t.CustomerID
, c.value
FROM
( SELECT
CustomerID
, STRING_AGG (CustomerName, ',') AS me
FROM customer
GROUP BY CustomerID) t
CROSS APPLY STRING_SPLIT(me, ',') c;
What is returned? See possible answers