Introduction In Level 1 of this series, I discussed Synapse Analytics basics and the steps for creation of the Synapse Workspace. In Level 2, data analysis was done on Data Lake files using Serverless SQL Pool. In Level 3, I will analyze data from the files uploaded in Data Lake container using a Spark Pool. I will […]
Introduction In Level 1 of this series, I discussed Synapse Analytics basics and the steps for creating the Synapse Workspace. In Level 2, we analyzed Data Lake files using the Serverless SQL Pool. In Level 3, we analyzed Data Lake files using the Spark Pool. In Levels 4 and level 5, I will discuss the Delta […]
This next article in the Stairway to Synapse looks at Delta tables and processing streaming data.
This next level of the Stairway to Synapse Analysis Services looks at the Dedicated SQL Pool.
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SQLAutomate is a lightweight PowerShell tool that simplifies SQL Server deployment by automating both installation and post-installation configuration. Whether you're setting up one instance or dozens, this guide shows how to use SQLAutomate to save time and reduce manual work.
There are several things that you can do to improve performance by throwing more hardware at the problem, but usually the place you get the most benefit from is when you tune your queries. One common problem that exists is the lack of indexes or incorrect indexes and therefore SQL Server has to process more data to find the records that meet the queries criteria. These issues are known as Index Scans and Table Scans.
In this next article, we are going to explore how to install, configure and use the command line to manage a couple different Fabric Lakehouse’s.
Purging data from a table is a common database maintenance task to prevent it from growing too large or to stay in compliance with data retention. When dealing with small amounts of data, this can be accomplished by a simple delete with no issues; however, with larger tables, this task can be problematic. Deleting records requires a lock that can block other processes from writing or even reading the data (depending on your isolation level). In this article I will share a technique I have used to work with some very large tables.
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I have this data in a table called dbo.NFLTeams
TeamID TeamName City YearEstablished ------ -------- ---- --------------- 1 Cowboys Dallas 1960 2 Eagles Philadelphia 1933 3 Packers Green Bay 1919 4 Chiefs Kansas City 1960 5 49ers San Francisco 1946 6 Broncos Denver 1960 7 Seahawks Seattle 1976 8 Patriots New England 1960If I run this code, how many rows are returned?
SELECT TOP 2
json_objectagg('Team' : TeamName)
FROM dbo.NFLTeams;
See possible answers