Although you can get started with R in SQL Server without understanding data frames, they are a key structure of the R language that are the equivalent of SQL Server table variables. They give you many ways of manipulating and analyzing data and passing it between R and SQL Server. For a database professional, they provide a clear and familiar concept when getting to grips with integrating R into the database.
2017-11-08
3,412 reads
This book holds the key to "encryption without fear". In it, John Magnabosco sweeps away some of the misconceptions surrounding SQL Server's encryption technologies, and demonstrates that, when properly planned and implemented, they are an essential tool in the DBA's fight to safeguard sensitive data.
2017-11-07
3,016 reads
Azure SQL Database’s Dynamic Data Masking (DDM) feature limits the exposure of sensitive information to non-privileged users. DDM can mask either the full value or partial value in a column. This method can also mask Social Security and Credit Card Numbers without making any changes to the application. Read on to learn more.
2017-11-07
2,210 reads
While Name Value Pairs Tables are anathema to Relational Database professionals, it is sometimes useful to consider such a structure in a production application.
This article illustrates the use of User-defined Table Type to improve such handling.
2017-11-06
5,920 reads
Some time ago, Phil Factor wrote his booklet 'SQL Code Smells', collecting together a whole range of SQL Coding practices that could be considered to indicate the need for a review of the code. It was published as 119 code smells, even though there were 120 of them at the time. Phil Factor has continued to collect them and the current state of the art is reflected in this article. There are now around 150 of these smells and SQL Code Guard is committed to cover as many as possible of them.
2017-11-06
8,928 reads
Additional files can be added to to a database to increase the storage. When more data is added to the files, Extended Events shows how SQL Server spreads data across multiple files.
2017-11-03 (first published: 2016-07-25)
4,723 reads
Inside the SQL Server Tacklebox you'll find day-to-day tools and techniques to automate and standardize SQL Server installation, document and report on your servers, migrate data and manage data growth, troubleshoot performance issues, receive notifications of impending issues, secure access to your servers and fight off the data corruption monster.
2017-11-03
7,262 reads
Azure SQL Database provides a number of benefits that leverage resiliency and redundancy built into the underlying cloud infrastructure. In addition to Point-in-Time Restore as well as Geo-Restore and Active Geo-Replication, you have the option of implementing long-term backup retention of Azure SQL Database.
2017-11-03
2,430 reads
This is the book that will de-mystify the process of using Dynamic Management Views to collect the information you need to troubleshoot SQL Server problems. It will highlight the core techniques and "patterns" that you need to master, and will provide a core set of scripts that you can use and adapt for your own requirements.
2017-11-02
10,061 reads
When your application is running slowly, the reflex action is to blame the database queries. It is certainly true that some of the more extravagant delays can be fairly blamed on a missing index or unnecessary locking, but there are other potential villains in the drama, including the network and the application itself. Dan Turner points out that you could save a lot of time and money by working out for sure where the problems lie before diving into the detail.
2017-11-02
7,358 reads
If you've ever loaded a 2 GB CSV into pandas just to run a...
By James Serra
What problem is Fabric Ontology trying to solve? For years, most data conversations have...
By Steve Jones
Recently I ran across some code that used a lot of QUOTENAME() calls. A...
Comments posted to this topic are about the item The New Software Team
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Comments posted to this topic are about the item The string_agg function
We create the following table and then insert some records in it:
create table t1 ( id int primary key, category char(1) not null, product varchar(50) ); insert into t1 values (1, 'A', 'Product 1'), (2, 'A', 'Product 2'), (3, 'A', 'Product 3'), (4, 'B', 'Product 4'), (5, 'B', 'Product 5');What happens if we execute the following query in both Sql Server and PostgreSQL?
select id,
category,
string_agg(product, ';')
over (partition by category order by id
rows between unbounded preceding and unbounded following) as stragg
from t1; See possible answers