SSMS 18.2 Available

Dinakar Nethi announces SQL Server Management Studio version 18.2:

We are excited to announce the release of SQL Server Management Studio (SSMS) 18.2. For this update, while we added some features, our focus was dedicated to fundamentals such as stability, reliability, performance, etc.

You can download SQL Server Management Studio 18.2 today.
Some of the new features in SQL Server Management Studio include:

– Intellisense/editor: Added support for data classification
– Query execution: Added a completion time in the messages to track when a given query completed its execution.
– ShowPlan: Added new attribute in query plan when the inline scalar UDF feature is enabled.

There are several bugfixes in there as well.

A Primer on Jenkins

Kevin Feasel

2019-07-26

DevOps

Shubham Dangare gives us a quick walkthrough of setting up a Jenkins pipeline:

Jenkins Pipeline (or simply “Pipeline”) is a suite of plugins which supports implementing and integrating continuous delivery pipelines into Jenkins.

A continuous delivery pipeline is an automated expression of your process for getting the software from version control right through to your users and customers.

It provides an extensible set of tools for modeling simple-to-complex delivery pipelines “as code”. The definition of a Pipeline is typically written into a text file (called a Jenkinsfile ) which in turn is checked into a project’s source control repository

Click through for a demo.

Defining Intent Locks

David Fowler explains what an intent lock is and why it’s useful:

Let’s just imagine a World without intent locks for a moment. In that World, a user has just decided to select a row from our database. SQL at that point is going to put down a shared lock against the row.

Now what’s going to happen when another user decides to modify a bunch of rows? Now because of the number or rows involved in this modification, SQL is going to want to take out an exclusive page lock. Where’s the issue here?

Read on to learn what the issue is.

Deleting in Batches

Kevin Feasel

2019-07-26

T-SQL

Jana Sattainathan gives us a few options to delete data in a way which avoids major log growth:

Today, I had to perform a DELETE that happened to remove quite a bit of data. This caused two issues for me

1. Log file growth which fills up the Transaction Log of the DB
2. TempDB was getting full

To avoid both problems I deleted in chunks.

Click through for two ways of doing it.

DAX Variables in Iterators

Kevin Feasel

2019-07-26

DAX

Kasper de Jonge explains how you can use a variable in the middle of an iterator:

As explained in the blog post the SUMX in this calculation will iterate over each row in the fact table which probably will have multiple currencies with different values for each date. The Min(FactExchangeRate[Factor]) will be evaluated for each currency and date and get the right value.

Now for those of you who have seen any of my sessions will ask why am I not using a variable as I always tell everyone to do so.

Click through for the answer and an example of where you can use a variable within an iterator.

Suspect Pages in msdb

Max Vernon explains what the suspect_pages table is in msdb:

When SQL Server detects corruption in a database, it reports that corruption immediately to the client who requested the data. But did you know SQL Server also stores the details about which pages have experienced corruption in the msdb database, in the suspect_pages table?

Read on to see the information you can get from this table, including a listing of what each event type means.

Testing ETL Pipelines

Ed Elliott has started a new series on testing ETL pipelines:

We test in production, this means we have monitoring and do things like have phased roll-outs using feature flags, or we roll-out to select customers first, prove it then roll it out to everyone else. Testing in production doesn’t mean hacking around getting some process to work. We don’t test “on production” (hacking), we test “in production” – while we are in production we are continually testing, and if anything goes wrong, we have alerts and can deal with it.

Testing pipelines feels difficult because there are so many moving pieces, but if you design for testability (e.g., being able to tee off samples of data, send test records through, etc.), things get easier.

Don’t Truncate Facts and Dimensions when Loading Data

Meagan Longoria explains why a truncate-and-reload strategy for data warehouses isn’t a good look:

Every once in a while, I come across a data warehouse where the data load uses a full truncate and reload pattern to populate a fact or dimension. While it may not be the end of the world for a small table, it does concern me and I usually recommend to redesign the load. My thoughts below on why this is an anti-pattern are true for using the actual TRUNCATE TABLE statement as well as executing a DELETE statement with no WHERE clause.

Read on for some great advice, including an exception to the rule.

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