Press "Enter" to skip to content

Day: January 15, 2021

The Benefit of Multiple Visuals

Elizabeth Ricks continues a series on data visualization techniques:

Data can be visualized many different ways, creating an often daunting task to select the best chart to use. We also sometimes complicate our audience’s experience, packing too much information into a single graph, in the hopes that it will answer every possible question. Instead of making the Swiss Army Knife of charts, we might instead create different views of the same data, examine what each iteration allows a viewer to see, and then decide which version(s) to share with our audience. 

Read on for more.

Comments closed

Including Database Name in Extended Events

Aaron Bertrand has a change of heart:

In my previous tip about SQL Server Extended Events, I discussed the trade-offs involved with choosing to store the database name instead of looking up the name via database_id later. At the time, we decided to use the latter approach since we are generally not concerned about the edge case where DDL is performed against a database that is then dropped before collection occurs.

Something else that has since come up: we want to filter out activity against a set of databases matching a specific name pattern since the DDL activity there is system-generated and does not need to be audited. We quickly learned that you have to be careful with how you use the database name in an Extended Events predicate.

Click through to understand the problem as well as the solution.

Comments closed

Saving Money on Backups to Azure Blob Storage

John McCormack has a few tips for saving some cash:

You have 5 databases on a SQL Server Instance. You take daily full backups of each database on your instance. You also take log backups every 15 minutes as each database is in full recovery mode. This means in 1 week, you will have 35 full backups and 3,360 transaction log backups. This multiplies to 1,820 full and 174,720 t-log backups over 52 weeks. Multiply this for 7 years or more and the costs can get very expensive.

Click through to see how you can save a considerable amount with a bit of planning.

Comments closed

Change Tracking with Temporal Tables

Erik Darling continues a line of thought:

Temporal Tables are different, because it will add new, and drop removed columns from the history table as they change in the base table. It’ll also push data type changes across. But the history table will not have the various permutations tracked. It just mirrors current state.

The one thing I do want to mention is that the column Erik calls JunkDate isn’t junk at all in its proper context—it’s the row end date for a Kimball-style dimension. I get that it isn’t useful in the “type 1” table, but it’s quite useful in the “type 2” table and the sorts of queries temporal tables were expected to solve use the row start date and the row end date together to give you an idea of whether the version of the row you’re seeing is the current version or not.

Comments closed

Elasticsearch and SSPL

Vicky Brasseur looks at an announcement:

In a play to convert users of their open source projects into paying customers, today Elastic announced that they are changing the license of both Elasticsearch and Kibana from the open source Apache v2 license to Server Side Public License (SSPL). If your organisation uses the open source versions of either Elasticsearch or Kibana in its products or projects, it is now at risk of being forced to release its intellectual property under terms dictated by another.

Click through to understand the details. I’d imagine that if Elastic goes through with this, people would fork the last pre-SSPL version of their product sets and create a community spin-off, similar to MariaDB spinning off from MySQL.

Comments closed

Calculations in Power BI Streaming Datasets

Reza Rad has a workaround for us:

If you use a streaming dataset in Power BI, you cannot download the Power BI file, and you cannot open it using Power BI Desktop. This means that you are limited not to use calculations in a streaming dataset. However, there is a small trick which you can use and can be helpful. I will show you that in this article and video.

Click through for the article, which includes the video.

Comments closed