A Fine Slice Of SQL Server
Chen Hirsh explains a concept:
A Streaming Table in Databricks is a table that receives data… well, in a stream.
And no, this has nothing to do with Netflix.
Imagine a conveyor belt carrying packages into a warehouse. A robot picks up each package and places it on the shelves.
Read on for the metaphor, as well as thoughts on when streaming tables make sense.
Leave a CommentErik Darling does some digging:
While looking at a client’s server, I noticed there was, at any given time, 2-3 background sessions with the
commandcolumn sayingBRKR TASK, which I found quite odd.Only the Microsoft-shipped broker queues were present, and none were activated. Nothing uses Service Broker, Mirroring, or Log Shipping.
Read on for Erik’s findings. If you need a Misfits song to listen to, we are close enough to the end of October for Halloween to work.
Leave a CommentChris Webb goes looking for unused objects:
A few weeks ago I wrote about how Semantic Link Labs now has tools with interactive UIs and showed how you could use this to view lineage and so Vertipaq-Analyzer-stuff in a notebook. I didn’t show what I think is the coolest new feature though: the ability to find the tables, columns and measures in a semantic model that aren’t used and which therefore could be deleted. There are tons of excellent third-party tools that do this available already of course but the advantage of using Semantic Link Labs for this is that you can automate the process of finding unused columns and run it from a notebook and, crucially, you have two ways of finding those unused columns: from analysing the structure of downstream reports and from analysing DAX queries captured in Workspace Monitoring.
Read on to see how it works.
Leave a CommentGreg Low has started a series on entity-attribute-value tables. The first post covers what they are:
If you’ve been working with databases for any length of time, you will have come across implementations of Entity-Attribute-Value (EAV) tables (or non-tables as some of my friends would call them).
Instead of storing details of an entity as a standard relational table, rows are stored for each attribute.
The second post covers pros and cons:
In an earlier post , I discussed the design of EAV (Entity Attribute Value) tables, and looked at why they get used. I’d like to spend a few moments now looking at the pros and cons of these designs.
Greg is very much against EAV, and I agree with this. I do like Greg’s alternative of using something like JSON, with the proviso that the database simply become a whole-record storage and retrieval engine rather than trying to strip out and splice in new JSON via T-SQL. Otherwise, spend the time on proper data modeling and take advantage of what the platform can do for you.
Leave a CommentAndreas Wolter shares some thoughts:
CISA recently published guidance on using cyber decoys, tripwires, breadcrumbs, and honeytokens to detect attackers who are already operating inside an environment: Using Cyber Decoys to Strengthen Detection and Response
Looking at this through my SQL Server lens, I think it is worth considering how these concepts can be applied to database systems.
Before building any kind of database honeypot, you need to ask yourself:
Click through for that list of questions, as well as additional thoughts from Andreas.
Leave a Comment