Category: Data

Aaron Bertrand updates an old tip:

Back in 2015, I wrote a tip called Search all string columns in all SQL Server databases. That tip focused on finding strings within string-based columns in all tables across all user databases. I was recently asked if this could be made more flexible; for example, can it search views as well, and can it search only in a specific database?

Click through for a new version which works in SQL Server 2016 and later.

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Tomaz Kastrun tries out several file formats in Azure Data Lake Storage (Gen2):

CSV data format is an old format and very common for data tasks, like import, export or storing. And when it comes performance of creating CSV file, reading and writing CSV files, how does it still stand against some other formats.

We will be looking at benchmarking the CRUD operations with different data formats; from CSV to ORC, Parquet, AVRO and others with the simple Azure data storage operations, like Create, Write, read and transform.

It’s important to remember that Parquet and ORC are intended to solve radically different problems than Avro. Parquet and ORC are columnar datasets intended to aggregate quickly and efficiently, whereas Avro is intended for efficient row storage. CSV is intended for easy-to-work-with row storage.

Then, Tomaz follows up with some R:

we have created Azure blob storage, connected secure connection using Python and started uploading files to blob store from SQL Server. Alongside, we compared the performance of different file types. ORC, AVRO, Parquet, CSV and Feather. Coming to conclusion, CSV is great for its readability, but not suitable (as a file format) for all types of workloads.

We will be doing a similar benchmark with R language. The goal is to see, if CSV file format can be replaced by a file type that better, both in performance and storage.

The Feather file format, by the way, comes from Apache Arrow and works especially well with Python and R. You might not get the same performance benefits in other languages, depending on its library support.

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Ust Oldfield notes the importance of data quality:

We can safely assume this because a lot of organisations do not have data quality at the top of the priority lists. Why might this be the case? Because monitoring data quality and correcting poor quality data is hard. If it was easy, every organisation would have a strategy and method for tracking and improving data quality.

Often, inertia – driven by an overwhelming amount of information to the risks – sets in as it’s difficult to know where to start. So, where do you start?

I’d say there’s an incentive alignment problem with data quality: organizations want it but not enough that they’d trade anything else for it. And agents within the organization consider data quality a chore, so they’re looking for the minimum viable path. Then, for end users, we consider it even more of a chore (or a nuisance). Furthermore, I’m one of those end users who will put in fake data if I can get away with it on the principle that I don’t want you to have my personal information because you’re probably going to sell it or lose it.

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Paul Andrew shares some thoughts on the true meaning of data governance:

… Someone asked me what I thought ‘good’ Data Governance might look like for a given data platform – warehouse or analytics solution. This included all aspects of data governance is was a very broad question.

To add some actual context, not related to Star Wars, this was during the time when Microsoft started talking about version 2 of the Azure Data Catalogue offering and what else could/should be included in a suite of governance tools. Certainly, long before the days of what we now call Azure Purview. It was also a time when GDPR had a lot of focus for the data community as we battled with the principals and technical implications.

Anyway, with a recent fresh perspective on things, I’ve decided to dust off my original notes and attempt to distill the answer of good data governanace into the following areas. What I also find with data governanace that the more I experience in the industry the deeper my perspective on the subject goes.

Paul has put together a lengthy answer on the topic, well worth the read.

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Dennes Torres points out a change to dynamic data masking in Azure SQL DB:

Dynamic data mask is a very interesting security feature allowing us to mask critical fields such as e-mail, phone number, credit card and so on. We can decide what users will be able to see the value of these features or not.

This feature faced many flaws when it was released, but I believe it’s stable now, although It’s not the main security feature you should care about, it can still be very useful.

However, until very recently, this feature was not very useful. If you mask many fields in many different tables, the fields may require different permission levels in order to be unmasked.

I agree that this is definitely not a security feature. But hey, at least it’s a bit more useful than it was before.

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