A Fine Slice Of SQL Server
Use this to monitor something that’s not already exposed as a performance counter. Like the progress of a custom task or whatever. If you can write a quick query, you can expose it to a counter that can be plotted by Performance Monitor.
This might track queue length, number of items processed, or whatever other time series measure you need but don’t have a UI to display.
Comments closedI don’t know how many of you are working with Windows Core these days but personally I think it’s a pretty cool concept. You aren’t supposed to be logging into your servers all that often so why have the extra overhead of Windows? Windows Core removes all of that and comes back with something that looks and feels remarkably like DOS from when I started with computers. That said, most of us, myself included, aren’t used to just having DOS commands any more so here are some helpful tips. I should point out that there is actually more than just DOS commands available and I’ll go over a bit of that here as well.
One other command I find quite helpful is sconfig, which pops up the Server Configuration app. That’s how you can, for example install Windows updates if you don’t have WSUS.
Earlier this week I received a question from a customer on how to get Power BI to work with data in ADLSg2 that is secured using ACLs. I didn’t know the answer, but I knew who would know, and I looped in Ben Sack from the dataflows team.Ben answered the customer’s questions and unblocked their efforts, and he said that I could turn them into a blog post. Thank you, Ben!
Read on for the answer.
Comments closedDan Guzman takes us through different SET options in T-SQL and where you can go wrong:
ANSI_PADDING OFF has also been deprecated for quite some time and the SQL Server documentation specifically calls out “ANSI_PADDING should always be set to on.” In summary, a column-level ANSI_PADDING OFF setting causes nullable fixed-length char(n) and binary(n) columns to behave like variable-length varchar(n) and varbinary(n) columns. Furthermore, SQL Server automatically trims trailing blank characters from character data and leading binary zeros from binary data and stores the values as variable length instead of storing the provided value as-is during inserts and updates. Varchar(n)/varbinary(n) columns with ANSI_PADDING OFF are similarly trimmed. Note that it is the persisted ANSI_NULLS column meta-data setting that determines the storage and trimming behavior, not the current session ANSI_PADDING setting. The session ANSI_PADDING must still be ON when using features that require proper settings.
Some of these will pop up in occasional errors, like if you’re using filtered indexes or indexed views.
Comments closedDinesh Chandrasekhar announces support for Apache Flink in Cloudera Streaming Analytics:
We cannot hold our excitement anymore! For the last few months, our Data-in-Motion engineering teams have been working hard to deliver a compelling and critical part of our Cloudera DataFlow (CDF) story. To enhance our Stream Processing and Analytics narrative within the overall Data-in-Motion platform, we give you support for Apache Flink with the general availability of Cloudera Streaming Analytics (CSA).
Cloudera Streaming Analytics, powered by Apache Flink, is a new product offering within the Cloudera DataFlow (CDF) platform that provides real-time stateful processing of IoT-scale data streams and complex events for predictive insights. Cloudera DataFlow (as seen in the picture below) is a comprehensive edge-to-cloud real-time streaming data platform. As one of the key pillars of CDF, stream processing & analytics is important for processing millions of data points and complex events coming from various streaming sources. Over the years, we have supported several streaming engines but the addition of Flink now makes CDF an extremely compelling platform for processing high-volumes of streaming data at high-scale.
This is adding support for Flink; it looks like Spark Streaming and Kafka Streams are also supported, though they are pushing Flink as a first option rather than one among equals.
Comments closedA general guide to interpreting the multiple correspondence analysis plot shown above for business insights would be to make a note as to how close input categorical features are to the target variable customer churn and to each other. For instance, senior citizens, customers with fiber optic internet service, those with month to month contractual agreements, and single customers or customers with no dependents are being related to a short tenure with the company and a propensity of high risk to churn. On the other hand, customers with more than a year contract, those with DSL internet service, younger customers, customers with multiple lines are being related to a long tenure with the company and a higher tendency to stay with company.
Read the whole thing.
Comments closedLeila Etaati takes us through a text classification problem:
Text classification is one of the important tasks for the aim of classifying the texts based on the allocated tags.
In the previous blog, the process of how to create Text classification in the Power Apps using AI builder has been explained,In this Blog Post, you will see how to use the created Text classification model in the Power Automate (Microsoft Flow).
Read on for the demo.
Comments closedErin Stellato gives us a moment of zen:
A question I’ve gotten a few times when teaching relates to database compatibility level and Query Store. I was talking to a client yesterday about post-upgrade plans and implementing Query Store, and the topic came again. They wanted to know what compatibility level the database needed in order to use Query Store.
The quick answer: it doesn’t matter.
Read on for a demonstration.
Comments closedDave Mason wants to remove quoted identifiers from a flat file:
I haven’t shown all the columns, but you get the idea–every column in the result set has data enclosed in double quotes. That’s exactly how it appears in the source data file.
Dave has a method which works for plenty of versions of SQL Server. If you’re using 2017 or later, the FIELDQUOTE parameter was added to solve this problem, though to be fair, I haven’t actually tried it to see if it works as expected.
However often I go on about
CHECKconstraints, there will always be a developer who will leave them out or mutter in a dignified manner about how all checks need to be done only at the application level. This attitude soon gets divine retribution. Bad data springs up like a rotting fungus over your database unless you addCHECKconstraints to all your tables. This is fine but then how do you prevent the excellent and estimable habit of adding them to then interfere with a release? The constraints will stop the build if they meet bad data: it is what they are trained to do. If you don’t like that, then you must fix the bad data first.
Click through for the process.
Comments closed