A Fine Slice Of SQL Server
Bryan Shalloway has fun with network plots:
In previous posts and threads I’ve alluded to the potential utility of visualizing the relationships between parsed functions/packages and files as a network plot.
I added the function
network_plot()to funspotr. In this post I’ll simply output the network plots of the parsed-out packages from the code collections discussed in the prior two posts:
Click through for interactive plots of what different people in the R community use.
Comments closedZadhid Powell has an example of working with ML.NET:
Machine learning is one of those areas of programming which is very capable of invitations and creativity. But, are you limited to any particular language like Python or R to develop either AI or ML projects? Who says that?
Nowadays, many developers have started learning to program with C#. But, if you’re one of them, you’d probably have heard that C# is not the best choice to start programming or it’s just useful for windows applications which is a wrong belief.
I mean, I’m still going to the mats for F# here but yeah, anybody who says C# is a bad starting language for programmers or that you can only build Windows apps with it lacks sufficient information on the language to make a sound judgment.
Comments closedDani Ljepava announces support for Managed Instance link is now in public preview:
As of today, we are pleased to announce that the link feature for Managed Instance is available in the open public preview, in all Azure regions worldwide. It can be used with existing, or new managed instances, and SQL Server 2019 Enterprise, or Developer edition, including SQL Server 2022 CTP (available through EAP). We have also released the tooling support for the link in the form of automated wizards available in SQL Server Management Studio, starting from SSMS v18.11.1.
With the link, replicated databases from SQL Server on Managed Instance are usable as R/O secondary replicas. While the link is in operation, transactions commited on SQL Server (primary) are instantaneously committed to Managed Instance (secondary). This provides an exact replica of your SQL Server database on Managed Instance, synced near real-time. The link was built to be resilient, in case of the network being down, SQL Server being rebooted, or maintained, or in case of some other issue, the link will automatically resume replicating where it has left off when the issue has been resolved.
Support for 2019 is a shrewd idea, given the SQL Server version adoption curve for companies. This isn’t going to replace having a proper availability group for high availability or even (most) disaster recovery options, though, because the link is currently one-way—though Dani does mention eventual support for bi-directional operation with SQL Server 2022.
Comments closedKenneth Fisher has a use for temporary stored procedures:
The other day I was asked to create a SQL Audit on several different databases. Completely unexpectedly (sarcasm warning!) the list grew, not once, not twice, but enough times that I’ve lost count, and each time I would copy and paste my code for the new databases and change the database name in each piece. Then on one notable occasion I had to change the code for each of the, at that point 10, copies of the code. Talk about a headache.
Then there was the epiphany.
Comments closedReza Rad has a video (and article):
Misconception 1: Power BI is not an enterprise reporting tool, it is only good for self-service.
This is a misconception. And it is there because many people who have heard of Power BI, are not aware of the data modeling engine, the data transformation, and other main components of it. Maybe they just know Power BI as a visualization tool.
Power BI came to the market with the promise of binging data analysis to everyone using extra-ordinary self-service ability using Power BI Desktop and Power BI Service. However, Power BI itself is built on top of Microsoft enterprise data analysis toolset.
Read on for more information about this, as well as four other misconceptions.
Comments closedAmit Khandelwal reminds us that MSDTC exists:
It’s been a while since I’ve had the opportunity to write and share a blog post about SQL Server containers and Linux. Today, I’d like to show you how to set up and use MSDTC (Microsoft Distributed Transaction Coordinator) to execute distributed transactions for SQL Server containers running on a Kubernetes platform.
Please see the following documentation for more information on DTC and SQL Server on Linux. How to configure MSDTC on Linux – SQL Server | Microsoft Docs.
I kid (sort of) but it is good to see as much parity between the Windows and Linux versions of SQL Server as possible.
Comments closedThe Hadoop in Real World team does some stacking:
stack function in Spark takes a number of rows as an argument followed by expressions.
stack(n, expr1, expr2.. exprn)stack function will generate n rows by evaluating the expressions.
Think of it as the Spark alternative to UNPIVOT.
Bhavya Garg explains how we can create dynamic directed acyclic graphs in Apache Airflow:
Airflow dynamic DAGs can save you a ton of time. As you know, Apache Airflow is written in Python, and DAGs are created via Python scripts. That makes it very flexible and powerful (even complex sometimes). By leveraging Python, you can create DAGs dynamically based on variables, connections, a typical pattern, etc. This very nice way of generating DAGs comes at the price of higher complexity and subtle tricky things that you must know
Read on for an example.
Comments closedKevin Jacobs teaches us to learn:
Active Learning is a method in which data is annotated in s smart way. With data annotation, you would normally get to see a randomly selected item which you need to label. This however can lead to a lot of repetition of similar items which you have to label. This is a waste of time. A better way would be to use Active Learning. For Active Learning, a batch of random items is selected first. Then, a lightweight classifier is used for evaluating the previously annotated data.
Basically, run your prediction mechanism, find the things about which the mechanism is least certain, and figure those out. Doing this reduces ambiguity and quickly leads to a better model.
Comments closedGauri Mahajan tries out the Q&A visual:
The speed at which the options for data hosting, data processing and data management keep growing, the options for data consumption have also been growing at the same pace. Traditionally, applications and reports used to be the most common and most frequent means of consuming data. As data consumption means matured with time, chatbots, analytics engines, machine learning and artificial intelligence tools and many others. Traditionally, to explore the data, some of the common mechanisms have been using database query languages, preparation of reports by report designers and data exploration in a self-service manner by power users. With the evolution of capabilities like machine learning, artificial intelligence, natural language processing and others, some of the popular and modern methods of data exploration includes natural language-based data analysis, voice-enabled data exploration using smart devices, computer vision-based data analysis, etc. While many of these methods are highly sophisticated and need user training for a user to employ these data exploration methods, natural language-based data exploration is one of the most popular data exploration methods. This method is offered out-of-box by many reporting tools including Tableau and Power BI as well.
The Q&A visual is a really cool concept which works a surprising amount of the time. The problem is that when it doesn’t work, it feels like pushing a string: no matter what you do, it just doesn’t quite do what you need it to.
Comments closed