A Fine Slice Of SQL Server
Rajendra Gupta builds out an Azure Synapse Analytics workspace:
In the article, An Overview of the Azure Synapse Analytics, we explored the Azure Synapse workspace and its features as an analytics service combining Big data analytics and enterprise data warehousing.
This article is a practical demonstration of deploying Azure Synapse Analytics workspace using the Azure portal.
Click through for step-by-step instructions on how to do it.
Comments closedBen Brauer takes us through the next step:
Congratulations! You’ve finished your Well-Architected Review of a workload, giving you a better understanding of where it could be fortified along the five pillars: Security, Reliability, Operational Excellence, Performance Efficiency and Cost Optimization. You have received Microsoft’s best practices as recommendations based on your answers to questions specific to each pillar.
The report (example below) shows a Well-Architected score for each pillar, as well as prioritized recommendations that allows for you to focus on biggest areas of impact. A great example is virtual machine right sizing. You can significantly lower your costs if you know which VM is best suited for your workload type.
By the way, if you have Azure resources, I highly recommend checking out the Well-Architected Framework assessment link there. It can take a very long time to go through because of just how many questions there are; that said, the results are also pretty specific and can be immediately helpful.
Comments closedKevin Chant saves some templates:
Previously I wrote a post about how to do version control for SQL Server Management Studio templates using Azure Repos. I wanted to highlight some things I did not point out in that post. In addition, I thought it was only fair that I showed how to do it with GitHub.
Plus, in my last T-SQL Tuesday post I mentioned the SQL Server diagnostic queries provided by Glenn Berry. Which reminded me to do this post. Because I want to do an example based on sharing one of the queries with your colleagues via GitHub. Like in the below diagram.
Click through to see the process.
Comments closedJoey D’Antoni is not impressed:
While some services include other really useful features (for example the query data collected by the Azure SQL Database and Managed Instance platforms), I wanted to focus on the common value adds to PaaS systems across providers. I made the last two of these bold, because I feel like they are are the most important, especially in scenarios where the vendor doesn’t own the source to the applications. Like Amazon RDS for SQL Server.
Click through for Joey’s thoughts on the topic.
Comments closedJeffrey Hicks has been working with color lately:
Let’s continue looking at how to use PowerShell and a Windows Presentation Foundation (WPF) form to display [System.Drawing.Color] values. This article builds on an earlier post so if you missed it, take a few minutes to get caught up. As I did earlier, before running any WPF code in PowerShell, you should load the required type assemblies.
This has been a fun series to watch.
Comments closedAndy Leonard heard it on the grape vine:
My first response was – and I quote – “WOO HOO!” It’s good to see SSIS getting some love.
A couple years ago, someone claimed SSIS was dying. I first checked it out. Then I blogged about it in a post titled SSIS is Not Dead (Or Dying). It’s been a couple years and SSIS is not dead. One could say SSIS functionality being added to Azure Synapse, arguably Azure’s flagship offering, appears to be the opposite of dying.
I’m not sure I’m as sanguine as Andy is about the future of SSIS but I will say at the very least I agree that it’s not going anywhere anytime soon.
Comments closedLouise Han has an announcement:
Today, we are excited to announce a wonderful collaborated feature between Multivariate Anomaly Detector and SynapseML , which joined together to provide a solution for developers and customers to do multivariate anomaly detection in Synapse. This new capability allows you to detect anomalies quickly and easily in very large datasets and databases, perfectly lighting up scenarios like equipment predictive maintenance. For those who is not familiar with predictive maintenance, it is a technique that uses data analysis tools and techniques to detect anomalies in the operation and possible defects in equipment and processes so customers can fix them before they result in failure. Therefore, this new capability will benefit customers who have a huge number of sensor data within hundreds of pieces of equipment, to do equipment monitor, anomaly detection, and even root cause analysis.
Click through for more details and a demonstration on how to use it.
Comments closedThe Finnstats folks talk about regression:
Basically, Regression analysis involves creating an equation to describe the significant association between one or more predictors and response variables, as well as estimating current observations.
The results of the regression reveal the direction, size, and analytical significance of the relationship between predictor and response, where the dependent variable is either numerical or discrete.
Click through for details on six types of regression. H/T R-Bloggers.
Comments closedTsuyoshi Matsuzaki takes us through the architecture for Project Bansai:
Project Bonsai is a reinforcement learning framework for machine teaching in Microsoft Azure.
In generic reinforcement learning (RL), data scientists will combine tools and utilities (such like, Gym, RLlib, Ray, etc) which can be easily customized with familiar Python code and ML/AI frameworks, such as, TensorFlow or PyTorch.
But, in engineering tasks with machine teaching for autonomous systems or intelligent controls, data scientists will not always explore and tune attributes for AI. In successful practices, the professionals for operations or engineering (non-AI specialists) will tune attributes for some specific control systems (simulations) to train in machine teaching, and data scientists will assist in cases where the problem requires advanced solutions.
Read on to see how it works.
Comments closedAndy Leonard builds out an Azure Integration Runtime:
Many Azure Data Factory developers recommend creating an Azure Integration Runtime for use with Mapping Data Flows. Why? One reason is you cannot configure all the options in the default AutoResolveIntegrationRuntime supplied when an Azure Data Factory instance is provisioned.
At the time of this writing, it’s not obvious how one creates an Azure Integration Runtime. You would think creating an integration runtime would begin with:
It turns out to be a little trickier than you might first expect.
Comments closed