A Fine Slice Of SQL Server
Kevin Chant points out a change to Azure Data Studio:
I talked about the using the wizard in the Insiders build to deploy Big Data Cluster in that post. You can read that post in detail here.
Now, as well as deploying Big Data Clusters you can also use the wizard to deploy SQL Server container images locally using Docker.
Read on for notes about the process.
Comments closedRob Sewell inverts the “Use Azure Data Studio to create Powershell notebooks” mantra:
This module contains only 3 commands at present
* Convert-ADSPowerShellForMarkdown
This will create the markdown link for embedding PowerShell code in a Text Cell for a SQL Notebook as described in this blog post
* New-ADSWorkBookCell
This command will create a workbook text cell or a code cell for adding to the New-ADSWorkBook command
* New-ADSWorkBook
This will create a new SQL Notebook using the cell objects created by New-ADSWorkBookCell
Click through for an example.
Comments closedAaron Nelson announces a new feature in Azure Data Studio:
In order to get all the nice intellisense and tab completion features of the PowerShell language inside your PowerShell Notebooks, be sure to install the PowerShell extension from the Azure Data Studio marketplace.
At this point, the biggest remaining language is R, though I’d love to see F# support as well (hey, Azure Notebooks offers F# support).
Comments closedDave Bland takes us through some of the formatting options available in Azure Data Studio notebooks:
When working in a Notebook you have two types of cells, text and code. The focus of this post is how to format the text cell. Of course text goes into this cell so that part is easy and of course the text can say anything you would like to say. When we work with text in Word, there is a format tool bar that we can use to make it look like we want it. The text cells do not have this toolbar.
You might be asking, without the format toolbar, does that mean we can’t format the text? That answer is no….we can still format the text, we just need to do it slightly different. Rather than use a toolbar, we need to use characters.
There’s a lot of power in Markdown.
Comments closedAlan Yu announces the newest release of Azure Data Studio:
With the November release of Azure Data Studio, we have added three key features:
– PowerShell kernel support
– Collapsible code cells
– Output toolbar for save output actions
There are plenty of other additions as well. Adding Powershell kernel support is interesting…but I still want F#.
Comments closedJeremy Smith, et al, announce a new product:
We are pleased to announce the open-source launch of Polynote: a new, polyglot notebook with first-class Scala support, Apache Spark integration, multi-language interoperability including Scala, Python, and SQL, as-you-type autocomplete, and more.
Polynote provides data scientists and machine learning researchers with a notebook environment that allows them the freedom to seamlessly integrate our JVM-based ML platform — which makes heavy use of Scala — with the Python ecosystem’s popular machine learning and visualization libraries. It has seen substantial adoption among Netflix’s personalization and recommendation teams, and it is now being integrated with the rest of our research platform.
There are some nice pieces to it, especially around language interop.
Comments closedRicardo Portilla shares a demo of financial time series analysis in Databricks:
We’ve shown a merging technique above, so now let’s focus on a standard aggregation, namely Volume-Weighted Average Price (VWAP), which is the average price weighted by volume. This metric is an indicator of the trend and value of the security throughout the day. The
vwapfunction within our wrapper class (in the attached notebook) shows where the VWAP falls above or below the trading price of the security. In particular, we can now identify the window during which the VWAP (in orange) falls below the trade price, showing that the stock is overbought.
Click through for the article, as well as a notebook you can try out.
Comments closedAaron Nelson has two separate ways of scheduling Azure Data Studio notebooks for us:
There are two new options for automating your SQL Notebooks with your SQL Servers. Earlier this month, the Insiders build of Azure Data Studio received the ability to add SQL Notebooks in SQL Agent. This past Friday (September 20th, 2019) a new version of the SqlServer PowerShell module was posted to the Gallery, with a new Invoke-SqlNotebook cmdlet.
Read on for demos of both.
Comments closedI wrote a blog post back in November 2018, about how to install and deploy SQL Server 2019 Big Data Cluster on Azure Kubernetes Service. Back then SQL Server 2019 Big Data Cluster was in private preview, (CTP 2.1 I believe), and you had to sign up, to get access to the “bits”. Well, you did not really get any “bits”; what you did get was access to Python deployment scripts.
Now, September 2019, the BDC is in public preview (you do not have to sign up), and it has reached Release Candidate (RC) status, RC 1. The install method has changed, or rather, in addition to installing via deployment scripts, you can now also install using Azure Data Studio deployment notebooks, and that is what this blog post is about.
Having gone through this myself, there’s quite a bit of reading involved in the setup, but they make the process pretty smooth. This also shows off one of the key benefits of notebooks: documentation and code together.
Comments closedJenny Jiang announces a new capability in Visual Studio Code:
With the Visual Studio Code extension, you can enjoy native Python programming experiences such as linting, debugging support, language service, and so on. You can run current line, run selected lines of code, or run all for your PY file. You can import and export a .ipynb notebook and perform a notebook like query including Run Cell, Run Above, or Run Below. You can also enjoy a notebook like interactive experience that includes your source code and markdown comments along with the running results and output. You can remove the unneeded sections, enter comments, or type additional code in the interactive results window. Moreover, you can visualize your results in a graphic format through a matplotlib like Jupyter Notebook. The integration with SQL Server 2019 Big Data Clusters empowers you to quickly submit a PySpark batch job to the big data cluster and monitor job progress.
This is rather useful for developers, though I greatly prefer the Azure Data Studio notebook interface.
Comments closed