T-SQL Tuesday #108 – Non SQL Server Technologies

T-SQL Tuesday #108 – Non SQL Server Technologies

It’s T-SQL Tuesday #108. This month’s topic is hosted by Malathi Mahadevan (B|T) and she asks us to…

“pick one thing you want to learn that is not SQL Server. Write down ways and means to learn it and add it as another skill to your resume. If you are already learning it or know it – explain how you got there and how it has helped you. Your experience may help many others looking for guidance on this.”

PASS Summit in Seattle took place last week and we know that SQL Server, not only new 2019 version, covers more areas then only SQL Engine. I love SQL Server and I love playing with data. But quite recently I adore playing with massive data. Big data. That’s why I’m more focusing on the Microsoft tools described below. Also, I’m really happy that gradually I realize my plan of learning established last year. Although, I haven’t used R language, Data Science and Machine Learning as I wished – I do it with other points on my list. And to tell the truth, I think that the original plan was too wide.

Power BI

I’m big fan of Power BI and I follow most news that appear in this area. Unfortunately, my role as a Data Engineer is not too much related to the tool. But whenever I can use Power BI to create quick and interactive report(s) – I’m using that opportunity.

Azure SQL DataWarehouse

Hmm… It still has got “SQL” in name, but how much is SQL in that SQL? Hard to say as Azure SQL DW is not an ordinary SQL. It’s Massively Parallel Processing (MPP) architecture and that means you need to change your way of thinking and designing solutions in it. At the beginning of year 2018 I started working with SQL DW in Azure, doing some experimentation and, by the way, I prepared entire session about the topic. Quite recently I joined to private preview SSDT program where I’m able to test this tool for Azure SQL DW.
How to start playing with Azure SQL DW? Do begin reading documentation and then try to do some practice exercise. Be careful. This is expensive tool and fee is charged per hour upfront. Drop your database once you finish experimenting.

Azure Data Factory v2 & Databricks

Also, at the beginning of the year I started working with ADFv2. Version 2 is a way way better than v1. And I’m talking not only about UX. You can build very efficient ETL/ELT processes using a lot of available connections for source and sinks (destination). One of the components can run Databricks’ notebook or JAR which giving you possibilities to extend the process and build business logic using Scala or Python.
But this is not the end of the story. New feature is quickly coming: Data Flow. If you remember Data Flow from SSIS – this is a counterpart. You can design all required transformation, calculation & aggregation without coding. And everything will be run by using Azure Databricks under the hood. I will be posting about this feature on my blog soon.
If you want to know more – read this documentation and ask ADF team for access to Data Flow in preview: adfdataflowext@microsoft.com

How to learn?

These days, there are tons of sources and materials. The only problem remains is… time.
Let me mention you some of the ways how to learn:
– Read Microsoft documentation: Power BI, Azure SQL DW, Azure Data Factory v2 Data Flow, Azure Databricks.
– Find local Azure/SQL/Power BI local user group – they always happy to welcome new members and share knowledge
– Learn from courses, e.g. edX.org
– Go on conference. Don’t you have budget? Join to free one like SQL Saturday.
– Stay updated. You can start with our Last Week Reading series.

This was my point of view and what I was learning during past 10 months. What is yours? Let me know – leave a comment.

Thanks for reading!

Previous Last week reading (2018-11-11)
Next ASF 016 video invitation

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Kamil Nowinski
Kamil Nowinski 84 posts

Blogger, speaker. Data Platform MVP, MCSE. Senior Data Engineer & data geek. Member of Data Community Poland, co-organizer of SQLDay, Happy husband & father.

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