By migrating the customer from Azure Data Lake Storage Gen1 to Azure Data Lake Storage Gen2, 3Cloud, formerly Pragmatic Works, helped them see a 40% savings in storage per month, totaling over $28,000 monthly.
We’re excited to share that, once again, Microsoft is a leader of Analytics and Business Intelligence (ABI) platforms in the 2020 Gartner Magic Quadrant – this is the 13th straight year that Microsoft has been recognized. In this Magic Quadrant report the analytics and BI platforms available on the market are compared based on their features, usability, and capabilities; and Power BI was a top leader.
If you have been in the BI industry as long as I have, then you know of Donald Farmer. He happens to be one of my heroes as a leader in the business intelligence and analytics field for the past two decades.
Many people ask the question, which is right for my company, on-premise or cloud modern data warehouse? In a recent webinar, David Hay, teaches you what you should look at to make the best decision for your organization between cloud vs on-premise modern data warehouse, as well as what the implementation process entails to stay within time and budget.
I have been on a long term Azure for BI implementation and took some time today for a “what went right”, “what went wrong” and “what I’ll do differently” introspection. Giving the outcome a bit of a positive twist, I’ll try not to repeat what I’ve already shared in Data Architecture for BI Programs and Transitioning from Traditional to Azure Data Architectures. I actually thought I’d write about parameterized shared ADF datasets, or using Python to auto generate transform views and load store procedures. Instead, it appears that my fingers have reverted back to tool agnostic critical success points. (At least I know what I want to write about next!) My tool agnostic thoughts:
Go Get Your First Customer!
Here is a movie quote for you: “Build it and they will come”. This thought process should be sent straight to Hades. Data architecture and analytics is not a Field of Dreams. Among other things, having an actual customer will result in a finished product that at least somebody wants. The best advertisement is word of mouth, and it is easier to sell a product that has at least one happy customer. It amazes me to see how many companies make this mistake. If you do not have one already, go get your first customer and put them on your design team. Now, how easy was that?!
Associate Each Data Architectural Decision with a Business Value
In my most recent project, my colleague, Joshuha Owen, and I put together a slide deck affiliating business value with data architectural decisions. There were several slides that looked similar to this for business users–>
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Data Governance – a hot topic on everyone’s minds. As I’m working on a BI project in the financial district in New York, it has me thinking about data governance and how it’s something we struggle with in the BI world.
Disclaimer: I create a different diagram for every engagement, so think of this as a suggestion, not a mold.
Unless you’ve been living under a rock, you’ve heard of Power BI. In case you don’t know, Power BI is a business analytics solution from Microsoft that lets you visualize your data and share insights across your organization or embed them in your app or website. You can connect to hundreds of data sources and bring your data to life with live dashboards and reports.
Summary of the Matter: If you only read one thing, please read this: transitioning to Azure is absolutely “doable”, but do not let anyone sell you “lift and shift”. Azure data architecture is a new way of thinking. Decide to think differently.
First Determine Added Value: Below are snippets from a slide deck I shared during Pragmatic Work’s 2018 Azure Data Week. (You can still sign up for the minimal cost of $29 and watch all 40 recorded sessions, just click here.) However, before we begin, let’s have a little chat. Why in the world would anyone take on an Azure migration if their on-prem SQL database(s) and SSIS packages are humming along with optimum efficiency? The first five reasons given below are my personal favorites.
Each of the reasons given above are a minimum one hour working session on their own, but I’m sharing my thoughts in brief in an effort to help you to get started compiling our own list. Please also look at the following diagram (Figure 1) and note two things: a.) the coinciding “traditional” components and b.) the value add boxed in red.
What do you know about consumption-based architecture? This refined approach to data analytics has been implemented with some of our clients to help them meet their organizational needs. In a recent webinar, BI Platform Architecture, Development and Admin, Michael French shows how to utilize this architecture for your business.