This is my second attempt to articulate lessons learned from a recent global Azure implementation for data and analytics. My first blog post became project management centered. You can find project management tips here. While writing it, it became apparent to me that the tool stack isn’t the most important thing. I’m pretty hard-core Microsoft, but in the end, success was determined by how well we coached and trained the team — not what field we played on.
Turning my attention in this blog post to technical success points, please allow me to start off by saying that with over twenty countries dropping data into a shared Azure Data Lake, my use of “global” (above) is no exaggeration. I am truly not making this all up by compiling theories from Microsoft Docs. Second, the most frequent question people ask me is “what tools did you use?”, because migrating from on-prem, Microsoft SSIS or Informatica to the cloud can feel like jumping off the high dive at the community pool for the first time. Consequently, I’m going to provide the tool stack list right out of the gate. You can find a supporting diagram for this data architecture here.
Microsoft Azure Tool Stack for Data & Analytics
Hold on, your eyes have already skipped to the list, but before you make an “every man for himself” move and bolt out of the Microsoft community pool, please read my migration blog post. There are options! For example, I just stood up an Azure for Data & Analytics solution that has no logic apps, Azure function, event hub, blob storage, databricks, HDI, or data lake. The solution is not event-driven and takes an ELT (extract, load, and then transform) approach. It reads from sources via Azure Data Factory and writes to an Azure Database logging the ELT activities in an Azure Database as well. Now, how simple is that? Kindly, you don’t have to build the Taj MaSolution to be successful. You do have to fully understand your customer’s reporting and analysis requirements, and who will be maintaining the solution on a long-term basis.
If you still wish to swan dive into the pool, here’s the list!
A common discussion we’ve had lately is about using Azure Databricks within Azure Data Factory for ETL.
In today’s post I’d like to review some information about using ORC, Parquet and Avro files in Azure Data Lake, in particular when we’re extracting data with Azure Data Factory and loading it to files in Data Lake.
In his Azure Data Week session, Modern Data Warehouse Design Patterns, Bob Rubocki gave an overview of modern cloud-based data warehousing and data flow patterns based on Azure technologies including Azure Data Factory, Azure Logic Apps, Azure Data Lake Store, and Azure SQL DB. 
In today’s post I’ll look at some considerations for choosing to use Azure Blob Storage or Azure Data Lake Store when processing data to be loaded into a data warehouse. My basis here is a reference architecture that Microsoft published, see diagram below.
Are you running a hybrid environment between on-premises and Azure? Do you want to be? In a recent webinar, Sr. Principal Architect, Chris Seferlis, answered the question: How can my organization begin using hybrid cloud today? In this webinar, he defines the four key pillars of true hybrid development, identity, security, data platform and development, and shows actionable resources to help get you started down the hybrid road.