As I have mentioned in prior blog posts, I have been writing a data architecture book, which I started last November. The title of the book is “Deciphering Data Architectures: Choosing Between a Modern Data Warehouse, Data Fabric, Data Lakehouse, … Continue reading →
When I’m leading a full-day architecture design session (ADS) with a customer and the goal is to come up with a data architecture for them, I first start doing “discovery” with them for at least a couple of hours so … Continue reading →
(NOTE: I have returned to Microsoft and am working as a Solution Architect in Microsoft Industry Solutions, formally known as Microsoft Consulting Services (MCS), where I help customers build solutions on Azure. Contact your Microsoft account executive for more info. … Continue reading →
Tomorrow (Tuesday (8/10/21) I will be on a podcast for SaxonGlobal called “The Alphabet Soup of Data Architectures” where I will talk about the modern data warehouse, data fabric, data lakehouse, data mesh, and more. I hope you can check … Continue reading →
One of the biggest differences between the Data Mesh and other data platform architectures is a data mesh is a highly decentralized distributed data architecture as opposed to a centralized monolithic data architecture based on a data warehouse and/or a … Continue reading →
Another buzzword that you may have been hearing a lot about lately is Data Fabric. In short, a data fabric is a single environment consisting of a unified architecture with services and technologies running on it that architecture that helps … Continue reading →
I created a short YouTube video (20 minutes) that is a whiteboarding session that describes the five stages (ingest, store, transform, model, visualize) that make up a modern data warehouse (MDW) and the Azure products that you can use for … Continue reading →
An extension to the Modern Data Warehouse (MDW) that I have heard a bit about lately is called “Reverse ETL”. Before I describe what that is, first I wanted to give a quick review of a typical MDW, which consists … Continue reading →
The two latest trends in emerging data platform architectures are the Data Lakehouse (the subject of my last blog Data Lakehouse defined), and the Data Mesh, the subject of this blog. Data Mesh was first introduced by ThoughtWorks via the … Continue reading →
Below are the top 15 questions I am seeing from customers looking to build a modern data warehouse in the cloud, and the blogs that I have wrote that try to answer each question (I have updated most of these … Continue reading →
A challenge I have with customers who want to get hands-on experience with the Azure products that are found in a modern data warehouse architecture is finding a workshop that covers many of those products. To the rescue is a … Continue reading →
I have heard some people say if you have a data warehouse, there is no need for cubes (when I say “cubes” I am referring to tabular and multidimensional OLAP models). And I have heard others say if you have OLAP … Continue reading →
As part of the Secrets of Data Analytics Leaders by the Eckerson Group, I did a 30-minute podcast with Wayne Eckerson where I discussed myths of modern data management. Some of the myths discussed include ‘all you need is a data lake’, ‘the … Continue reading →
As a follow-up to my blog Is the traditional data warehouse dead?, I did a webinar on that very topic for the Agile Big Data Processing Summit. The deck is here and the webinar is below:
Continue reading →As a follow-up to my blog Is the traditional data warehouse dead?, I will be doing a webinar on that very topic tomorrow (March 27th) at 11am EST for the Agile Big Data Processing Summit that I hope you can join. Details … Continue reading →
The Azure Data Architecture Guide has just been released! Check it out: http://aka.ms/ADAG Think of it as a menu or syllabus for data professionals. What service should you use, why, and when would you use it. I had a small involvement … Continue reading →
There have been a number of enhancements to Hadoop recently when it comes to fast interactive querying with such products as Hive LLAP and Spark SQL which are being used over slower interactive querying options such as Tez/Yarn and batch … Continue reading →
Data virtualization goes by a lot of different names: logical data warehouse, data federation, virtual database, and decentralized data warehouse. Data virtualization allows you to integrate data from various sources, keeping the data in-place, so that you can generate reports … Continue reading →
I had previously blogged about the Data Warehouse Fast Track for SQL Server 2016, a joint effort between Microsoft and its hardware partners to deliver validated, pre-configured solutions that reduce the complexity of implementing a data warehouse on SQL Server … Continue reading →
With so many product options to choose from for building a big data solution in the cloud, such as SQL Data Warehouse (SQL DW), Azure Analysis Services (AAS), SQL Database (SQL DB), and Azure Data Lake (ADL), there are various combinations … Continue reading →
