Mark Kromer

Build scalable ETL data pipelines in the cloud using Azure Data Factory’s Mapping Data Flows. Each chapter of this book addresses different aspects of an end-to-end data pipeline that includes repeatable design patterns based on best practices using ADF’s code-free data transformation design tools. The book shows data engineers how to take raw business data at cloud scale and turn that data into business value by organizing and transforming the data for use in data science projects and… Show more

Build scalable ETL data pipelines in the cloud using Azure Data Factory’s Mapping Data Flows. Each chapter of this book addresses different aspects of an end-to-end data pipeline that includes repeatable design patterns based on best practices using ADF’s code-free data transformation design tools. The book shows data engineers how to take raw business data at cloud scale and turn that data into business value by organizing and transforming the data for use in data science projects and analytics systems. Show less

Chapter 11: Big Data Analytics in the Cloud

BI Expert perspectives on Data Lakes for experienced DW/BI practitioners

Data is the lifeblood of today’s successful data-driven businesses
and makes the roles of data engineer, data architect,
and BI designer critical to the success of your business. Those
IT roles should focus on developing and maintaining a solid
data foundation so that the BI solutions you produce for your
users will provide reliable, accurate, and scalable data. To be
successful, you must do more than provide a list of features
for your users. You must provide a data… Show more

Data is the lifeblood of today’s successful data-driven businesses
and makes the roles of data engineer, data architect,
and BI designer critical to the success of your business. Those
IT roles should focus on developing and maintaining a solid
data foundation so that the BI solutions you produce for your
users will provide reliable, accurate, and scalable data. To be
successful, you must do more than provide a list of features
for your users. You must provide a data platform that is secure
and can grow with your business.
A common approach among data warehouse professionals
is to start looking at big data as an integral part of your data
warehouse strategy. As you begin your journey into big data
analytics, you may very well find yourself in the same position
that your peers find themselves in: a mix of various RDBMS,
MPP, and big data platforms. In this article, I discuss how I
have seen these pieces work together to enable end users to
enjoy a single experience from their BI tools for data exploration,
business reporting, and analytics.
A data architect must make many choices when evaluating
databases and business intelligence tools. In this article,
I focus on common ways that I see hybrid architectures
becoming prevalent in today’s growing world of big data. I
will discuss massive-scale, on-premises architectures that
include different data technologies, not just one. There are
many cases where your data warehouse growth trajectory can
be satisfied with traditional database platforms such as Oracle
Exadata, SQL Server, Postgres, or another flavor of MPP or SMP
databases. However, for big data analytics, big data warehouse
architectures will be needed. Our goal here is to make massive
amounts of detail data (including real-time events) viewable
as roll-ups and make it available to your business decision
makers, built upon a scalable data platform. Show less

On-going blogging for Microsoft BI

How the Azure Public Cloud PaaS provided the scale, cost structure and time-to-production we needed to bring a simple registration app to market for our event.

Azure Reporting Services - The Killer App of SQL Azure

How Microsoft utilized SQL Server, SharePoint and PerformancePoint Services to deliver POCs to our customers that demonstrate real business value - the key to a successful BI solution.