Have you started your data platform migration yet? If not, you can be certain that your competitors have either already migrated, or will be soon. What are you waiting for? A modern data platform opens a wealth of opportunities for your organization.
Category: Sql Server23 Results
We are beyond excited to announce that Pragmatic Works was awarded as a finalist for Microsoft 2017 Data Platform Partner of the Year! We submitted some of the impressive things our team did this year that are making a huge impact for our customers. Microsoft chooses 3 finalists from over 2800 partners from 115 countries — we are incredibly proud to be one of those finalists this year.
Data platform migration can be a big, scary project for organizations, but at Pragmatic Works we have an incredible reputation of success through our years of experience with data platform and BI work. To make the most of your data migration initiative and make sure it’s successful, there are a few factors you need to focus on.
While the language has been around since 1996, the momentum in the trade press about R has been steadily increasing over the last few years. R is part of the explosion of all things Data Science, which has shown great promise in providing meaning to data through the application of advanced analytics and the ability to visually present data.
A feature of Azure SQL Data Warehouse is the ability to scale (and pay for) compute resources as needed. This is particularly useful when loading data to a data warehouse. ETL operations typically put heavy load on the data warehouse, and could benefit from increased computing power. When the load process completes, however, that extra power may no longer be necessary.
What Dynamic Data Masking Is
As the COO of a company, Tim understands the important role data plays in a business's success. Whether it's annual reports, forecasting sales or making important decisions for the business's future, Tim relies on data - data he knows is accurate because it's been tested and validated.
Pragmatic Works helps customers all over the world with their data-centric challenges. If it involves data, we’ve pretty much done it. One challenge our customers continue to see, and we experience ourselves, is the random and unexpected bad result. These are results that cause our business users and customers to question the validity of the reports we produce for them. Even with Master Data Management, Data Cleansing and products like DQS, companies continue to wrestle with bad data. But why?
We are really excited about our July webinar lineup. Several of the awesome folks at Microsoft have signed up to host some amazing sessions, you might even recognize some of the names as being past Pragmatic Works employees. The topics in July cover SQL Server 2016, Power BI and Azure. So if you ever wanted some behind the scenes information on these topics, check out our July webinars!
One of my favorite features (admittedly, there are quite a few) of SQL Server 2016 is PolyBase. It’s a fantastic piece of technology that allows users to near seamlessly tie relational and non-relational data. This feature has been available for Analytics Platform System (APS) and SQL Data Warehouse (SQL DW) for some time, and fortunately, it has finally made its way to SQL Server, thanks to the SQL Server 2016 release.
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