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Come See Jonathan Mugan Speak

Blacklight Solutions is proud to be a part of the technology community here in Austin and we are excited to spotlight our new leadership positions in the community, upcoming Data Science events, and two community-focused fundraising campaigns. The Association of Computing Machinery is the largest professional organization specifically for computer scientists in the world. The

By | 2019-04-11T20:36:23+00:00 April 11th, 2019|Uncategorized|

Feeling Good about Game of Thrones, Finally

Our community work is part of our DNA, and we challenge ourselves all the time to find time in our schedules to contribute to the community.  Whether it is becoming one of the biggest fundraisers for Austin Sunshine Camps  or through our community education work with the Association for Computing Machinery (ACM) we continue to

By | 2019-04-05T22:03:46+00:00 April 5th, 2019|Uncategorized|

Accelerate Your Time to Market

From Services Provider to Software Vendor Your service is successful.  You are fulfilling the needs of your customers.  Your reputation is healthy, and now you are looking to expand more.  The most basic expansion strategy is asking what else you can do for your existing customers to strengthen your relationship. Offering a software product is

By | 2019-04-05T15:05:23+00:00 April 4th, 2019|Uncategorized|

3 Reasons Faster Achieves More in Analytics

Interested in a counter-intuitive statistic? In one survey successful business intelligence project leaders reported getting value from their implementations more than twice as fast as those that failed to meet their objectives.  A natural tension usually exists between speed and impact.  When one is prioritized in technology, too often the other suffers.  As indicated in

By | 2018-04-04T11:40:51+00:00 April 4th, 2018|Data Practices|

Simple Authentication

When Blacklight evaluated technologies for the partners in our platform, the cloud provider was a major piece of the decision. We needed a partner that was robust, provides virtually infinite scale, clear pricing and most of all the breadth of tools our customers most need. While other providers offered great integration for a specific stack,

By | 2017-12-20T14:37:50+00:00 December 20th, 2017|Data Practices, Embedded BI, Engineering, Products|

Illuminations NewsCard Q4 2017

Amazon certainly knows how to go big at a conference.  With nearly 50,000 attendees AWS re:Invent was spread across Las Vegas hotels and included fully booked sessions from end to end.    Over 1000 sessions were held and venues spanned the MGM, Aria, Venetian, LINQ and Mirage elevating conference logistics and unfortunately wait

By | 2017-12-11T19:28:14+00:00 December 11th, 2017|Uncategorized|

Observations from AWS re:Invent

Amazon certainly knows how to go big at a conference.  With nearly 50,000 attendees AWS re:Invent was spread across Las Vegas hotels and included fully booked sessions from end to end.    Over 1000 sessions were held and venues spanned the MGM, Aria, Venetian, LINQ and Mirage elevating conference logistics and unfortunately wait times to

Testing Automation in Embedded BI

Congratulations, you’ve made the choice to embed a BI solution into your product line. You have thought through the implications of building it yourself and have decided you want to focus your team on what makes you money, your specific domain. Unless you sell a BI solution, you don’t want to keep up with the

By | 2017-11-19T13:37:42+00:00 November 19th, 2017|Embedded BI|

Strategic Partnerships

We are excited to have recently become Amazon Web Services (AWS) and Hortonworks consulting partners. In addition to our partnerships with KNIME and Yellowfin. Since the beginning of this company we have been striving to do more for our clients and faster.  New capabilities emerge for our clients with this combination of technologies, a combination

By | 2017-11-14T11:43:48+00:00 November 14th, 2017|Uncategorized|

Observations from KNIME Summit 2017

Any analytics project today requires data integration, cleansing, manipulation and often they require machine learning.  In order to solve these problems a hodge podge of tooling is typically required.  That usually leads to glueing technologies together that were never meant to really work with each other in the same place.   The problem is exacerbated