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Automate UI, load and performance testing for web, desktop and mobile
Rapidly develop, manage and deploy business apps, delivered as SaaS in the cloud
Automate decision processes with a no-code business rules engine
Build mobile apps for iOS, Android and Windows Phone
Deploy automated machine learning to accurately predict machine failures with technology optimized for Industrial IoT.
Optimize data integration with high-performance connectivity
Connect to any cloud or on-premise data source using a standard interface
Build engaging multi-channel web and digital experiences with intuitive web content management
Data lakes take advantage of data storage techniques for massively scalable, low-cost storage of data files in any format.
In our last blog, we talked about our data-dependent universe and how companies and people that make use of all this data do well. Look at Amazon. Amazon.com, a huge online retailer, started out as a bookstore. We buy everything from Amazon.com, right? Airbnb took in over 10 million bookings for lodging last year, and they don’t have a single storefront. There's also Hulu, Apple, and more. But the businesses that don’t embrace these new technologies and new customer engagement models, they bite the dust. Borders, Blockbuster—a few years ago, there was one in every mall, now they’re gone. Kodak, an American giant, went bankrupt.
Back to tech-savvy companies. What many of them are starting to do is create data lakes. Basically, with a data lake, you have all these streams of information flowing in so the data is stored in one location. However, if you look a little more closely, below the surface, you will see that these streams are coming in from many different repositories.
You might have a Mongo data stream or a SQL Server data stream, and Hadoop is part of this, too. On one side you have all these data scientists. They’re your fishermen, the guys analyzing the bits and pieces, trying to find the next bit of information to pull out of the lake as they look for relevant facts that’ll help you run your business. You have data generalists and programmers who tap into the streams for real-time analytics, or write your everyday code or use your BI tools to pull information that supports decision-making. Then on the other side, specific data is pulled and treated before going into your data warehouse—the place where you keep all of your clean stuff that you don’t want to mix with anything else. This is one of the big data processes we see companies pursuing in order to pull and work with the most valuable data for business.
Fastest data access in the industry
The drivers my team and I build here at Progress® DataDirect® are designed to be the fastest in the industry, and we stand behind that claim with award-winning technical support. When you find performance degradation issues in our software, we treat them as defects because we understand how important speed is to your business.
You can pick up a free trial today and try it out for yourself, or watch a replay of our webinar, “Industry Insight: Optimizing Your Data for Better Performance.” Don’t forget to check back for the next installment of this series!
My good friend and colleague Jesse Davis has moved on to an exciting opportunity outside of Progress DataDirect so I’ll be finishing up this series. I’ve been a part of the DataDirect organization for over 22 years supporting and developing our industry leading products as well as leading the teams that build them. Stay tuned for more from me in this series!
Mike is a proven leader with over 20 years of experience in developing commercial software for the industry leader in standards-based data access software. He has extensive experience in all aspects of commercial software development including requirements analysis, developing functional requirements, developing and mentoring individuals, staffing, budgeting, product development, quality assurance, training and customer communication. Mike has progressed in his career in large part from his strong work ethic and a “do whatever it takes” attitude.
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