Big Data has been around for a while and blockchain technology currently rides the hype wave. What results can the concoction of these two innovations produce?
As new Big Data technologies and products hit the markets, more and more exciting opportunities arise. Look at what Big Data has to offer in terms of projects and tools in 2017.
Big Data analytics, machine learning, search engine indexing and many more fields of modern data operations require data crawling and scraping. The point is, they are not the same things!
Sustained growth is the only way to ensure survival of your startup. While you might be willing to work with blood and tears, this might not be enough. Big Data can become the key to victory.
Successful Big Data mining relies on the correct analytical model, choosing the relevant data sources, receiving worthy results and using them to ensure the positive end-users’ experience.
Reading the streams of numbers is possible only in the movies. In the real world, the businesses have to use data visualization tools to get the trends and patterns out of the raw data.
We recently explored the Big Data visualization principles. Now it’s time to delve deep into Big Data visualization techniques and find out which one is appropriate for various use cases.
Big Data visualization is undoubtedly the most essential part of Big Data analytics. Instead of drowning the user in unstructured data, visualization helps provide the actionable insights.
A startup needs a good idea, a great team, a decent funding and an eager audience to succeed and scale. A data-driven startup has all these things covered. Wanna know how?
As any new technology, Big Data gains a lot of attention and gathers around itself quite a ton of myths. We will look through 5 most popular myths of Big Data today — and demystify them!
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