The term "real time" has been infused throughout tech, from real-time stock picks to real-time pizza tracking. As everyday enterprises begin incorporating data tools and tactics used inside the biggest of big tech companies, a sector of data services providers has emerged to help them take advantage of the truly real-time analytics and machine learning approaches only giant companies with far larger database teams and resources could have afforded in the past. Companies like Hazelcast, Rockset, Tecton and others enable split-second analytics and machine learning for things like financial fraud prevention, dynamic pricing or product recommendations that respond to what you just clicked. These companies promise to leave plodding batch-data processing for old-school business intelligence analysis in the dust. But whether every enterprise needs, wants or is ready to operate at a clip as fast paced as a Citibank, Uber or Amazon remains to be seen. Updating data every few days, every night or even every hour or so for business analysis using a typical batch processing approach "is like playing Monday morning quarterback," said Venkat Venkataramani, CEO and co-founder of Rockset, a company that provides a database for building applications for real-time data, analytics and queries.

"By 2024, 60% of the data used for the development of AI and analytics projects will be synthetically generated." This is a prediction from Gartner that you will find in almost every single article, deck or press release related to synthetic data. We are repeating this quote here despite its ubiquity because it says a lot about the total addressable market of synthetic data. Let's unpack: First, describing synthetic data that is "synthetically generated" may seem tautologic, but it is also quite clear: We are talking about data that is artificial/fake and created, rather than gathered in the real world. Next, there's the core of the prediction -- that synthetic data will be used in the development of most AI and analytics projects.

That being said, riding the wave of deep learning with a unique dataset can be extremely profitable.

Last year's Camp Fire in California was devastating. California's hillsides are still green, thanks to a surplus of rain in the past few months, but the state is already exhorting homeowners to build 100 feet of "defensible space" around their homes, an ominous warning of the coming wildfire season. Cape Analytics, a data startup, wants to do one better, using images from the air and data analytics to identify homes most at risk from a fast-moving wildfire. The Mountain View, California-based company said Wednesday that it is releasing a new product that makes use of its machine learning tools for aerial imagery to assess wildfire risks to people's homes. The primary customer for the product is insurance companies, who can use the tools to assess risk and notify homeowners if that risk can be mitigated.

Big data storage startups have started focusing on more sophisticated solutions rather than the generic way of dealing with data analytics. Lately, new data startups have been emerging and this has significantly boosted the businesses all over the world. New startups that made their inception in the recent years have eventually become the major players in various sectors. Companies like Hortonworks and Cloudera are examples of such feat. General analytics has been widely replaced with vertical solutions.