Industries today are making informed decisions based on real-time data and trend analysis in multiple sectors

Operational BI for collaborative analytics using notebooks

The world of BI has long relied on rollups, nightly summaries, canned queries and reports on historical data to provide insights to users. As data volumes grow, the need to collaboratively define data analytics has led to the growth of notebook style visualization aids like Jupyter, Apache Zeppelin etc.

The fundamental idea here is that notebooks can work directly with live data sources (like Spark/SnappyData) and allow users to define adhoc queries, the results of which can be visualized in the notebook. Furthermore, the output from these notebooks can be easily shared for building out collaborative analytics in real time, making Operational BI a much more dynamic capability within the enterprise. From retail to manufacturing to finance, operational BI improves business agility and decision making by making real time information available to decision makers at the right time

Discover more about Operational BI

Analytic Platform as a service in financial services

Discover more about APaaS in Finance

Providing advanced analytics capabilities as a service for multiple workloads across multiple lines of business. These would include post trade analytics to help with trade visibility, where the IT teams have to support brokerage functions (customer service, regulatory reporting, surveillance, advisory, prospecting) that need to interrogate deep histories of customer transactions across all assets and businesses.

Other use cases in this space include tick analytics where data from real time feeds are streamed into SnappyData and the stream provides analytics (stream analytics and deep analytics) on windowed streams, which are then returned to the user. Market surveillance is another use case that is supported by APaaS. The primary goal of market surveillance is to detect patterns that point to insider trading and market manipulation by insiders. These use cases require more concurrency than is what is provided by traditional analytics systems. Data volumes in APaaS use cases will typically be in the 2-10TB range.

IoT Analytics for predictive maintenance

Predictive maintenance is the killer IoT big data application for large manufacturing powerhouses. Companies ranging from GE to Siemens to Hitachi all incur significant costs caused by unscheduled machinery maintenance. Most of these devices (which includes construction equipment, medical equipment, aircraft engines, windmill farms etc.) are now equipped with sensors which produce large streams of data which are sent to the cloud for both stream analytics that allow immediate actions to be taken as well as deep analytics on historical data to improve asset performance and help with predictive maintenance

Discover more about Predictive Maintenance

Exploratory analytics for data scientists in the Ad Tech world

Discover more about Exploratory Analytics

The ability to understand ad performance in real time, the ability to control the velocity of ad placement across various networks, ability to target ads more effectively and avoiding over serving or under serving ads in a target market are all significant challenges for the ad tech industry which employs an army of data scientists to pore over historical and near real time data to improve the overall effectiveness of the ad platform.

As more and more people go online and become targets for the online ad industry, the ability to effectively target users and improve click through rates is of prime importance to ad tech companies

Telco analytics for real time location based services & maintenance

As cell phone usage skyrockets around the world and Telco network coverage becomes a commoditized feature for most consumers, Telco companies are racing to mine insights from cell phone towers to help improve quality of service, improve customer experience, reduce congestion and use predictive maintenance to avoid dead cell towers.

To do this, they have to capture statistics such as dropped calls, detect dead zones in coverage, identify and proactively deal with handset issues from inspecting network data and optimize the network for better delivery of voice and data.

Discover more about Telco Analytics

The Apache Spark Database

SnappyData is Spark 2.x compatible and open source. Download now