Extract actionable business insights from your Big Data

Overview

Effective leveraging of voluminous data growth by uncovering appropriate business insights enables effective decision making and improves business outcomes.

HTC’s big data expertise can help you derive effective business insights for driving competitive business advantage. We enable our clients conceptualize and drive a well thought out big data program across domains and focus areas for enabling them to achieve the key dual objectives – maximizing revenue and improving operational efficiency.

Resources

Apache Kafka

Businesses depend on data. Vast volume of data is generated continuously in the form of click-streams, output from sensors or via sharing on popular social website. Real-time usage of these multiple sets of data collected from production systems imposes a challenge due to the volume of data collected and processed. Apache Kafka is a fast, scalable, and distributed system, which is used to buffer messages between data stream producing systems. It classifies messages into topics, arranges them linearly, and indexes each message. The new business models which are emerging worldwide are data centric and data products driven. Industries must realign their business solutions to meet the dynamic business challenges.

Managing Real-time Data Streaming Effectively with CEP Solutions

Businesses are relying on real-time analytics, alerting, on-line machine learning, and continuous computation to achieve their goals. Real-time streaming analytics enables enterprises to collect, integrate, process, analyze, and visualize data, as and when it is generated.Complex Event Processing (CEP) combines data from multiple sources and creates actionable, situational knowledge from distributed message-based systems, databases, and applications in real-time or near real-time. When complex events are processed and used effectively, they can provide organizations with the capability to define, manage, and predict events, situations, exceptional conditions, opportunities, and threats in complex, heterogeneous networks.

Data Stream Ingestion & Complex Event Processing Systems for Data Driven Decisions

Data ingestion is the process of obtaining, importing, and processing data for later use or storage in a database. It has become a significant challenge for enterprises not only to ingest the relentless feeds of data, but also to capture value from them. Event processing systems are designed to rapidly extract and filter information from massive amounts of high velocity data that are impossible for humans to analyze. They play a vital role in data driven decisions.

Event Processing Systems-the Way to Improved and Quicker Decision Making

Event processing is the method of tracking, analyzing, processing streams of events / information / data about events that happen and deriving a conclusion from them / run-time decision making. New generation applications generate huge volumes of data depending on the type of interactions and sphere of operations. The data generated by these stream generators can be viewed as event streams, where events are the outcome of an interaction. Businesses that can make better and faster decisions in response to their customers or operations stand to gain market share.

Operational Analytics

Big data and predictive analytics are used by business and industries to change and improve the processes and operations majorly. Operational Analytics uses big data technologies for the latest applications to analyze machine data and gain insight, which gives better business results. The data generated by machines collected by the IT systems contain valuable insights. IT Operational Analytics automates the process of collecting and organizing data for locating patterns that help in identifying business results and improving system performance.

Publish Subscribe System

A Publish/Subscribe system maintains a database of subscriptions, where each subscription is a Boolean expression. When an event occurs, the Publish/Subscribe system reports all subscriptions in its database that are matched (or satisfied by the event). Customers who posted these matching subscriptions will be notified.

Publish/Subscribe systems are used in diverse applications with varied performance requirements. Event-based systems are very different from database systems because they enable data dissemination from publishers to subscribers in the present and future.

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