Book Image

Real-time Analytics with Storm and Cassandra

By : Shilpi Saxena
Book Image

Real-time Analytics with Storm and Cassandra

By: Shilpi Saxena

Overview of this book

Table of Contents (19 chapters)
Real-time Analytics with Storm and Cassandra
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Distributed computing problems


Let's dive deep and identify some of the problems that require distributed solutions. In the world we live in today, we are so attuned to the power of now and that's the paradigm that generated the need for distributed real-time computing. Sectors such as banking, healthcare, automotive manufacturing, and so on are hubs where real-time computing can either optimize or enhance the solutions.

Real-time business solution for credit or debit card fraud detection

Let's get acquainted with the problem depicted in the following figure; when we make any transaction using plastic money and swipe our debit or credit card for payment, the duration within which the bank has to validate or reject the transaction is less than five seconds. In less than five seconds, data or transaction details have to be encrypted, travel over secure network from servicing back bank to the issuing bank, then at the issuing back bank the entire fuzzy logic for acceptance or decline of the transaction has to be computed, and the result has to travel back over the secure network:

Real-time credit card fraud detection

The challenges such as network latency and delay can be optimized to some extent, but to achieve the preceding featuring transaction in less than 5 seconds, one has to design an application that is able to churn a considerable amount of data and generate results within 1 to 2 seconds.

Aircraft Communications Addressing and Reporting system

The Aircraft Communications Addressing and Reporting system (ACAR) demonstrates another typical use case that cannot be implemented without having a reliable real-time processing system in place. These Aircraft communication systems use satellite communication (SATCOM), and as per the following figure, they gather voice and packet data from all phases of flight in real time and are able to generate analytics and alerts on the data in real time.

Let's take the example from the figure in the preceding case. A flight encounters some real hazardous weather, say, electric Storms on a route, then that information is sent through satellite links and voice or data gateways to the air controller, which in real time detects and raises the alerts to deviate routes for all other flights passing through that area.

Healthcare

Here, let's introduce you to another problem on healthcare.

This is another very important domain where real-time analytics over high volume and velocity data has equipped the healthcare professionals with accurate and exact information in real time to take informed life-saving actions.

The preceding figure depicts the use case where doctors can take informed action to handle the medical situation of the patients. Data is collated from historic patient databases, drug databases, and patient records. Once the data is collected, it is processed, and live statistics and key parameters of the patient are plotted against the same collated data. This data can be used to further generate reports and alerts to aid the health care professionals.

Other applications

There are varieties of other applications where the power of real-time computing can either optimize or help people make informed decisions. It has become a great utility and aid in the following industries:

  • Manufacturing: A real-time defect detection mechanism can help optimize production costs. Generally, in the manufacturing segment QC is performed postproduction and there, due to one similar defect in goods, entire lot is rejected.

  • Transportation industry: Based on real-time traffic and weather data, transport companies can optimize their trade routes and save time and money.

  • Network optimization: Based on real-time network usage alerts, companies can design auto scale up and auto scale down systems for peak and off-peak hours.