How to Take Advantage of Modern Real-Time Analytics

Real-Time Analytics – The ability to quickly act on information to solve problems or create value has long been the goal of many businesses.

However, it was only recently, with the emergence of new technologies, that the speed and scalability requirements of real-time analytics could be addressed both technically and cost-effectively by organizations on a larger scale.

DBTA recently held a roundtable webinar featuring Kevin Petrie, senior director, Attunity; Brian Bulkowski, CTO, Yellowbrick; and Emma McGrattan, SVP of engineering, Actian, who discussed how to succeed with real-time analytics.

Real-time data, combined with historical data, provides the most context for decision making, according to Bulkowski. Building data pipelines with fewer systems and steps leads to greater scalability and reliability.

Real-Time needs the following:

  • Ingest on-the-fly data
    • Natively from apps, Kafka/Spark, ETL tools, high speed loaders
  • Write groundbreaking analytic applications
    • Custom dashboards, reporting
  • Deliver massive capacity
    • With minimal node count
  • Guarantee performance
    • Across thousands of users with reserved resources
  • Provide universal accessibility with ANSI SQL

However, real-time is only part of the picture, Bulkowski explained. It is a small view of the stream compared to the broad view over time. Business value lies in the right amount of history.

To build a real-time future, companies should identify the right capabilities. These include the ability to ingest and provide data loading direct from apps, Kafka/Spark, Change Data Capture from OLTP systems, ETL, YB Load; data store scale and expansion to see the capacity, number of concurrent users, mixed workloads; and make sure data is accessible through interactive applications, ad hoc SQL, business critical reporting.

Real-time analysis of transactional data enables new use cases, according to McGrattan. This includes the abilities to:

This article originally appeared on dbta.com To read the full article, click here.

Nastel Technologies uses machine learning to detect anomalies, behavior and sentiment, accelerate decisions, satisfy customers, innovate continuously.  To answer business-centric questions and provide actionable guidance for decision-makers, Nastel’s AutoPilot® for Analytics fuses:

  • Advanced predictive anomaly detection, Bayesian Classification and other machine learning algorithms
  • Raw information handling and analytics speed
  • End-to-end business transaction tracking that spans technologies, tiers, and organizations
  • Intuitive, easy-to-use data visualizations and dashboards

If you would like to learn more, click here.