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Monetizing Core Big Data Technology: Real-time Analytics

  • September 2015
  • -
  • Frost & Sullivan
  • -
  • 29 pages

This report asserts that real-time analytics is not “settled science.” It is not a foregone conclusion that every company needs and can afford real-time analytics in all areas of the business. There is also not, at this time, one shining example for all to follow in deploying it; but, instead, several models. Therefore, this report explores these and other issues and challenges surrounding real-time analytics; cooperative industry efforts that are addressing many of the challenges it poses; major providers who are deploying it; and the benefits that are accruing to their customers.

Executive Summary
This Stratecast report is designed to speak to the needs of these roles and teams:
- Big Data, IT, and Data Science teams
- Product and Sales & Marketing teams, including the CMO
- The C-Suite
- All other Line of Business (LOB) stakeholders

The report’s main conclusions and key takeaways are as follows:
1. Companies are beginning to get a handle on Big Data, accessing all types of data from all relevant sources; but the pace of business, and the pace of life itself, now demands that they equip people with real-time insights.
2. Real-time analytics is arguably the hottest area of a hot market: Big Data and analytics (BDA), which Stratecast assessed at more than $ billion in 2014. Stratecast forecasts it to grow at a CAGR of % through 2019.1

3. Companies in every vertical may be clamoring for it, but real-time analytics is not “settled science.” Not every organization needs real-time insights in every area of its business. In fact, the best way to look at the state of data in most companies today is that there are now three “data speeds”: real-time analytics, which provides insights in hundreds of milliseconds; nearreal-time analytics, which does the same in from hundreds of milliseconds to 10 seconds; and batch analytics, which covers anything longer than that. Companies not only may not need real-time insights everywhere, but focusing on real-time at the expense of trending data
and ongoing patterns would be a mistake for most.
4. Deploying real-time and near-real-time analytics places new demands on companies, including new capital expenditures (capex) and operating expenditures (opex); and creates organizational challenges as well. However, the Apache Software Foundation—which is an active and effective incubator for the most important developments in Big Data and realtime analytics—is also answering at least part of the cost question. Its open source software has lowered the barriers to entry for organizations that wish to deploy real-time analytics, as well as for the vendors that wish to help them do so.
5. That said, the range of technologies and solutions emanating from the ASF may also be causing market confusion, or at least hesitation. If the ASF can coalesce around one preferred architecture for real-time analytics, deployment will accelerate.
6. Those who have already deployed real-time analytics are reaping a whirlwind of benefits, radically improving their businesses today and positioning themselves for the future.

Table Of Contents

Monetizing Core Big Data Technology: Real-time Analytics
Table of Contents
Executive Summary .... 5
Introduction.. 6
The Case for Real-time Analytics ... 7
Banking and Financial Services 7
Healthcare 8
Mobile Apps and Gaming . 8
Travel and Hospitality .. 8
A Balanced View of Data Speeds and Strategic Value.... 11
Real-time Analytics Is Not Essential for Every Need—and Not Always Possible .. 11
Speed is Important, but Strategic Value is Imperative 11
Real-time Analytics: How It's Made .12
Apache Software Foundation (ASF): Open Source Real-time Analytics Accelerator.... 13
Key ASF Innovations Accelerating the Growth of Real-time Analytics 15
Micro-batching: Apache Storm Trident and Apache Spark Streaming ..15
Stream processing engine that can handle batch processing: Apache Flink ....15
Unified stream and batch processing: Apache Apex...16
In-memory data fabric for real-time insights from massive datasets: Apache Ignite ....16
Ability to run a large enterprise on a single data cluster: Apache Kafka 17
Real-time Analytics: Organizational Challenges ..19
Real-time Analytics is Earning Mixed Reviews in the Market.20
Case Study Snapshots of Real-time Analytics in Action .20
Providers of Real-time Analytics Solutions .24
The Last Word.28

List of Exhibits
Exhibit 1: Specific Impacts of Real-time Analytics on Various Verticals ... 8
Exhibit 2: Impacts of Real-time Analytics on Functions that Impact All Industries ... 10
Exhibit 3: Apache Software Foundation (ASF) Solutions Supporting Real-time Analytics ... 14
Exhibit 4: Ignite Speeds Analytic Insights with Parallel Processing of Data and Queries .... 16
Exhibit 5: Kafka and Hadoop Combine to Deliver “All-speed Analytics” .... 18
Exhibit 6: Selected Customer Results from Deploying Real-time Analytics. 21
Exhibit 7: Major Providers of Real-time Analytics Solutions .... 24

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