8 big trends in big data analytics
“The reality is that the tools are still emerging, and the promise of the Hadoop platform is not at the level it needs to be for business to rely on it,” says Loconzolo. But the disciplines of big data and analytics are evolving so quickly that businesses need to wade in or risk being left behind. “In the past, emerging technologies might have taken years to mature,” he says. “Now people iterate and drive solutions in a matter of months — or weeks.” So what are the top emerging technologies and trends that should be on your watch list — or in your test lab? Computerworld asked IT leaders, consultants and industry analysts to weigh in. Here’s their list.
1. Big data analytics in the cloud
Hadoop, a framework and set of tools for processing very large data sets, was originally designed to work on clusters of physical machines. That has changed. “Now an increasing number of technologies are available for processing data in the cloud,” says Brian Hopkins, an analyst at Forrester Research. Examples include Amazon’s Redshift hosted BI data warehouse, Google’s BigQuery data analytics service, IBM’s Bluemix cloud platform and Amazon’s Kinesis data processing service. “The future state of big data will be a hybrid of on-premises and cloud,” he says.
Smarter Remarketer, a provider of SaaS-based retail analytics, segmentation and marketing services, recently moved from an in-house Hadoop and MongoDB database infrastructure to the Amazon Redshift, a cloud-based data warehouse. This might still leave you with the question, “what is Amazon Redshift and how can it benefit my business,” which is a great start in trying to find the best solution for an efficient cloud server. The Indianapolis-based company collects online and brick-and-mortar retail sales and customer demographic data, as well as real-time behavioral data and then analyzes that information to help retailers create targeted messaging to elicit a desired response on the part of shoppers, in some cases in real time.
2. Hadoop: The new enterprise data operating system
Distributed analytic frameworks, such as MapReduce, are evolving into distributed resource managers that are gradually turning Hadoop into a general-purpose data operating system, says Hopkins. With these systems, he says, “you can perform many different data manipulations and analytics operations by plugging them into Hadoop as the distributed file storage system.”
What does this mean for the enterprise? As SQL, MapReduce, in-memory, stream processing, graph analytics and other types of workloads are able to run on Hadoop with adequate performance, more businesses will use Hadoop as an enterprise data hub. “The ability to run many different kinds of [queries and data operations] against data in Hadoop will make it a low-cost, general-purpose place to put data that you want to be able to analyze,” Hopkins says.
3. Big data lakes
Traditional database theory dictates that you design the data set before entering any data. A data lake, also called an enterprise data lake or enterprise data hub, turns that model on its head, says Chris Curran, principal and chief technologist in PricewaterhouseCoopers’ U.S. advisory practice. “It says we’ll take these data sources and dump them all into a big Hadoop repository, and we won’t try to design a data model beforehand,” he says. Instead, it provides tools for people to analyze the data, along with a high-level definition of what data exists in the lake. “People build the views into the data as they go along. It’s a very incremental, organic model for building a large-scale database,” Curran says. On the downside, the people who use it must be highly skilled.
4. More predictive analytics
With big data, analysts have not only more data to work with, but also the processing power to handle large numbers of records with many attributes, Hopkins says. Traditional machine learning uses statistical analysis based on a sample of a total data set. “You now have the ability to do very large numbers of records and very large numbers of attributes per record” and that increases predictability, he says.
5. SQL on Hadoop: Faster, better
6. More, better NoSQL
Alternatives to traditional SQL-based relational databases, called NoSQL (short for “Not Only SQL”) databases, are rapidly gaining popularity as tools for use in specific kinds of analytic applications, and that momentum will continue to grow, says Curran. He estimates that there are 15 to 20 open-source NoSQL databases out there, each with its own specialization. For example, a NoSQL product with graph database capability, such as ArangoDB, offers a faster, more direct way to analyze the network of relationships between customers or salespeople than does a relational database.
7. Deep learning
Deep learning, a set of machine-learning techniques based on neural networking, is still evolving but shows great potential for solving business problems, says Hopkins. “Deep learning . . . enables computers to recognize items of interest in large quantities of unstructured and binary data, and to deduce relationships without needing specific models or programming instructions,” he says.
8. In-memory analytics
The use of in-memory databases to speed up analytic processing is increasingly popular and highly beneficial in the right setting, says Beyer. In fact, many businesses are already leveraging hybrid transaction/analytical processing (HTAP) — allowing transactions and analytic processing to reside in the same in-memory database. source