"In 2015, data lakes will evolve as organizations move from batch to real-time processing and integrate file-based, Hadoop and database engines into their large-scale processing platforms," he says. "In other words, it's not about large-scale storage in a data lake to support bigger queries and reports; the big trend in 2015 will be around the continuous access and processing of events and data in real time to gain constant awareness and take immediate action."
3. Self-Service Big Data Goes Mainstream
Advances in big data tools and services means that 2015 will be the year that IT can ease away from being a bottleneck to the access of data by business users and data scientists, Schroeder says.
"In 2015, IT will embrace self-service big data to allow business users self-service to big data," he says. "Self-service empowers developers, data scientists and data analysts to conduct data exploration directly."
Previously, IT would be required to establish centralized data structures," he adds. "This is a time-consuming and expensive step. Hadoop has made the enterprise comfortable with structure-on-read for some use cases. Advanced organizations will move to data bindings on execution and away from a central structure to fulfill ongoing requirements. This self-service speeds organizations in their ability to leverage new data sources and respond to opportunities and threats."
4. Hadoop Vendor Consolidation: New Business Models Evolve
In early 2013, Intel made a splash with the introduction of its own Hadoop distribution, saying that it would differentiate itself by taking a ground-up approach in which Hadoop was baked directly into its silicon. But just a year later, Intel ditched its distribution and threw its weight behind Hadoop distribution vendor Cloudera instead.
At the time, Intel noted that customers were sitting on the sidelines to see how the Hadoop market would shake out. The number of Hadoop options were muddying the waters. Schroeder believes Hadoop vendor consolidation will continue in 2015 as the also-rans discontinue their distributions and focus elsewhere in the stack.
"We're now 20 years into open source software (OSS) adoption that has provided tremendous value to the market," Schroeder says. "Technologies mature in phases. The technology lifecycle begins with innovation and the creation of highly differentiated products and ends when products are eventually commoditized. [Edgar F.] Codd created the relational database concept in 1969 with innovation leading to the Oracle IPO in 1986 and commoditization beginning with the first MySQL release in 1995. So historically, database platform technology maturity took 26 years of innovation prior to seeing any commoditization."
"Hadoop is early in the technology maturity lifecycle with only 10 years passing since the seminal MapReduce white papers were published by Google," he adds. "Hadoop adoption globally and at scale is far beyond any other data platform just 10 years after initial concept. Hadoop is in the innovation phase, so vendors mistakenly adopting "Red Hat for Hadoop" strategies are already exiting the market, most notably Intel and soon EMC Pivotal."