Top 5 Big Data Trends Of 2014

headhunters, universities, MOOC providers, and others offering various forms of training to big-data-analysis wannabes. Here a sampling of related developments in 2014:

With education and training opportunities flourishing, there will no doubt be waves of new talent available by the time we meet the graduating classes of 2015, 2016, and 2017.

4. Cloud options multiply
Hadoop, NoSQL databases, analytics tools and platforms: You name the technology and businesses are likely to start experiments in the cloud. And many will stay there, having no interest in deploying servers and administering software on premises. That's certainly true of small and midsize practitioners we've met. Vendors are responding to the demand. Here are few of the notable cloud-oriented big data announcements made in 2014:

5. Focus turns to analysis
Data platforms will inevitably be commoditized. The real value in data is delivered through analysis, not just putting it all in a lake or on a data hub. Apache Spark offers a compelling promise: Machine learning, SQL, R-based analytics, graph network analysis, and streaming analysis all on one system. Support soared in 2014. Here's a sampling of related coverage this year:

Spark detractors (perhaps threatened) are whispering that Spark is too green or that niche alternatives (like Apache Storm for streaming analysis) might be better. Spark developer Databricks is responding with system tweaks and benchmark tests said to prove scalable performance.

Rest assured the real competitive battle in big data will be to lead in providing tools and capabilities for data analysis. Multiple commercial vendors (including Actian, Pivotal, and Teradata) seem to be aping the multi-analysis-engine platform strategy, and Cloudera's recent acquisition of DataPad, which offered a Python-based data analysis library, showed it's headed deeper into analytics.

Fasten your seatbelts -- it's going to be a competitive, and interesting, 2015.

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