Ladies and gentlemen, data scientists of all ages, here is the big finale to 2016. The ne plus ultra companies that have accelerated and improved their positions in the big data and analytics marketplace. It's been no easy task to select, much less rank, all the top 20. I'd like to also note once more that there are many, many other worthy companies ticking right along and executing on their vision, earning new customers, and generally succeeding. And yes, it's all a bit subjective, but I truly believe these are ones that have made the most progress. Here are my final five choices:
5. Teradata - The data warehouse is dead, long live the data warehouse. Yet, it's everything else they are doing that earns Teradata this spot in the awards. The company has embraced open source software in a surprising number of important ways: sponsoring projects, embedding and integrating with their extant product line, inventing entirely new offerings, and not least incorporating serious expertise in professional services to make stuff work. Big turnaround for Teradata, and well worth recognition.
4. MapR - In a year where many were critical of the various Hadoop distribution darlings losing momentum, each has striven to expand its portfolio and relevance. I'd contend that scrappy underdog MapR has arguably delivered the biggest re-invention, except it's actually just executed on exactly what it said all along. The Converged Data Platform is radically elegant and couldn't have been built without the clever architectural decisions made early on. It's only now that everyone else is finally appreciating the advantages. Converged MapR has diverged from the pack.
3. Confluent - Pipelines are where it's at. Not for oil, but for something far more valuable: data. Kafka is coming into the forefront as the leader for manipulating real-time streams of data, and Confluent has suddenly asserted itself as the leader for Kafka. With sharp new leadership around both product and marketing, the company is now delivering the enterprise-quality, de facto standard for streaming. Hint: their work around universal schemas has massive market-shaking potential, too.
2. Amazon Web Services - Ok, AWS isn't exactly new to big data. AWS infrastructure-as-a-service has been emerging as THE place for developers and data scientists to explore, create, play, test, and run their data, their analytics models, and their applications. That isn't a secret, but perhaps few realized that AWS wasn't just for easy access to compute and storage resources. The company has quietly made huge gains around EMR, Redshift, Kinesis, ElasticSearch, plus NoSQL, graph and relational databases (RDS). AWS introduced new offerings like Athena for direct SQL query and QuickSight for BI. The biggest player is going to rapidly harden its dominant leadership position with these improvements.
1. Google Cloud Platform - So who can challenge AWS for supremacy? Google can. And Google will. GCP has suddenly de-cloaked with an impressive arsenal of capabilities. While the company is famous for driving efficiency at scale, it's now more powerful than ever with BigQuery, DataLab, DataProc, DataFlow, Pub/Sub, and yes, cloud-based machine learning. The strategic move to open up Tensorflow is working as planned. Google is once again showing that their "invented here" can creatively ignore assumptions of how to do things, and has quickly disrupted the order of the big data and analytics universe. If there was a (positive) disturbance in the Force felt broadly in 2016, it was Google.
Congratulations to all Delta-V award winners! Most importantly, while this year is in the books a new year awaits and many surprises will come to the industry so let's all work together to have a lovely and lively 2017!
Catch you on the flip. - Nik