Early in December 2016, I spoke at the Strata+Hadoop World 2016 Singapore conference on sensor data analysis approaches, specifically, time series analysis. My company, The Data Team, were represented at Strata+Hadoop World at the innovator’s pavilion. It was a wonderful learning experience for me at the conference, and I have the following key take-aways:
- There is a lot of interest in advanced machine learning algorithms, deep learning and the capabilities it offers
- Platform-level innovation is still driving a large part of the big data and data science world forward. Hadoop ecosystem projects are aplenty, and have plenty of variety at the moment
- There is significant interest in Apache Spark and the capabilities it provides to data science teams. With growing data processing needs and the need to distribute data processing, the Databricks team have improved the Apache Spark interface and performance, so that it is easier to use than before, and scales with fewer teething problems
- Finally, there is a growing interest in the Internet of Things. Both on the platforms side of things, where new frameworks, architectures and ideas around such systems are discussed, and on the data science side of things, where sensor data analysis approaches and best practices are being discussed, there is increasing momentum.
My talk at Strata+Hadoop World 2016 was on the subject of sensor data analysis. The talk discussed a broad range of approaches, for the analysis of aggregate (I.I.D) data and time series data. You can find a video of the talk at this link. Slides from my talk are here (as a ZIP file).