There will be a series of tutorials and sessions on tools and methods for managing and analyzing graphs and time-series data at the Strata Data Conference in San Jose, March 5-8,2018. Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode of the Data Show, I spoke with Leo Meyerovich, co-founder and CEO of Graphistry. Graphs have always been part of the big data revolution (think of the large graphs generated by the early social media startups). In recent months, I've come across companies releasing and using new tools for creating, storing, and (most importantly) analyzing large graphs.

Imagine a future where Minority Report-style data visualizations run in every web browser. This is a big step forward for critical workflows like investigating security and fraud incidents, and making critical insights for the next level of BI. Today's options are dominated by rigid Windows desktop tools and slow web apps with clunky dashboards. The Apache Arrow ecosystem, including the first open source layers for improving JavaScript performance, is changing that. Frustrated with legacy big data vendors for whom "interactive data visualization" does not mean "sub-second", Dremio, Graphistry, and other leaders in the data world have been gutting the cruft from today's web stacks.

The field of AI has seen a significant increase in interest in the past couple of years, including an upswing in the number of large enterprises running their own experiments with AI systems. The vast majority of AI projects in enterprises is still at the experimentation stage where about half of large enterprises are experimenting with "smart computing" projects. These organizations are examining problems and deciding whether AI can be applied to find solutions. These efforts are still in the early stages. But one area that appears to be taking off is how enterprises are now seriously looking at "accelerated analytics" and "AI driven analytics" as a solution to the data deluge many companies are seeing.

The insideBIGDATA Guide to Deep Learning & Artificial Intelligence is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting area of technology. In this guide, we take a high-level view of AI and deep learning in terms of how it's being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We present the results of a recent insideBIGDATA survey that reflects how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains.