March 2018 Summaries

The Economist's Innovation Summit focused on "Building the Intelligent Company" with AI being a central theme. Discussions revolved around where AI can make significant impacts, create new value, and undermine existing business models. A key point was the emphasis on data and its proper use. Panelists highlighted the need for ethical and bias-free AI, as well as the importance of refining and analyzing data to extract value. The healthcare industry is an example where real-time data collection and AI can be used effectively. Quality, correctness, and responsible usage of data were emphasized as crucial factors in implementing AI successfully.

DataStax CEO Billy Bosworth recently participated in a panel discussion at The Economist's Innovation Summit on "AI Haves and Have-Nots." The conversation revolved around how enterprises can effectively leverage AI, as well as the impact of government regulations like GDPR on businesses' competitive edge in an increasingly AI-centric world. Bosworth emphasized that many companies struggle to utilize AI due to internal data issues. The panelists agreed that "good data" is crucial for AI success and maintaining seamless user experiences. However, they also highlighted the challenges of regulating data in a global economy powered by AI while ensuring safety, privacy, and open competition. Bosworth argued that dealing with multiple geo-centric government regulations could hinder AI innovation. The panelists acknowledged that there is still much to learn about AI's potential uses and applications, as well as the need for increased transparency and accountability in handling customer data.

The concept of dimensions in technology has evolved from three to four, five, six, or even ten. Applications today need "The Five D's" - being highly relevant to users through contextualization of historical and real-time behaviors, ensuring availability 100% of the time, providing instant responsiveness, handling a widely distributed user base with easy scalability, and keeping users constantly engaged. These features create customer loyalty and revenue growth. The back end data platform is also crucial for building such applications without incurring high costs or disrupting existing systems.

The author, an immigrant raised by a hardworking mother, shares their experience shadowing DataStax CEO Billy Bosworth for a day. They were impressed by the company's diverse and supportive working environment, as well as Billy's genuine care for his colleagues. The shadow day not only fueled their aspirations to join the corporate world but also taught them valuable lessons about patience in achieving life goals.

This article discusses a pattern for forming Gremlin results using Domain Specific Languages (DSLs) and demonstrates its implementation with Gremlin .NET. The mechanics of the pattern are demonstrated using the C# GLV for .NET, which is part of Apache TinkerPop™. The purpose of this pattern is to shape Gremlin traversal results so that they fit specific needs of an application or analytical view. This can involve limiting property keys returned from a vertex or edge, performing collection manipulation, data aggregation, or other similar operations. The article also explores the implementation of DSLs in .NET via Extension Methods and provides examples of how to create reusable and concise Gremlin code using these methods.

Over six years of working at DataStax, it has been observed that customers using DataStax Enterprise (DSE) and Apache Cassandra™ have experienced zero downtime on their data platform. This is a significant achievement considering the complexities involved in maintaining such platforms. The key to this success lies in DSE's masterless architecture, which allows users to read and write data to any node in a cluster and ensures automatic replication and synchronization of data across multiple locations. Unlike other major database vendors that use master-slave or multi-master architectures, DataStax/Cassandra's unique design enables true constant uptime for distributed applications. This has been demonstrated by customers like Walmart, eBay, Netflix, who have not experienced any downtime in their data layer despite facing hiccups and outages in other areas of their infrastructure.