October 2016 Summaries

We're excited to announce the first ever SingleStore Real-Time Roadshow, being held in Phoenix, a vibrant city with leading corporations in industries such as healthcare, education, and finance. These businesses face the challenge of keeping up with user expectations for personalized and immediate services, and real-time applications are seen as a solution. The event aims to connect industry leaders and data engineers with experts who can provide insights on real-time initiatives. Through technical sessions and demonstrations, attendees will learn about forces driving the need for real-time workloads, processing millions of data points into actionable insights, and how predictive analytics gives companies a competitive advantage. Top data architectures for real-time analytics and streaming applications will also be discussed, along with use cases and examples from companies building real-time applications. The roadshow features speaking sessions on topics such as driving the on-demand economy with predictive analytics, real-time analytics with SingleStore and Apache Spark, and real-time data ingestion and analytics with Apache Kafka and SingleStore.

Twitter has become a significant platform for real-time discussions and analysis during the 2016 presidential election, with Pew Research Center stating that 44% of U.S. adults reported learning about the election from social media in January 2016. The first presidential debate between Donald Trump and Hillary Clinton was the most-tweeted event ever on Twitter, with 17.1 million interactions recorded. To analyze sentiment around these candidates, a real-time analytics demo was built using Apache Kafka, SingleStore, Machine Learning, and Pipelines. The demo collects tweets containing keywords related to Hillary and Trump, analyzes their sentiment, and provides insights into the trending positive or negative sentiments for each candidate in real-time. Two pipelines were created: one to store data from the collected tweets into a table and another to perform sentiment analysis using Python's Natural Language Toolkit (nltk) Vader module. The demo allows users to access analytics through SQL queries, visualizing rolling average tweet sentiment for both candidates in real-time, and can be accessed by following instructions on GitHub or downloading SingleStore Pipelines.

The operational data warehouse powering real-time data ingest and analytics is SingleStore, which powers leading companies in finance, retail, media, and energy. The company hires exceptional talent from top universities and tech giants to drive innovation in real-time technology. A new office in Seattle, Washington, marks a concerted effort to attract the best engineers, offering breathtaking views of the city and its landmarks. The location was inspired by industry experts based in Seattle, who share SingleStore's vision for real-time database solutions. With rapid growth in San Francisco and expanding product offerings, the company seeks talented engineers with expertise in databases and distributed systems.

Analytics is trending as organizations across multiple markets recognize the value of applying actionable insights using real-time analytics to anticipate and adapt to rapidly evolving market dynamics. According to a Gartner report, more than 50% of surveyed users believe that senior executives see advanced analytics as critical to their organization's success, with nearly double the inquiries on the topic in January 2015 compared to January 2014. The top reasons businesses choose advanced analytics platforms are ease of use and the ability to quickly build large numbers of models, reflecting a shift away from legacy approaches that rely on disparate databases or piecemeal collections of tools. To truly operate in real time, modern enterprises need vendors providing highly durable and distributed in-memory databases with sub-second transaction and analytical processing capabilities, as well as scalability and flexibility. Gartner advises technology users to select vendors based on their own needs rather than relying solely on ratings or other designations.

The text discusses the process of winning a deal by finding a suitable workaround to solve an issue, such as executing a database proof of concept. It highlights that there are no perfect databases and workloads, and that real-world scenarios are often based on models built over many years. To succeed, one must be able to innovate, adapt, and be flexible, focusing on strengths rather than trying to overcome limitations. The process involves understanding the data and workload, loading the data, executing the workload, identifying how to meet customer requirements, packaging up the results, and presenting it. Ultimately, cost-effective solutions that meet most of the customer requirements tend to win the deal. The text also references the Kobayashi Maru Training Exercise from Star Trek, which is a no-win scenario designed to test character and problem-solving skills.