In 2009, the future founders of Kinetica came up empty when trying to find an existing database that could give the United States Army Intelligence and Security Command (INSCOM) at Fort Belvoir (Virginia) the ability to track millions of different signals in real time to evaluate national security threats. So they built a new database from the ground up, centered on massive parallelization combining the power of the GPU and CPU to explore and visualize data in space and time. By 2014 they were attracting other customers, and in 2016 they incorporated as Kinetica. The current version of this database is the heart of Kinetica 7, now expanded in scope to be the Kinetica Active Analytics Platform. The platform combines historical and streaming data analytics, location intelligence, and machine learning in a high-performance, cloud-ready package.

Elastic, the company behind Elasticsearch and the Elastic Stack, announced the appointment of Paul Appleby as president, worldwide field operations. Appleby was most recently the chief executive officer of Kinetica, and will be responsible for enhancing the customer journey, driving global revenue growth, and developing strategies for addressing the large market opportunity for Elastic. Appleby will report to Elastic founder and Chief Executive Officer Shay Banon. Appleby joins Elastic as the company continues to see increasing demand for its enterprise search, observability and security solutions that are built on a single technology stack under a unified pricing model. Elastic's unified, resource-based pricing enables customers to predictably control costs and fuel rapid adoption across its solutions.

Watch how a you can leverage Kinetica as a unified platform for machine learning to accelerate traditional data science workflows in a scenario involving Airbnb listing prices. Take advantage of the raw processing power of Kinetica's GPU-accelerated database, geospatial operations & location intelligence and graph solvers. This demo illustrates the start to end process of building a model, deploying on Kubernetes in a push button environment, visualizing that model right along side the data, and geospatially enriching the model to improve performance.

Ever since the industrial chemist Leo Baekeland began synthesizing phenol and formaldehyde in 1907, the world has developed a love-hate relationship with the resulting polymer: plastic. While plastic is convenient, durable, and cheap, 50% of all plastics (about 150 million tons every year, worldwide) are used only once and then thrown away. Even for those who dutifully recycle our plastic water bottles and sandwich bags, we're only tackling a small part of the problem. "Considering the size of the problem, there's relatively limited infrastructure in place to capture and treat stormwater," says Tony Hale, program director for environmental informatics at the nonprofit San Francisco Estuary Institute (SFEI). That's where SFEI is looking to use research and data--and most recently, drones--to make a difference.

Last week we wrapped up a highly successful GPU Technology Conference (GTC) Europe in Munich! GTC is NVIDIA's international conference series, bringing together the top minds in deep learning, analytics, and of course GPUs for sessions, workshops, keynotes, and more. This was the place to be for any and all European organizations interested in leveraging the power of the GPU. As the Kinetica engine runs on GPUs, there's no better place for us to share our solutions for advanced analytics and deep learning. This year we noticed a significant increase in the number of organizations that understand the challenges of the Extreme Data Economy.

Just because you start a company and raise tens of millions of dollars to fuel it's growth, there's no guarantee you have what it takes to build a large company. In fact, some 60% of founders do not survive their Series D round of venture funding. This comes to mind in considering San Francisco-based database supplier, Kinetica. Founded in 2009, Kinetica raised $50 million in June 2017 -- bringing its total funding to $63 million. Six months later, Kinetica's board replaced the its cofounder and CEO -- Amit Vij -- with Paul Appleby, an experienced sales executive.

How can we avoid the data science black hole of complexity, unpredictability, and disastrous failures and actually make it work for our organizations? We, as a field, and I mean academics, scientists, product developers, data scientists, consultants … everybody … need to redirect our efforts towards operationalizing data science. We as practitioners can unleash the power of data science only when we make it safe and find a way to fit it into normal business processes. Here at Kinetica we couldn't agree more! What's the point of building a brilliant model if you can't actually get it into production?

JupyterLab is an integrated environment that can streamline the development of Python code and Machine Learning (ML) models in Kinetica. With it you can edit Jupyter notebooks that integrate code execution, debugging, documentation, and visualization in a single document that can be consumed by multiple audiences. The development process is streamlined because sections of code (or cells) can be run iteratively while updating results and graphs. It can be accessed from a web browser and supports a Python console with tab completions, tooltips, and visual output. One of the difficulties of using Jupyter notebooks with Kinetica had been that an environment needs to be installed with all the necessary dependencies.

In 2009, the U.S. Army Intelligence and Security Command wanted the ability to track, in real-time, national security threats. Potential solutions had to provide instant results, and use graphics to provide insight into their extremely large streaming datasets. At the time, there was nothing available to meet their needs. Both NoSQL solutions and classical relational systems couldn't handle the scaling requirements. In response, Nima Negahban and Amit Vij, of Kinetica, designed and built a new database.

Today's analytical workloads require faster query performance, advanced analysis methods, and more frequent data updates. For real-time analysis of massive data sets, particularly for use cases where time and location matter, enterprises are turning to new next-generation databases to explore data faster and uncover new insights. "Based on its enormous potential, investments in AI can be expected to increase in 2018, while investments in IoT will need to show measurable return," said CTO and Cofounder of Kinetica Nima Negahban. "The ability to operationalize the entire pipeline with GPU-optimized analytics databases now makes it possible to bring AI and IoT to business intelligence cost-effectively. And this will enable the organization to begin realizing a satisfactory ROI on these and prior investments."