October 2018 Summaries

ProtectWise launched its cloud-powered Network Detection and Response (NDR) platform, The ProtectWise Grid™, in 2015 to shift core network security functionality away from traditional hardware appliances to a fully on-demand model delivered entirely from the cloud. DataStax Enterprise (DSE), with its distributed database capabilities, is a critical component of this technology infrastructure, enabling efficient storage and management of massive time series data without impacting performance and availability. DSE's integration with Apache Solr™ allows ProtectWise to search through billions of network communications quickly and derive answers from these data points in mere seconds. The partnership between ProtectWise and DataStax is creating the future of security, offering a simpler, faster, more effective, and affordable approach to threat detection and response.

The second part of this blog series focuses on setting up DataStax Enterprise Analytics with Apache Cassandra™ and Apache Spark™, Jupyter Notebooks, and all the required Python package dependencies using Docker. This process automates the task of asking a large number of people for movie recommendations. By utilizing Big Data technologies such as DataStax Enterprise Analytics, Apache Spark Machine Learning Libraries, Twitter Developer API, Pandas, and others, users can explore data science concepts and customize their analysis. The third part of this series will delve into each cell in the Jupyter notebook. For more information on deploying DSE Docker containers, refer to Kathryn Erickson's blog post.

DataStax Academy Developer Days is a series of events aimed at teaching developers and practitioners about distributed databases and systems, with a focus on the DataStax Enterprise product built on Apache Cassandra™. The day-long courses cover core principles behind distributed systems, including consistent hash ring, datacenter and racks, gossip, replication, snitches, and more. There are three specific tracks for varying levels of expertise. So far, events have been held in New York City, Washington DC, Chicago, and Dallas, with upcoming events scheduled in London and Paris. Future event locations are determined based on community interest and feedback.

DataStax customers are praised for their innovative solutions in the fast-paced and data-rich world. Babylon Health, a healthcare provider, uses DataStax Enterprise Graph to track and analyze patient session data, personalize healthcare recommendations, and manage data across multiple cloud services worldwide. Intercontinental Exchange (ICE), an operator of 12 regulated exchanges and 6 clearing houses, utilizes DataStax Enterprise for real-time search and analytics in providing end-to-end risk management solutions to customers in financial and commodities markets. Both companies leverage the power of always-on cloud databases to exceed user expectations and over-deliver on business outcomes.

Advanced Micro Devices (AMD) is an American multinational semiconductor company that develops computer processors for business and consumer markets. The advent of big data has revolutionized analytics and data science, leading to the need for efficient data processing architectures like DataStax Enterprise (DSE). DSE implements a multi-workload, low-latency database at cloud-scale, enabling outstanding customer engagement and real-time personalization. AMD's revolutionary EPYC™ processor is well-suited to power this system due to its x86 architecture, high core counts, large memory capacity, ample memory bandwidth, and massive I/O capabilities. The balanced set of resources in the EPYC processor allows for more freedom to right-size server configurations to workloads. EPYC has gained significant momentum in the industry this year, being designed into over 50 server platforms by major vendors like HP, Dell EMC, Super Micro, and Cisco. AMD is proud to partner with DataStax to create jointly validated and engineered solutions to meet today's challenges head-on.

Organizations today can leverage their data for better decision-making, improved customer experiences, innovation acceleration, and crisis management or prevention. However, many struggle with this due to data silos - isolated repositories that impede fluid access and analysis of all available data. To break down these silos, companies should prioritize eliminating them, use platforms offering data autonomy, encourage communication and collaboration among teams, simplify and consolidate tech infrastructure, and clearly define data handling guidelines for employees to follow.

Enterprises are merging analytic and operational data environments to capture real-time data and provide services based on outbound events. IBM's Global Business Services unit developed the Digital Insights Platform, a multi-tiered platform that captures, stores, analyzes, and takes action on data from various sources. The platform features a three-tiered data layer that is easy to scale for different applications, departmental uses, and enterprise uses. DataStax Enterprise (DSE) helps organizations obtain access to all the right data in one comprehensive and secure environment, offering real-time, always-on, and highly scalable solutions that traditional RDBMS environments cannot provide. DSE is designed for data management at scale across different data centers, between cloud regions, and across hybrid cloud deployments.

Many organizations are adopting hybrid cloud and multi-cloud strategies for flexibility and choice in managing their data. However, a successful implementation requires a well-aligned data management strategy to avoid data silos and ensure seamless communication between applications. A multi-cloud database strategy is crucial, with features such as full data autonomy, linear scalability, and support for multiple regional and global data centers. An Active Everywhere database like DataStax Enterprise can help businesses grow and innovate by providing an efficient enterprise data layer to manage their hybrid/multi-cloud environment effectively.

Registration for the DataStax Accelerate conference is now open. This premier event will be held on May 21-23, 2019 at the Gaylord National Resort and Convention Center in Oxon Hill, Maryland. The conference aims to discuss trends and technologies related to distributed database technology. It will feature separate executive and technical tracks, training sessions, an exhibitor pavilion, networking opportunities, and presentations from DataStax executives, customers, and partners. Attendees can learn about building and deploying enterprise applications using Apache Cassandra and DataStax while scaling their data needs to fit the hybrid and multi-cloud world.

DataStax has officially launched production-ready Docker images for its products, including DataStax Enterprise (DSE), Studio, and OpsCenter. The company has spent years refining these images to ensure their reliability when deployed with Docker. Since the non-production release in 2017, feedback from users has been incorporated into enhancements made in the production images. These images are now officially production-ready and serve as the foundation for many of DataStax's training courses and the full KILLRVIDEO application stack.

The new Thread Per Core architecture in DataStax Enterprise (DSE) 6 offers improved performance through its coordination-free design, which maximizes throughput. In addition to performance optimizations and analytic speed improvements, DSE 6 introduces a bulk loader feature that makes this release impactful for customers. Storage engine optimizations have led to improved latencies, while compaction performance has also been enhanced. NodeSync is the new standard for maintaining data consistency between replicas. Traffic Control, a back-pressure mechanism introduced in DSE 6, prevents server overloads by safely queuing requests as thresholds are met. This feature greatly improves resiliency and enables users to avoid being down. Search and analytics enhancements have also been made, with improvements to indexing stability and the availability of SQL through AlwaysOn SQL.

Hybrid cloud is a common term used by enterprises for policy-based and coordinated service provisioning across internal and external cloud services. Currently, 96% of enterprises use the cloud with 71% identifying as hybrid cloud and 81% as multi-cloud in their data architecture. The adoption of hybrid and multi-cloud strategies is crucial to prepare for unpredictable future paths. Hybrid cloud offers benefits such as massive scalability, streamlined digital services, and agility in today's world. However, companies often face challenges like data siloing when shifting to hybrid cloud. An Active Everywhere distributed cloud database can help leverage hybrid multi-cloud architecture and maintain control of data while ensuring seamless communication between applications across public clouds and on-premises data centers.

The text discusses a leading ecommerce company's use of DataStax Enterprise (DSE) in their software stack, highlighting a single table within an operational DSE database that is 40TB in size and still growing. This scale is considered impressive as most databases have trouble scaling to such sizes. The author emphasizes the need for a data management platform with real linear scale and architecture capable of handling big data without any fine print or footnotes. Datanami selected DSE as their big data product pick for 2018, noting its suitability for businesses that require medium to very large amounts of operational data to stay competitive. The author invites readers to explore resources showcasing DSE's capabilities and consider trying it out in their own environment or using DataStax Managed Cloud.

The blog provides a summary of the fantastic keynote and wonderful talks that were featured at Graph Day 2018. Dr. Gosnell's keynote discussed six lessons learned from production graph application via SimCity 2000, emphasizing setting priorities, understanding rules, accepting mistakes, making compromises, planning effectively, and learning from others' experiences. Other talks covered topics such as supernodes in graph databases, practical tips to defend against them, using DSEGraphFrames for moving graphs between Cassandra and Apache Spark GraphFrames, and a comparison of TinkerPop Vertex Programs vs GraphFrames. The event also included live demos with code, interviews, and keynotes that will be posted on the Youtube channel.