August 2018 Summaries

DataStax has partnered with Microsoft Azure Stack to help enterprises leverage their data effectively in strategic initiatives such as customer 360, fraud detection, ecommerce, and security. The increasing velocity and volume of data have led to the emergence of multiple data warehouses, lakes, and databases, creating additional complexity for organizations. DataStax Enterprise (DSE) 6, a multi-model NoSQL database built on Apache Cassandra™, offers a masterless architecture with no single point of failure, allowing nodes to reside in different data centers or multiple clouds. This technology is not a rip and replace solution, making it easier for enterprises to manage data from various sources at high speed. DataStax and Microsoft continue to strengthen their partnership by focusing on customer needs and delivering innovation, ease-of-use, and cost-effectiveness.

In this two-part series, we explored integrating graph databases with web applications. Part 1 focused on creating a data access object (DAO) to implement basic CRUD operations for vertices and edges. In this second part, we delved into graph visualization in user interfaces using Javascript frameworks like D3js, HighCharts, and VisJS. We also discussed how to create a REST API accepting Gremlin queries as input and generating JSON format expected by VisJS. The final result is an interactive and visually appealing representation of data from graph databases.

Relational databases management systems (RDBMSs) have been widely used for storing and processing critical business information since their inception in a 1970 paper by Edgar Codd. The introduction of SQL allowed for efficient access and modification of large data sets, making RDBMSs the most popular data management system. However, they are limited to handling relatively small amounts of structured data. With the rise of big data, NoSQL databases emerged as a solution for managing large volumes of unstructured data. While currently only accounting for 3% of the database market, NoSQL is gaining traction and offers advantages over RDBMSs such as handling large data volumes and supporting various data models. Despite this, businesses are not abandoning their RDBMS systems entirely, as they remain effective for managing transactional workloads. An enterprise data layer can help integrate legacy technology with new applications, allowing organizations to take advantage of both RDBMSs and NoSQL databases.

This article discusses how to leverage Apache Cassandra's paging features to support a great user experience in web applications. Paging is an important technique for scalability and performance, breaking the loading of large data sets across multiple requests or "pages". The DataStax drivers default to a page size of 5000 rows, which can be overridden at the driver level or for individual statements. Apache Cassandra's paging feature is available since version 2.0 and has been used in various services within the KillrVideo reference application. The article also mentions continuous paging, introduced in DataStax Enterprise 5.1, which can improve read performance up to 3x for analytic queries such as full table scans.

This article discusses integrating graph databases with web applications, focusing on CRUD operations, pattern detection, and visualization in the user interface. It provides a step-by-step guide to setting up DataStax Enterprise using Docker images and creating a schema for the Killrvideo reference application. The text also demonstrates how to populate the graph with sample data and execute Gremlin queries using Java applications. In Part 2, the author will discuss visualizing the graph in web application user interfaces.

In the past, venture capitalists were skeptical about the need for distributed database platforms outside of tech giants like Google and Amazon. However, today it is clear that any business wanting to connect with customers via digital applications requires a multi-home based platform. Multi-cloud has become the new multi-data center approach for many enterprises. Gartner Group identifies four different cloud deployment models: Architecture Spanning, Use-Case Specific, Multicloud, and Intercloud. The latter is an evolution of the multi-data center, multi-home design with single applications spanning multiple clouds and on-premise data centers. DataStax's active-everywhere architecture allows for easy provisioning, deployment, upgrading, and administration of a secured database cluster that runs across multiple clouds and on-premises. This is not something offered by legacy relational database management systems or cloud vendor databases limited to their own platforms.

DataStax has been named to the Constellation ShortList for Hybrid- and Cloud-Friendly NoSQL Stores, highlighting the importance of modern database platforms in assisting enterprises with their digital transformation. The recognition also emphasizes the role that hybrid- and cloud-friendly NoSQL stores play in helping companies make strategic decisions regarding data and cloud deployments. DataStax Enterprise, which is built on Apache Cassandra open source software, offers enhanced performance, simplified operations, and advanced capabilities such as search, analytics, and graph. These features provide a robust data platform for enterprise users to drive business transformation in the increasingly hybrid cloud world.

This article provides a comprehensive guide on how to quickly get started with DataStax Enterprise using various deployment options. It covers setting up clusters in Google Cloud Platform Marketplace and Azure Marketplace, as well as using Docker for local testing and development. The author shares their experience of joining DataStax and the need to have live 3+ node clusters for immediate use cases. They also discuss different ways to get a cluster up and running without waiting, such as using Google Cloud Platform's Cloud Launcher or Azure Marketplace. Additionally, the article provides information on how to use Docker images for local development with DataStax Enterprise 6 setup.

The financial institution and banking industry face immense pressure during digital transformation due to customer expectations, regulatory demands, and competition from app-only banks. Data management is crucial for success in this sector, with BFSI organizations turning to data experts like Chief Data Officers. Capital One and Macquarie are examples of banks that have successfully implemented distributed cloud databases for efficient data management, real-time customer interaction analysis, and exceptional user experiences. Additionally, financial firms need robust security measures, such as linear scalability, masterless architecture, end-to-end encryption, and built-in enterprise-grade security to protect customer data and meet regulatory requirements like the GDPR.

This article discusses the pros and cons of public cloud database services, the case for using DataStax Enterprise as a data layer across multiple clouds, and advice on running DSE in Kubernetes. It highlights that smart large enterprises will not lock themselves into a particular cloud vendor due to reasons such as the need for data to flow freely and be fetched quickly whenever needed. The article also mentions that Google has open-sourced Kubernetes and announced that the GKE is available on-prem, which enables multicloud and hybrid cloud strategies. DataStax Enterprise is highlighted as a solution for running DSE in Kubernetes by leveraging StatefulSets and Local Persistent Volumes. The article concludes with a suggestion to weigh the input and output of running DataStax on Kubernetes against your needs and requirements before using it just for its popularity.

The Technical Evangelist team at DataStax has been renamed to Developer Advocates. Their mission is to make developers successful with Apache Cassandra and DataStax products by educating and exciting them about the technology, amplifying their voices within DataStax, and representing their viewpoints internally. The change in title emphasizes two-way interactions rather than one-way communication, reflecting a shift from raising awareness to focusing on developer success with the technology.

The Uptime Institute's report reveals that downtime incidents are increasing and their consequences are becoming more severe. This contradicts the common perception of cloud providers offering 99.99% uptime guarantees. Third-party service providers, including colocation, hosting, or cloud, contribute to a significant portion of downtime. To prevent downtime, Uptime suggests using a multi-home strategy called "Distributed Resiliency." DataStax Enterprise (DSE) is the only masterless architecture available from any mainstream database vendor today, offering true zero downtime and deployment freedom across multiple on-premise data centers, cloud providers, or hybrid clouds.

The global eCommerce market is rapidly expanding and is expected to more than double by 2020, reaching above $4 trillion. To maximize revenues in this sector, companies need efficient, future-proofed eCommerce engines and effective customer targeting strategies. However, many organizations overlook critical components of their data infrastructure and eCommerce strategies. Common mistakes include relying on outdated tools and legacy infrastructure, neglecting site architecture, having poor product data, and lacking the ability to analyze collected data effectively. To succeed in eCommerce, companies must leverage modern solutions designed for the Right-Now Economy and focus on improving their data management practices.

Banks are facing disruption due to their tarnished image since the financial crisis, interchangeable services, and competition from fintechs with innovative digital approaches. Current studies show that customers want real-time advice, analysis, and recommendations via their smartphones, similar to online giants like Amazon, Ebay, or Google. The two main reasons for loss of market share in the banking sector are not knowing enough about customers and not personalizing offers. Financial institutions need to unlock the full potential of customer data by merging siloed data and using external and internal data records together to gain powerful insights. Social media allows financial services institutions to offer personal advice, develop individually tailored offers, and improve multi-channel integration. Modern databases and data management systems are crucial for managing data across all sources and channels. Banks need to store and analyze every customer interaction to make the right decisions and create innovative mobile banking apps with features like investments in foreign currencies or precious metals, Twitter transfers, social lending, social trading, virtual currency trading, community functions, real-time transaction categorization, and open APIs connecting to other financial products.