June 2018 Summaries

The rapid growth of cloud technology has led to new expectations for customization, personalization, intelligence, speed, and agility in both B2B and consumer contexts. However, many enterprises are making critical errors when deciding on the data layer that underpins their cloud applications. Both assuming that a cloud application can rely on databases of yesteryear or that a new cloud message will solve complex database requirements will lead to failure. The cloud requires new approaches to data. Additionally, consumer regulations like GDPR and the rise of tech titans as cloud giants pose further challenges for enterprises. DataStax proposes the Five Freedoms of Enterprise Data as guiding principles to help navigate these difficult decisions: freedom from vendor lock-in, freedom to choose the right technology, freedom to innovate, freedom to scale, and freedom to protect data privacy. These freedoms are critical to maintaining healthy competition, accelerating cloud benefits, and protecting consumer interests.

DataStax Enterprise 6 (DSE 6) has been confirmed by an independent source, zData, to significantly improve performance compared to open source Cassandra. The addition of DSE Advanced Performance in DSE 6 includes optimizations and tools designed to increase throughput, reduce latency, and enhance data loading and unloading speeds. In a benchmark test using an IoT-patterned workload on an AWS cluster, zData found that DSE 6 achieved a 3x performance boost with a 10x reduction in latency, surpassing the internal goal of a 2x speedup in throughput. This demonstrates the potential benefits of running DSE over open source and other Cassandra-styled projects for enterprises seeking to avoid application slowdowns and maintain customer satisfaction.

DataStax provides ODBC drivers for Business Intelligence (BI) tools to enable use cases with Apache Cassandra® and DataStax Enterprise (DSE). In 2015, a new CQL ODBC driver was released, followed by a SQL ODBC driver for Apache Spark™. Both drivers can be used independently with DSE. For BI and ETL tasks, users should adhere to the strengths of the technology being connected to and what's available. Cassandra is best suited for simple queries using CQL read and write fundamentals, while more complex reads, aggregations, joins, and ETL are better handled by Apache Spark. DSE offers enhanced capabilities over DataStax Basic due to its integration with both Cassandra and Spark, as well as additional features like AlwaysOn SQL and custom optimizations in the Spark engine.

DataStax Enterprise (DSE) Advanced Security introduces three new critical security enhancements in DSE 6: Private Schemas, Auditing by Role, and Separation of Duties. These features provide administrators with more control over schema visibility and access to sensitive data while supporting the principle of least privileges for meeting security compliance standards. Auditing by role allows administrators to audit changes and user activity based on roles rather than specific database objects, reducing the audit trail. Restricting SELECT and MODIFY privileges on an admin role is simple in CQL. Security remains a priority in DSE, with these enhancements aimed at keeping customers' data safe.

Distributed cloud databases are becoming increasingly popular for scaling data quickly, easily, affordably, and safely in various applications, including ecommerce. Key features of a suitable distributed cloud database include flexibility across on-premises, private, or public clouds; multi-model capabilities to handle different data models; masterless architecture with advanced replication; linear scalability by adding new nodes; and support for mixed workloads (translytics). DataStax Enterprise is an example of a distributed cloud database that combines these features into one seamless solution, helping enterprises enhance their ecommerce applications.

The global cloud database market is expected to grow from $2.1 billion in 2015 to $68.9 billion by 2022, driven primarily by the adoption of hybrid cloud databases. These solutions offer numerous benefits such as data sovereignty, compliance support, agility, cost-effectiveness, and future-proofing tech infrastructure. Hybrid cloud databases enable organizations to store data securely in specific regions, adapt quickly to changing business needs, reduce management complexity, and innovate faster. They also allow companies to take advantage of public cloud resources while maintaining control over their on-premises hardware stack. Overall, hybrid cloud databases provide a versatile and competitive solution for modern enterprises undergoing digital transformation.

The global ecommerce market is growing rapidly and is expected to nearly double by 2020, reaching $4.058 trillion. To succeed in this competitive landscape, businesses need powerful eCommerce experiences that keep customers coming back. This requires choosing the right database for customer-facing applications. Key features of an ideal eCommerce database include resiliency and speed at scale, advanced security features to prevent data breaches, data autonomy to retain control over data sovereignty, seamless scaling to handle traffic peaks, multi-model capabilities to provide individualized recommendations, compatibility with existing infrastructure, and high availability for continuous revenue generation. By selecting the right database, businesses can unlock significant eCommerce revenue streams and maintain a competitive edge in the thriving market.

Organizations are increasingly moving from traditional relational database management systems (RDBMS) to multi-model databases due to the increasing volume and variety of data. Multi-model databases, which include NoSQL databases, enable companies to store, process, and analyze data as graphs, documents, key-value, and JSON files. These modern databases offer several advantages over RDBMS, including increased efficiency by consolidating infrastructure and reducing operational complexity, improved scalability for performance expansion or reduction, easier detection of relationships between different data formats, enhanced availability with fault tolerance, and greater flexibility in configuring infrastructure. Companies like Microsoft, Sony, and Capital One have adopted multi-model databases to power their operations.

Macy's transformed into a leading ecommerce site by unlocking the full potential of its data, enabling it to provide customers with real-time, personalized experiences. By utilizing DataStax Enterprise and DSE Analytics, Macy's implemented intelligent category-specific filters and a global recommendation engine, enhancing customer satisfaction and driving growth in average monthly visits. The company's omnichannel catalog, powered by DataStax, allows seamless sales across all channels with rapid response times, catering to the needs of the Right-Now Customer.

In today's fast-paced eCommerce landscape, businesses must deliver seamless and personalized customer experiences to stay competitive. Legacy relational databases often fail to meet these demands due to their lack of scalability and inability to handle multiple workloads simultaneously. Modern eCommerce platforms require multi-model databases that can efficiently manage various types of data, including tabular, key-value, JSON, and graph. Additionally, advanced features such as performance optimization, security, analytics, search capabilities, and managed services are crucial for a successful eCommerce database. DataStax offers an always-on distributed cloud database built on Apache Cassandra™, designed to power real-time applications at massive scale and provide meaningful engagement to customers across various platforms.

The latest versions of DataStax Enterprise (DSE) 5.1.10 and 6.0.1 have significantly improved graph traversal performance by optimizing TraversalStrategy initialization in Gremlin traversals, leading to a 1.5x - 2x+ throughput improvement for the Java DSE Graph Fluent API. This enhancement benefits both server-side and client-side applications using the Java DSE Graph Fluent API or String API. Users are encouraged to upgrade to these latest versions of DataStax Enterprise and the DSE Java Driver to leverage these improvements in their graph traversal operations.

The banking and financial services industry (BFSI) faces challenges in managing large volumes of sensitive data due to stricter security regulations and outdated legacy systems. Key mistakes include housing data in multiple silos, collecting data in batches rather than real-time, difficulty deriving actionable insights from relevant data, and interruptions during maintenance windows. Effective solutions involve using an integrated and unified data collection system, a scalable multi-model database with hybrid cloud solution for real-time data collection and analysis, a centralized cloud database like DataStax Enterprise to reduce complexity, and investing in always-on architectures to ensure continuous service delivery.