May 2018 Summaries

The European Union's General Data Protection Regulation (GDPR) has brought about new obligations for data controllers and processors, as well as new rights and power for EU citizens regarding their personal information. With the rise of cloud applications that gather and analyze large amounts of data across various touchpoints, companies are facing challenges in managing this massive volume of data while ensuring GDPR compliance. Graph databases like DataStax Enterprise (DSE) Graph can help provide a complete, real-time picture of customer data, giving both companies and their customers peace of mind in the age of the GDPR.

DataStax Enterprise (DSE) Graph 6 introduces new features and improvements for its distributed cloud database designed for hybrid cloud environments. DSE Advanced Performance provides differentiating performance, with one-hop traversal performance increasing almost 50% and two-hop traversal performance increasing almost 60%. DSE Graph Frames offers a powerful method to work with full graphs or large subgraphs for transactional analytics purposes, mass data transformation/movement, and Apache Spark™-based machine learning algorithms. With the release of DSE 6, users will no longer have to choose which analytical implementation to use to perform a graph analytics operation, as the DSE Graph engine will automatically route a Gremlin OLAP traversal to the correct implementation. Additionally, users can now remove any graph schema element they need in DSE Graph 6, giving them a similar schema experience compared with the rest of DSE.

The new Node.js Fluent API for interacting with DataStax Enterprise (DSE) Graph using Apache TinkerPop™ Gremlin-JavaScript has been released, allowing users to write Gremlin traversals from their JavaScript code. This package leverages the high-level client driver features of the DataStax Enterprise Node.js Driver and supports batching multiple graph updates into a single transaction. The documentation for this API is available here: [link].

DataStax OpsCenter 6.5, a visual management and monitoring solution for DataStax Enterprise (DSE) clusters, has been released with enhancements that simplify operational needs. The Upgrade Service allows seamless patch upgrades of DSE clusters without sacrificing robustness or simplicity. NodeSync feature synchronizes replicas of designated keyspaces and tables as a background process. OpsCenter 6.5 adds thread-per-core metrics to assess the health of DSE nodes and identify performance bottlenecks. The latest version also supports new features such as AlwaysOn SQL in DSE Analytics, with improvements in general product stability.

DataStax Enterprise (DSE) 6 has been released, building on its reputation as the best distribution of Apache Cassandra™ by making it faster, more resilient, and easier to operate. The latest version includes server-side improvements such as DSE Graph statements in batches, support for more languages like C# and Node.js, automatic data synchronization with NodeSync, enhanced Continuous Paging feature, improved prepared statements, and tighter integration of DSE Search with CQL. These enhancements aim to provide a seamless experience for developers working with DSE 6.

DataStax Enterprise (DSE) has been reported to be twice as fast due to its new features and improvements. Alongside this, users can now visualize their data 2x faster when using the Spark SQL connection in Tableau. This is made possible by DSE's Advanced Performance features and DSE Analytics AlwaysOn SQL. The use of visual graphs for data analysis has been proven to be more efficient than tabular formats. In previous versions, SQL access was handled differently, with limitations such as requiring manual start-up and monitoring of the Spark SQL Thrift Server. However, in DSE 6, AlwaysOn SQL addresses these issues by offering a production-ready, enterprise-grade ODBC/JDBC service. Magnitude (previously Simba) has also enhanced their ODBC/JDBC driver to allow seamless integration with the AlwaysOn SQL REST API. For more information or assistance, users can refer to Brian Hess's blog on AlwaysOn SQL or contact [email protected]. A tutorial is available for creating a simple Tableau workbook using data stored in DSE.

This tutorial demonstrates how to connect Tableau to a DataStax Enterprise (DSE) 6.0 Docker Container using DSE Alwayson SQL via the Spark ODBC driver and create a simple Tableau workbook. The integration of DataStax Enterprise and Tableau allows for efficient data computation, joins, aggregates on the server side while visualizing the data through Tableau. This tutorial utilizes Docker to run the DSE 6.0 container, Tableau Desktop 10.5, and Spark ODBC driver 1.2.6.1007. The integration of these tools enables powerful customer experiences and real-time insights by combining advanced data management capabilities with visual analytics.

DataStax Studio 6 has been released with several improvements aimed at enhancing developer productivity. Key features include notebook export/import for collaboration and tutorial distribution, a notebook history feature for tracking changes, support for writing Spark SQL queries, and an interactive graph experience for DSE Graphs exploration without requiring Gremlin expertise.

DataStax Enterprise (DSE) 6 is built on the best distribution of Apache Cassandra™ and includes a production-certified version of Apache Solr™ 6. DSE 6 Search introduces several enhancements, including improved performance and stability, reduced complexity for users, and advanced search capabilities. The new core architecture allows for a cleaner redesign of the search indexing pipeline, resulting in less configuration required to operate search, improved search data consistency, and more throughput. NodeSync brings major benefits to DSE Search by automatically repairing stored search data. Additionally, native CQL queries can now leverage search indexes for a wider array of CQL query functionality and indexing support. Future enhancements will include relevance queries, spatial filters, and facet queries.

DataStax has made its Bulk Loader tool freely available to Open Source Cassandra users, aiming to make loading and unloading data faster and easier. The DataStax Bulk Loader (DSBulk) is a command-line utility for Linux and Windows environments that supports all DSE Advanced Security features. It can load files up to 4x faster than cqlsh's COPY command and is designed to handle bulk loading and unloading use cases. DSBulk does not create new tables or perform data transformations but supports input data in JSON or delimited format from a single file or directory of files. It can also set time-to-live on the data being inserted, add the current timestamp as a column, and specify CQL for insertion. DSBulk is available as a standalone download or as part of DataStax Enterprise and DataStax Basic.

DataStax Enterprise (DSE) 6 introduces AlwaysOn SQL, a highly-available and secure SQL service for analytical queries. This service allows BI, ETL, and other tools to connect via ODBC and JDBC protocols to analyze data in DSE, including tabular data from the database, graph data from DSE Graph, and files stored in DSEFS. AlwaysOn SQL is designed with production environments in mind, ensuring high availability without manual intervention and seamless integration with DSE's Advanced Security features for authentication and authorization. This service enables a broad group of developers to analyze data in DSE without having to learn Spark programming or new languages, making it an important step towards supporting business analysts and the tools they use.

The latest release of DSE Analytics focuses on non-stop availability and ease-of-use for operational analytics workloads, introducing significant new features. These include improvements to Continuous Paging, the general availability of DSEFS, enhancements to DSE’s Spark Resource Manager, and the introduction of AlwaysOn SQL, a production-ready ODBC/JDBC service that provides SQL access to data in DSE. Additionally, DSE 6 upgrades the DSE Analytics engine to Apache Spark 2.2, introduces a new Structured Streaming sink for DSE, and leverages any DSE Search indices. These enhancements improve simplicity, reliability, flexibility, and performance of the DSE platform.