January 2019 Summaries

Hybrid cloud strategy is becoming increasingly popular among organizations, with 81% of them adopting a hybrid or multi-cloud approach according to RightScale's 2018 State of the Cloud Survey. This diversified approach helps mitigate risks associated with relying on a single cloud provider, such as downtime and data loss. Hybrid cloud environments enable organizations to build redundancy into their infrastructure, ensuring that employees can access the data they need regardless of cloud provider availability. As companies increasingly move to hybrid cloud environments, it is crucial for them to develop robust data management strategies to protect their most valuable asset - data.

DataStax Developer Relations team members Patrick McFadin and Jeff Carpenter discuss their anticipation for the upcoming DataStax Accelerate conference, which they consider to be the biggest gathering of the Cassandra community since the last Cassandra Summit in 2016. They emphasize the importance of community involvement in making this event successful, noting that there are still opportunities for individuals to present their stories and contribute to the conference's content. The CFP (Call for Papers) closes on February 15th, and they provide tips and assistance for those interested in submitting an abstract.

An enterprise data layer is crucial for driving innovation within organizations by providing speed, reliability, scalability, and agility needed to thrive in today's complex business landscape. It offers five key benefits: data autonomy, compliance support, personalization capabilities, increased business agility, and flexibility to adapt quickly to new technologies. By leveraging an enterprise data layer, companies can enhance organizational productivity, meet external regulations, deliver personalized experiences, pivot quickly as market dynamics shift, and stay ahead of competitors by moving to new solutions as their needs change.

As companies increasingly adopt cloud applications, data management has become a significant concern. Traditional relational databases struggle with the volume and flexibility required by modern applications, leading to the adoption of NoSQL databases. Despite this shift, there are five common myths about NoSQL databases: security concerns, lack of reliability, inability to scale out, limited use cases, and unsuitability for distributed cloud applications. In reality, NoSQL databases offer robust security features, high availability, the ability to scale horizontally, support analytics directly on the platform, and are well-suited for distributed cloud and hybrid applications. As more organizations adopt multi-cloud strategies, NoSQL databases can provide significant benefits in managing rapidly growing data demands.

Hybrid cloud databases are being utilized by leading enterprises for various purposes such as powering always-on web, mobile and desktop applications, detecting fraud in real time, supporting personalization, delivering positive user experiences, and processing data in real time. These databases provide agility, reliability, and availability to organizations, helping them stay ahead of the competition and maintain agility. They support distributed teams across the world by ensuring consistent user experiences across devices and geographies, thereby increasing productivity. Hybrid cloud databases also help financial services firms reduce fraud occurrences significantly, protecting customers and increasing profitability. Additionally, they enable personalized experiences for employees and customers, which is becoming increasingly important in today's market. Versatile, multi-model databases store data efficiently and ensure consistent customer experiences across channels. Hybrid cloud databases also provide the agility needed to respond to opportunities immediately, reducing costs and increasing efficiency. Overall, these databases enable organizations to supercharge their operations by mitigating potential risks and increasing productivity.

This tutorial explores the use of StreamSets Data Collector for migrating data from a relational database management system (RDBMS) to Apache Cassandra or DataStax Enterprise. It covers both batch and streaming-based data ingestion, using the KillrMovies RDBMS as an example. The tutorial demonstrates how to configure StreamSets for bulk load use case and implement change data capture (CDC) patterns from mySQL to Cassandra. By leveraging StreamSets configuration as code, users can migrate from a data model appropriate for RDBMS to a data model suitable for Cassandra without writing any custom code.

DataStax Developer Relations team has had a busy and rewarding year, attending numerous events across the US and Europe to provide hands-on learning opportunities for developers. The team also spent time updating courses on DataStax Academy, including new releases of DataStax Enterprise. In 2019, they plan to grow their content offerings online and in person, as well as expand the team. They are looking forward to another exciting year helping developers be awesome.

Over the past couple of months, new versions of C/C++ drivers for DataStax Enterprise (v1.6.0+) and Apache Cassandra (v2.10.0+) have been released. These releases fundamentally changed the internals of the C/C++ drivers and are the biggest since their initial 1.0.0 release. They included a complete refactor of internal components, increased test coverage, and improved performance. The new version of the driver has substantially improved throughput by eliminating the need for requests to be processed by a single shared session thread and upgrading I/O handling. These changes have led to decreased resource utilization and better unit testing capabilities. Future releases will focus on increasing test coverage, improving performance, and incorporating user feedback.

In 2019, personalized experiences, real-time insights, and 360-degree customer views will be driven by data analytics capabilities. Geospatial data usage is expected to increase as technology evolves. Data governance will become more prominent due to increasing importance of ensuring the integrity, availability, and security of data. Organizations will look for solutions that support data autonomy and hybrid cloud environments. Kafka and Docker integration will be a priority for real-time data pipelines and streaming applications.