June 2019 Summaries

The increasing adoption of IoT-enabled supply chains is challenging data management specialists to handle large amounts of data generated by connected devices and vehicles, especially with the advent of 5G networks. Real-time analytics will be crucial for managing this data in both short and long term. Organizations need to explore options like cloud databases and multi-cloud platforms to prepare for IoT adoption, while considering integration strategies and costs. A hybrid application model can help businesses manage large volumes of incoming data from IoT devices without causing major disruptions or loss of service. Companies that plan their data management strategy now will have a competitive advantage in the future.

Hybrid and multi-cloud strategies are becoming increasingly popular as companies seek to avoid vendor lock-in, protect sensitive data, and increase organizational agility. Hybrid cloud environments incorporate infrastructure from several platforms and data centers, while multi-cloud infrastructure involves a combination of on-premises, hosted, and cloud services spread over internal data centers and multiple private and public clouds. There are various types of hybrid and multi-cloud deployment scenarios based on application needs and IT infrastructure setup. Hybrid cloud adoption is surging due to the cost savings, agility, scalability, and availability it provides for modern applications. However, managing associated data can be complicated without the right approach. Companies need a well-thought-out strategy before migrating to hybrid or multi-cloud environments to overcome challenges and unlock the true promise of the cloud.

Hybrid clouds combine public and private clouds to provide fast development, hassle-free scaling, and efficient app distribution. To implement a successful hybrid cloud strategy, organizations should follow these six essential steps: determine their cloud resource needs, take an inventory of available cloud resources, integrate data and apps across different clouds, establish data autonomy, containerize applications for flexibility, and manage the hybrid environment effectively. Hybrid clouds also offer resilience against service outages, natural disasters, and cyberattacks. DataStax provides a platform that enables organizations to easily deploy their data-driven applications across on-premises data centers and cloud providers while ensuring security and scalability.

DataOps, or data operations, is the next evolution of DevOps teams where data scientists, processes, and tools are integrated into the development process at data-focused enterprises. This allows companies to leverage real-time data for better customer experiences and decision-making. By adopting an agile approach to data management, businesses can build modern, data-rich applications in less time with more automation and agility. DataOps is not just DevOps with data; it promises to transform the way organizations use data and run data analytics, speeding up data-driven processes considerably. The benefits of DataOps include faster development cycles, improved efficiency, and better decision-making. Implementing DataOps requires buy-in from teams, the right tools, and an active everywhere database for real-time contextual data access. Despite the challenges, adopting a DataOps mindset can lead to significant dividends in terms of speed, decision-making, and customer satisfaction.

Data modeling is crucial for managing complex relationships between applications and their data, especially with the increasing volume, variety, and velocity of modern databases. However, relational databases are less effective at handling diverse types of data. Apache Cassandra® offers a more suitable approach to data modeling by focusing on application requirements rather than the data itself. Unlike relational databases, Cassandra allows for efficient writes, denormalization, and duplication of data, which can improve read efficiency. Proper data modeling accelerates application development, improves performance, and lowers long-term maintenance costs. Transitioning from a relational database to Cassandra may seem challenging, but with guidance and the use of SQL-like Cassandra Query Language (CQL), it can be accomplished in just five simple steps.

In this blog post, we discuss the process of unloading data using DataStax Bulk Loader (DSBulk). We begin with a simple unloading example and cover various aspects such as handling multiple threads, specifying output root, modifying input and output using command-line tools, selecting specific columns or rows, and customizing delimiters and date formats. Additionally, we explore the use of DSE Search predicates in custom queries for filtering data during unloading.

Hybrid cloud is becoming increasingly popular among enterprises as they seek to build modern applications. Many companies are now investing in cloud technologies at a faster rate than other software technologies combined, with cloud spending expected to grow more than six times faster than other IT expenditures through next year. The adoption of hybrid cloud typically follows several stages: initial on-premises applications without a clear cloud strategy; identifying opportunities for cloud migration and using SaaS apps; developing a hybrid cloud strategy and eliminating data silos; achieving app convergence and improving security; corralling data to increase efficiency; and finally, reaching full hybrid cloud maturity with optimized, interoperable clouds that preserve data autonomy. Organizations must continue to invest in their cloud assets to stay ahead of competitors and meet evolving business needs.

DataStax Accelerate 2019, the first user conference by DataStax, highlighted the partnership between DataStax and VMware in building modern applications for hybrid clouds. The event also marked the inaugural Partner of the Year Award given to VMware. Both companies have been working together on enhancing their joint solutions, including multi-cloud capabilities and enterprise-grade availability. With recent investments, they aim to provide innovative capabilities to organizations adopting this solution. DataStax and VMware deliver three key benefits: multi-cloud capabilities, enterprise-grade availability, and intrinsic security. Together, they empower today's enterprises to take full advantage of multi-cloud computing environments with enhanced security and availability.