July 2019 Summaries

Apache Cassandra is widely recognized as the top NoSQL engine for handling large volumes of operational data, due to its masterless, divide-and-conquer architecture that outperforms other databases. However, open source Cassandra has a storage limitation of around one terabyte per machine, which can lead to negative management and cost implications for extraordinarily large databases. DataStax Enterprise (DSE) offers solutions to this issue by enabling users to store 2-4X the amount of compressed data per node compared to open source Cassandra, resulting in productivity and cost savings. DSE's advanced performance enhancements and storage engine optimizations allow it to handle more compressed data per node for general applications and time-series styled applications like IoT. A real-life example is a large media content delivery company that saved $7 million over three years by switching from open source Cassandra to DSE, along with other benefits such as reduced cloud costs and elimination of additional search clusters.

The use of containers in building modern microservices-architected applications is increasing due to their efficiency and cost reduction capabilities. Many organizations are using platforms like Docker to build applications with containers, and half of these companies are utilizing container orchestration platforms such as Kubernetes. Kubernetes adoption has been steadily growing over the years, making it a popular choice for data management in addition to its use in application development. DataStax Enterprise is a solution that enables organizations to manage their data effectively using containers and Kubernetes, offering benefits like flexibility, speed, scalability, and easy integration with native Google Cloud services.

Microservices architecture is becoming increasingly popular in modern applications due to its speed, availability, and performance benefits. Containers are a key technology enabling microservices, with Docker being the most widely used container platform. By 2022, Gartner predicts that over 75% of global organizations will use containers. The primary reasons for this adoption include accelerated development lifecycles, increased efficiency, resource sharing, and improved productivity. Containers allow developers to package code and dependencies, enabling easy movement between environments. They also help eliminate repetitive work and old-school development practices, making it easier to build applications. As companies scale their infrastructure, the use of containers becomes more likely, leading to better business outcomes through improved software development approaches.

Hybrid cloud strategies are becoming increasingly popular among enterprises as they seek to leverage the benefits of both public and private cloud providers. The hybrid cloud market is expected to grow significantly, with more than 65% of organizations already using such environments. However, a successful transition to hybrid cloud requires a well-thought-out strategy that considers factors like vendor lock-in, data management, and implementation plans. By addressing these issues and choosing the right solutions, companies can build powerful applications that enhance employee productivity and deliver exceptional user experiences.

Modernizing IT architectures is crucial for enterprises to build better applications, innovate faster, and stay competitive. A recent survey conducted with Dimensional Research highlighted the challenges faced by IT executives in this regard. More than 300 IT execs from companies with at least 5,000 employees participated in the survey, with 80% coming from companies with 10K or more employees and 46% from companies with 50K or more. The results showed that while all organizations are modernizing their technology architecture, nearly all (99%) face challenges in doing so. Key factors behind this struggle include legacy systems, lack of resources, and difficulty integrating new technologies.

DataStax, a company working in hybrid and multi-cloud environments, recently announced the launch of DataStax Constellation, a new cloud data platform designed to simplify application development and management. This announcement has led to significant changes within the organization as it expands its cloud-native offerings. As part of this restructuring, around 10% of the company's workforce was let go, with many employees transitioned into new roles aligned with the future plan. DataStax is now hiring approximately 100 new employees to support the development and launch of its Constellation platform and cloud-native offerings. The company aims to grow responsibly while delivering innovative products and services in the rapidly evolving cloud landscape.

DataStax's Java driver for Apache Cassandra and DSE includes a useful feature called Object Mapper, which simplifies database access by allowing direct mapping of Cassandra tables into business objects. This approach eliminates the need to write numerous calls like getInt or getString to extract data from returned rows and convert it into classes. The Object Mapper also supports execution of custom queries for cases when a set of objects needs to be retrieved, or when the Mapper approach is not flexible enough. Scala as a JVM-based language also provides support for annotations, which can be used with Object Mapper in similar ways as Java.

Cloud technology is evolving rapidly with public cloud spending expected to reach $206.2 billion this year. Key trends to watch include hybrid cloud environments that combine private and public cloud resources for improved security, scalability, and availability; edge computing which moves workloads closer to data sources for better IoT performance; microservices architecture for agile software development and easier scaling of applications; and data portability for increased control over data and compliance with regulations. Companies adopting these trends can benefit from enhanced agility, productivity, security, and customer experiences.

This blog post discusses the count mode feature in dsbulk version 1.1.0, which allows users to count data in a table. The author provides examples of how to use this feature for various purposes such as checking if data was loaded correctly or determining the size by token ranges. Additionally, the post explains how to filter and only count specific records using the --schema.query parameter.

This post discusses the implementation of data access using Apache Cassandra and the DataStax Python Driver in a Python application called KillrVideo. The author shares examples ranging from simple to complex, including the use of cqlengine mapper for productivity boosts, as well as handling cases where the mapper couldn't fully address needs. The post also covers how to manipulate counters with the mapper and using prepared statements for paging in the Comments Service. Additionally, it touches upon the Search Service implementation using DataStax Enterprise Search for text search features.