July 2021 Summaries

The use of a combination of technologies such as Apache Cassandra, Kubernetes, Elasticsearch, Apache Kafka, Apache Pulsar, and Apache Spark can significantly increase an organization's ability to generate revenue from data and analytics. This combination is referred to as "the open data stack." However, technology alone does not guarantee success with data; successful companies also follow specific patterns and practices beyond just the tools they use. DataStax research surveyed over 500 CIOs and tech leaders to identify key attributes of these "data leaders" who derive at least 20% of their revenue from data and analytics. The findings are detailed in the report, "The State of the Data Race 2021."

This article discusses an innovative approach for processing Apache Cassandra's high-speed transactional data using tools from the RAPIDS ecosystem, which allows users to obtain analytical insights faster and more efficiently. The RAPIDS project is a suite of open source libraries designed for analytics and data science end-to-end on GPUs. It leverages common AI/ML APIs like pandas and scikit-learn and makes them available for GPU acceleration. The article explores five different approaches to make Cassandra's SSTable files available for analysis with RAPIDS, including fetching data using the Cassandra driver, converting it into a pandas DataFrame, and turning it into a cuDF. It also discusses reading SSTables from disk using Cassandra server code, serializing it using Arrow IPC stream format, and sending it to the client. The article concludes by presenting results of performance tests against datasets with varying numbers of rows and announces an open source project called sstable-to-arrow that converts data to Apache Arrow for use in analytics libraries including NVIDIA's GPU powered RAPIDS ecosystem.

The text discusses the development of sstable-to-arrow, a C++17 program that uses Kaitai Struct library for parsing Cassandra's SSTable files into Arrow data format. It aims to enable GPU analytics on SSTable data by converting each column in the table into an Arrow Vector and shipping the data to clients where it can be converted into a cuDF for further analysis. The ultimate goal is to include a read_sstable function in RAPIDS ecosystem similar to cudf.DataFrame.from_csv. Performance improvements, broadening support for different CQL types, handling large datasets are areas of continuous development. sstable-to-arrow can be run using Docker and supports saving SSTable data as a Parquet file. It is available on GitHub and accessible via Docker Hub as an alpha release. A free online workshop with hands-on examples will be held in mid August for those interested in trying out the project.

The Dynamo paper published by Amazon in 2007 sparked a revolution in distributed databases and cloud-native applications. One of its authors, Avinash Lakshman, later joined Facebook to build Cassandra, an implementation of Dynamo. As the scale problems faced by companies like Amazon and Facebook grew, so did the need for reliable and scalable data storage solutions. The release of Apache Cassandra 4.0 marks a significant milestone in database technology, with its focus on stability and compatibility with Kubernetes. Future innovations will likely involve interoperability and integration with other data infrastructure projects, making Cassandra an ideal choice for cloud-native applications.

DataStax has won the NorthFace ScoreBoard Award for its outstanding customer service and support in 2020. The company is obsessed with understanding, connecting with, and serving enterprises and developers, reflected in their products like Astra DB. They achieved an overall Customer Satisfaction rating of 9.3 (on a scale of 10) and a high Net Promoter score (NPS) of +79 (on a scale of -100 to +100). DataStax views support as a differentiator, and their commitment to customer service is evident in the positive word-of-mouth from customers.

Database-as-a-Service (DBaaS) is a cloud-based database service fully managed by a third-party provider. It includes the software, services, support, and skilled staff necessary to set up, manage, and scale databases. DBaaS allows companies to move away from on-premises databases while shifting headaches and maintenance concerns to the cloud provider. Benefits of DBaaS include saving time for development teams, simplifying database operation, monitoring, and maintenance, scaling up or down to meet demand, cost savings through pay-as-you-go model, speed of setup and provisioning, reliability due to data replication across servers, and enterprise-grade security.

DataStax Luna is enhancing its subscription-based technical support for open source Cassandra by adding consulting services at no additional cost. New and renewing subscribers of DataStax Luna Professional and Enterprise will benefit from two comprehensive health assessments, monthly technical sessions, and targeted ad-hoc advisory services. The DataStax Enterprise Success Consulting Team helps customers accelerate their ROI and lowers the inherent risks of Cassandra-backed data modernization programs. This enhancement aims to guarantee maximum performance and long-term stability from optimized Cassandra clusters.

In the DataStax Developer Newsletter, we introduce Eddie Jaoude, a popular coder with a YouTube channel full of coding tips and an inclusive Discord community. We feature his tutorial on creating a JavaScript Discord bot using TypeScript and NodeJS to make user profiles editable via chat. Additionally, we provide news links for upcoming DataStax webinars and invite developers to join the K8ssandra open source project that combines Apache Cassandra® with Kubernetes.

Dolphin is excited to announce a collaboration with Dell Technologies on a new architecture for highly available, scalable, and secure Apache Cassandra deployment via DataStax Enterprise. Built on Apache Cassandra, this NoSQL database enables enterprises to handle large and distributed datasets effectively. A newly released white paper by Dell, "Application Modernization for High-Volume Workloads with DataStax," explores how DataStax Enterprise supports application modernization and its performance on Kubernetes® platforms like Red Hat® OpenShift® and VMware Tanzu® running on Dell EMC™ PowerEdge™ servers. The architecture includes optimized DataStax Enterprise configurations created by Intel, DataStax, and Dell to test latency, throughput, and overall performance in various scenarios. The whitepaper provides guidance and best practices for deploying DataStax on Dell EMC and Intel infrastructure, offering benefits such as improved scalability, enhanced security, and increased efficiency. To learn more about these benefits and best practices, download the whitepaper or visit our solution brief. For further information on application modernization, check out Dell Technologies' InfoHub for data analytics, "5 Steps to Solving Modern Scalability Problems" Whitepaper, and the companion webinar.