January 2020 Summaries

Migrating an application designed for a relational database to Apache Cassandra involves several steps and considerations. Firstly, assess if it's time for a change by identifying performance and scalability challenges in your current relational database. Prioritize use cases for migration and aim for incremental rather than big-bang migrations. Cassandra data modeling differs from relational data modeling; denormalization is the norm in Cassandra, and tables are designed based on application workflows to contain all required information in a single query. Load testing can help determine optimal cluster size and configuration. Updating application code involves using DataStax Drivers available in popular languages to connect to DataStax Enterprise or Cassandra clusters. Developers should familiarize themselves with Cassandra's distributed nature, consistency levels, lightweight transactions, and batches. Planning the Cassandra cluster includes considering factors like node configuration, data center placement, replication strategy, and hardware requirements. Lastly, perform a one-time data migration using tools like DataStax Bulk Loader or use Apache Kafka for zero-downtime migration. Validate results using Apache Spark to compare records from the source system with those in your Cassandra cluster. Throughout this process, resources such as DataStax Academy courses and blogs can provide valuable guidance and support.

The German Federal Employment Agency (FEA) sought a solution to manage its rapidly growing data while maintaining high privacy standards. After considering various options, FEA chose Apache Cassandra, an open-source NoSQL database, for its flexibility and ability to scale with increasing data volumes. To further enhance performance and support complex search queries, the agency implemented DataStax Enterprise (DSE), along with Grafana and DSE Search. This combination has provided FEA's IT staff with agility and responsiveness in monitoring large amounts of data while ensuring scalability for future growth.

DataStax has released its first version of DSBench in DataStax Labs, a tool designed for model validation, performance and scale testing for Cassandra and DSE. It allows users to run tests against DataStax Enterprise (DSE) or Apache Cassandra, validate data models, incorporate scale testing into continuous integration practices, and more. The tool is free and aims to be accessible to everyone with a common set of concepts and standard configuration format. DSBench focuses on workloads - data, operations, and access patterns - and doesn't require compilation or large amounts of test data. It also has built-in instrumentation that can integrate with most metrics systems. DataStax encourages users to provide feedback for future improvements.

GraphQL is a popular query language that solves common problems faced by graph databases, particularly in terms of ease-of-use. Despite this, it's not a graph database query language. The global community has shown an increasing interest in easy-to-use query languages for solving graph problems over the past five years. Open source software (OSS) is vital to the industry, but its sustainability is threatened by the fact that most contributions come from a few individuals working for free. Companies like Snowflake have proven that offering a free access tier can be profitable. The future of the industry will likely involve creating more accessible technologies and encouraging collaboration between academia and industry.

Locstat, a South African software company, has developed an AI and event-processing solution for real-time data analysis and visualization. The company's technology focuses on deep analysis and predictive analytics of sensor data from IoT devices, supporting various use cases such as high-volume transaction monitoring, fraud detection, customer 360, recommendation engines, and more. To support these complex use cases, Locstat chose DataStax Enterprise (DSE) built on Apache Cassandra™ for its scalability, performance, and compatibility with hybrid cloud environments. By leveraging DSE Graph and DataStax Analytics, Locstat can deliver innovative IoT solutions like geospatial risk management tools and real-time rules engines.

Containers have become popular due to their ability to enable software teams to ship better products faster and more efficiently. However, they introduce complexities of their own, which is why Kubernetes, an open-source container orchestration platform, has gained popularity among development teams. Despite its benefits, Kubernetes also introduces additional complexities. To address this issue, DataStax built the Kubernetes Operator to simplify the process of describing many lower-level Kubernetes components and provide a logical interface for describing an application. The operator automates deployment tasks such as managing individual Kubernetes resources for a cluster and handling configuration changes in a rolling fashion. This allows administrators to focus on more important aspects of their work, while ensuring that no system slips through the cracks during configuration, startup, or deployments stages.

DataStax Accelerate conference is approaching and the call for papers will close soon (January 29). This year's event will take place in San Diego and London, aiming to bring together the Cassandra community and provide a platform for sharing knowledge. Community members are encouraged to contribute their stories of problem-solving experiences with Apache Cassandra. The conference seeks diverse perspectives and unique contributions from its participants. Contributions can range from code to ideas, validation, and inspiration. By sharing their experiences, contributors can positively impact the community and help others succeed in similar situations. Submit your talk proposal here: [link].

The text discusses an eBook by DataStax titled "Getting Started with NoSQL and Apache Cassandra®". It highlights how to migrate to Apache Cassandra, a distributed, elastically scalable, highly available and fault-tolerant platform. Applications that require high security, resiliency like eCommerce apps, time-series based applications, those with high data volumes such as IoT use cases or real-time transaction-based ones are ideal candidates for this migration. The text also mentions DataStax distributions which expand on Cassandra's benefits with advanced security, performance, multi-model, and operational management capabilities. It introduces the concept of a graph database option provided by DataStax called DataStax Graph, which leverages all the benefits of Cassandra for building complex data models representing relationships between people, products, interactions, transactions etc., essential for applications that glean insights from connected data like fraud detection, supply chain optimization, social network analysis and customer-facing recommendation engines. Unlike relational databases, graph databases do not require foreign keys or JOIN operations as data is retrieved by traversing the graph. The text ends with an invitation to download the full eBook or tune in next week for another excerpt.

The text is an excerpt from a DataStax eBook titled "Getting Started with NoSQL and Apache Cassandra™". It discusses how to migrate to Apache Cassandra™ and build powerful applications using it. CQL (Cassandra Query Language) is introduced as a fast way to start working with Cassandra, especially for those familiar with SQL. The text also highlights key similarities and differences between CQL and SQL, such as the way data deletion works in Cassandra compared to an RDBMS. It mentions that Apache Cassandra is designed for high write throughput and uses SSTables (immutable once written) where a delete operation is actually an update and updates are inserts into new SSTables. The text also notes that similar to SQL, permissions and resources can be controlled in CQL.

The `count(*)` function in Apache Cassandra returns the number of rows in a table, but it is expensive due to the full table scan required. This can be problematic as Cassandra tables are distributed across multiple nodes. There are alternative methods for counting rows that do not require running the `count(*)` function, such as using secondary indexes or executing queries with specific WHERE clauses. These alternatives can save resources and still provide accurate results. DataStax Academy offers courses to enhance Cassandra skills further.

DataStax has unified its OSS and DSE drivers under a single Apache License v2.0 open-source DataStax driver, providing additional functionality for all users. This includes Spring Boot Starter, Java Reactive Programming support, Python wheels, and improved load balancing policy. The upgrade process is simple, requiring only an update to the latest version of the drivers. Support for DSE drivers will continue until January 1st, 2022, with all new features and functionality being incorporated into the unified DataStax driver.

Many database administrators face challenges when their developers fail to understand Apache Cassandra, a NoSQL database known for its masterless architecture, high availability, and linear scalability. To address this issue, attending workshops on data modeling and app development using Cassandra is recommended. DataStax offers upcoming workshops in New York City, London, Paris, and Austin where participants can learn the basics of Cassandra within an hour and write code in their preferred programming language. These workshops aim to increase developer productivity, keep database administrators happy, and deliver better applications to users.

Saab Medav Technologies, a global provider of military and civil security products, services, and solutions, has implemented DataStax Enterprise (DSE) as its new database solution for its Information Fusion System (IFS). The IFS platform collects, analyzes, stores, and displays data from multiple sources into customizable reports. Saab chose DSE due to its flexibility, allowing the addition of DSE Graph, DSE Search, and DSE Analytics to enhance the functionality of the IFS platform. This has enabled Saab's customers to analyze millions of records daily, making strategic decision-making more efficient. The adoption of DSE has also reduced complexity and administrative overhead for Saab, leading to increased scalability and value for its customers.

The text discusses the ease of transitioning from relational databases to Apache Cassandra™ NoSQL projects. It highlights that while there are new concepts to learn, many similarities exist between RDBMS and Cassandra. Additionally, it mentions the availability of numerous resources such as DataStax Academy, documentation, presentations, blogs, webinars, and supportive communities to aid in this transition.

Ed is joining DataStax with a mission to create innovative products that exceed current expectations. With experience in market disruption through product innovation, he aims to impact the $60 billion database market. Customers seek a robust NoSQL platform for modern applications, demanding features like breakthrough developer productivity, zero downtime, no vendor lock-in, and infinite scale. Ed will serve as Chief Product Officer at DataStax, with upcoming developments expected in the coming months.

In a new on-demand webinar, What's Ahead in Data Management in 2020, Philip Russom explores the trends affecting data management, such as analytics, geospatial search, smart data governance, and hybrid/multi-cloud environments. Despite these advancements, many organizations still struggle with legacy systems, data silos, and managing large volumes of data from IoT devices. As 5G networks emerge, the challenge of navigating our increasingly data-driven world becomes more complex. The webinar aims to provide insights on how to manage this challenge effectively.