May 2019 Summaries

The talk discusses ways to mitigate super nodes and partitioning strategies in graph databases. It is vendor-neutral, despite the speaker working at DataStax. The presentation covers definitions of super nodes, vertices, adjacent vertices, and neighbors. Super node problems include query performance traversals and storage retrieval issues. The talk provides valuable insights on how to query, design, and set up data in graph databases effectively while avoiding common pitfalls when designing a schema for graph data.

DataStax introduces its new cloud platform, Constellation, designed to enhance the Database as a Service (DBaaS) market with sophisticated capabilities at a low cost. The platform delivers services that simplify building modern database applications in the cloud. Constellation's first two services are DataStax Apache Cassandra as a Service and DataStax Insights. The platform supports Google Cloud Platform, Amazon Web Services, and Microsoft Azure. Constellation offers advanced features such as auto-management, self-healing functionality, and AI-powered performance optimization. It is also more resource-efficient than other DBaaS offerings, resulting in cost savings for users.

DataStax has announced its new cloud data platform, Constellation, designed to simplify hybrid and multi-cloud data management. The platform offers the power of DataStax solutions with push-button simplicity, allowing customers to rapidly develop cloud applications with on-prem compatibility. Constellation's masterless architecture enables fast application development regardless of infrastructure configuration or scale and performance needs. The first offerings on Constellation will be DataStax Apache Cassandra as a Service and DataStax Insights, a hosted performance monitoring solution.

Kesque, an AI company, has chosen Apache Pulsar over Apache Kafka for building its messaging infrastructure service due to several reasons. Firstly, Apache Pulsar is a two-in-one solution that can handle high-rate real-time use cases like Kafka and also supports standard message queuing patterns. Secondly, unlike Kafka, Apache Pulsar doesn't require partitions for all topics, making it simpler to use in some scenarios. Thirdly, Apache Pulsar avoids the problem of copying large logs by breaking them into segments, which are distributed across multiple servers using Apache BookKeeper as its storage layer. This makes failure scenarios easier to handle and scaling out a breeze. Fourthly, Apache Pulsar brokers are stateless, allowing for quick start-up times and easy scalability. Lastly, geo-replication is a first-class feature in Pulsar, making it suitable for globally distributed applications or disaster recovery scenarios. Additionally, benchmark tests have shown that Pulsar delivers higher throughput along with lower and more consistent latency compared to Kafka.

Participants can enter a contest for a chance to win a DataStax Swag Bag by following @DataStax on Twitter and attending DataStax Accelerate 2019. The contest involves participating in the #Accelerate19Games Selfie Scavenger Hunt, where selfies with items from a provided list must be posted on Twitter using the hashtag #Accelerate19Games. The contest ends at 12:30pm PST on Thursday, May 23rd. Employees of DataStax are not eligible to win a prize.

Kesque, a company that focuses on empowering developers to build cloud-native distributed applications, chose Apache Pulsar over Apache Kafka for its messaging infrastructure service. The reasons include Pulsar's ability to handle high-rate real-time use cases like Kafka and support standard message queuing patterns, its simplicity in managing topics without partitions, efficient log management through segmentation, stateless brokers that allow easy scaling, built-in geo-replication, consistent performance, and a fully open-source project under the Apache umbrella.

The CAP theorem states that a distributed database cannot guarantee consistency, availability, and partition tolerance simultaneously. In practice, databases tend to be either CP (prioritizing consistency over availability) or AP (prioritizing availability over consistency). Apache Cassandra is usually described as an AP system, ensuring data availability at the expense of consistency. It maintains tunable consistency levels for read and write operations, allowing users to specify the number of replicas that need to respond for a successful operation. A higher consistency level ensures accuracy but impacts latency. Developers can configure these behaviors individually for each operation, managing trade-offs between consistency, availability, and performance.

Securing data in modern applications is crucial due to the rising number of security breaches and their severe consequences on brand reputation and customer trust. To protect distributed systems like intercloud, hybrid, or multi-cloud architectures, consider implementing various security practices such as securing environments, encrypting data and communication channels, using TLS over SSL for encryption, managing encryption keys, employing authentication methods like multi-factor authentication, planning role-based access control, restricting access to system schemas, and separating duties among different teams.

As of February 1, 2020, DataStax will no longer sell DataStax Distribution of Apache Cassandra. For enterprises interested in support for Apache Cassandra, DataStax Luna is available. The text provides a step-by-step guide on how to create a simple Java application using version 3.7.1 of the DataStax Java Driver for Apache Cassandra®. It covers creating a Cluster object, a Session object, executing statements, and running the application. Additionally, it mentions upcoming webinars and comprehensive documentation for further learning.

Open source software plays a crucial role in the success of hybrid cloud environments, as evidenced by its widespread adoption among leading organizations such as Netflix, Apple, and the US government. The open source market is expected to grow significantly, reaching nearly $33 billion by 2022. Hybrid cloud computing combines infrastructure from multiple platforms and data centers, allowing businesses to maximize benefits while minimizing costs. DataStax offers an active everywhere database for hybrid cloud environments, utilizing the power of Apache Cassandra®, an open source distributed NoSQL database. Open source software provides a range of lower-cost options for transitioning to hybrid cloud, with free training available on platforms like DataStax Academy and free downloads of certain distributions.

Apache Spark is a popular big data tool that offers fast processing and an easy entry point into machine learning. However, adding any technology to your stack comes with costs, so it's essential to consider whether using Spark is the best solution for your specific needs. Some factors to consider include the complexity of your data model, the frequency of ad-hoc report generation, and the need for a JDBC endpoint. In some cases, simpler solutions like DataStax Bulk Loader may be more appropriate. To learn more about when it's appropriate to use Spark or not, visit DataStax Accelerate.

DataStax Accelerate is an upcoming event featuring numerous informative talks and speakers. For developers, the "Building Modern Applications" track offers discussions on building applications with various languages and technologies such as Spring Boot in Java, Go, microservices in Python, and serverless functions. Other topics include designing for time series applications at scale, architectures with Kafka and Cassandra, data modeling, leveraging DataStax Enterprise to build a feature store, and data as a service. This event aims to provide valuable knowledge from top experts in the Apache Cassandra world.

In this post, the author discusses writing business logic and reading/writing data to DataStax Enterprise (DSE) using Cassandra Query Language (CQL). They share their experience building a Python implementation of the KillrVideo microservice tier. The author explains that there is not much business logic in the KillrVideo service tier, mainly validation code. They describe how they implemented the User Management Service and used DataStax Enterprise Python Driver to connect to DSE. The post also covers setting up configuration values associated with the driver in a JSON file. Finally, the author discusses using entity classes that define types mapped to Cassandra tables and implementing methods for retrieving user account data.

Modern applications and hybrid cloud are buzzwords in the database community that complement each other for digital transformation. Modern apps, consisting of microservices, offer five foundational attributes (CARDS) and benefit from the organizational agility, security focus, and lean efficiency provided by a hybrid cloud environment. Hybrid cloud supports dynamic needs of modern applications and enables agility in enterprise operations. The combination of modern apps, hybrid cloud, and an active everywhere database accelerates time to market for modern applications while addressing potential data management challenges.

Apache Cassandra 4.0 is nearing its official release after two years of community effort. DataStax Accelerate, a gathering of Cassandra professionals, will provide insights into the project's progress and updates from committers, experts, and power users. Nate McCall, Apache Cassandra Project Chair, will deliver a technical keynote on Thursday morning. The event also features an "Innovating with Apache Cassandra" track that delves into key features of the upcoming release and includes presentations by companies like Yahoo Japan, T-Mobile, Instagram, Bloomberg, and The Last Pickle.

Starting February 1, 2020, DataStax will no longer sell DataStax Distribution of Apache Cassandra but offers DataStax Luna for enterprises interested in Apache Cassandra support. Many organizations use open source databases and hybrid or multi-cloud to deliver modern applications with Apache Cassandra. DataStax Distribution of Apache Cassandra is a production-ready, 100% open source compatible database supported by DataStax experts. The offering on Azure simplifies building, deploying, and scaling Cassandra-based applications in the cloud. DataStax has contributed significantly to Apache Cassandra development and provides support for developers using this technology. Open source Cassandra is easy to adopt but may require increased support as deployments scale. DataStax Distribution of Apache Cassandra offers unique support experience unmatched in the market, including design, configuration, management, scaling, optimization, security, monitoring, integration assistance, and advice on running Cassandra in production.

The text discusses the author's excitement about attending their first DataStax conference, DataStax Accelerate. They are particularly interested in presentations that address challenges faced by customers when scaling solutions. One talk they look forward to is Max Melnick's discussion on using DataStax Enterprise (DSE) Analytics and Graph for massive-scale entity resolution of data across domains. The author is curious about the problems Max faced, the solutions deployed, and lessons learned. They also mention that there are many other presentations covering various industries and use cases at the conference.

DataStax Accelerate conference is fast approaching and features high-profile speakers from leading brands such as Netflix, Facebook, Yelp, Deloitte, Capital One, Walgreens, and T-Mobile. The event will take place at the Gaylord National Resort & Convention Center in Maryland on May 21-23. A key talk to look forward to is Rapid Cloud Development with Spring Boot and Cassandra by DataStax's Frank Moley. This session will provide a hands-on real-world example of using Spring Framework and Apache Cassandra for rapid development, which is crucial in today's fast-paced business environment. The conference promises to be one of the best developer conferences of the year, offering valuable insights into cutting-edge technologies and their applications in real-world scenarios.

Apache Cassandra is a popular distributed database system known for its multi-data center replication, fault tolerance, and tunable consistency. DataStax Accelerate, the premier Cassandra conference, will take place near Washington, D.C., from May 21–23. The event aims to bring together Cassandra enthusiasts to learn about new features, share ideas, and network with industry professionals. Sessions at the conference will cover topics such as Yahoo! Japan's use of Cassandra, the upcoming release of Apache Cassandra® 4.0, performance optimization techniques, integration with Kafka, reactive programming, and real-world case studies from companies like Instagram and FamilySearch.

The text discusses the challenges faced by data architects in building real-time, flexible, and scalable data architectures within modern enterprises. It highlights that single technology solutions are often insufficient to meet customer needs, especially when multiple teams are involved. Many customers use DataStax Enterprise and Confluent Enterprise as part of their tech stack. The upcoming DataStax Accelerate conference will feature discussions on these issues and the interplay between Apache Kafka and Apache Cassandra, which are considered critical building blocks for modern applications. Cliff Gilmore from Confluent will be presenting a session on this topic.

Kubernetes is a rapidly evolving technology that simplifies database operations. The introduction of StatefulSets has made managing stateful applications in Kubernetes easier, particularly for globally distributed Cassandra clusters. As companies aim to standardize their deployments across clouds and on-premises data centers, the significance of running databases on Kubernetes is growing. DataStax Accelerate conference offers valuable insights and lessons from experts like Mike Treadway, a principal architect at IBM, who will share his knowledge about deploying Cassandra clusters on Kubernetes in the cloud. The event features numerous operator-oriented and Kubernetes talks with speakers eager to share their experiences and learnings about DataStax Enterprise and Cassandra.