Distributed Data Chicago
Events
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PostgreSQL Enterprise Features
PostgreSQL has seen increased enterprise adoption over the last years. While it has always been considered a rock-solid and secure data base system, recent years brought native replication and partitioning, JSON support as well as increasing federation to other databases or data sources. Operational features like high availability, horizontal and vertical scalability or zero-downtime upgrades can be achieved natively and/or via external projects. This webinar will present the most important enterprise-related features currently implemented in PostgreSQL and provide examples on how use them. Those include: Predictable major and patch releases, long support timeframes Fault tolerance and data consistency Enterprise relevant
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Building Your First Cadence Workflow with Java and Go
Cadence, open sourced by Uber under the MIT license, is a service and programming framework that greatly simplifies the development and operations of workflow-style business logic while supporting the most demanding availability, reliability, and scalability requirements. By reconstituting the application state following a hardware or software failure, Cadence allows the creation of applications that are truly fault-oblivious. In this webinar, we'll work through step by step getting your development environment set up and building a basic workflow in Cadence. This webinar is targeted at people who are familiar with developing in the but who have no experience building applications with
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Apache Kafka® or Amazon Kinesis, which technology is right for you?
Data streaming technologies are essential for modern organizations that need to process and analyze large amounts of data in real time. In this webinar, we will compare two of the most popular data streaming solutions: Apache Kafka® and Amazon Kinesis. We will evaluate their technical features, pricing, and performance to help you determine which technology is best suited for your specific use case and business needs. The webinar will provide an in-depth analysis of the capabilities and limitations of Apache Kafka and Amazon Kinesis. We will discuss their architecture, scalability, and reliability, as well as their support for different data
