In this session you’ll learn how to perform functional, scalability, performance, and reliability tests on your Couchbase Mobile application. James will demonstrate how to test query, synchronization, conflict resolution, and security. You’ll also see how to test the scaling capabilities of Sync Gateway, the performance of Couchbase Lite and Sync Gateway, and the high availability and disaster recovery capabilities of Sync Gateway. Next, you’ll learn how to install, upgrade, and scale Couchbase Mobile. Traun will cover the end-to-end process for deploying your Couchbase Mobile application.
Cars.com selected Couchbase as our NoSQL database solution in late 2015, and in 2016 we delivered our first projects into production. We will share the story of our decision to adopt a NoSQL solution, our vendor selection process, and the steps we took to deploy the platform and release the first projects. We will conclude with a description of how we intend to evolve the Couchbase platform in the coming year.
Arrays can be simple; arrays can be complex. JSON arrays give you a method to collapse the data model while retaining structure flexibility. Arrays of scalars, objects, and arrays are common structures in a JSON data model. Once you have this, you need to write queries to update and retrieve the data you need efficiently. This talk will discuss modeling and querying arrays. Then, it will discuss using array indexes to help run those queries on arrays faster.
Couchbase helps some of the world’s biggest companies handle their heaviest workloads. Taking real-world examples we’ll explain how to prepare your clusters and applications with scalability and resilience in mind. We’ll talk about what real customers have done with Couchbase to ensure their most important periods manage to make the headlines for all the right reasons.
This session kicks off the Couchbase Connect Big Data track by answering some fundamental questions about the relationship between NoSQL and the other major Big Data technologies: Hadoop, Spark, and Kafka. Each technology addresses the “big data” challenge, but they target different parts of it. We’ll get concrete about where it makes sense to deploy NoSQL versus where it makes sense to deploy Hadoop, Spark, and Kafka. More importantly, we’ll discuss how NoSQL and the other Big Data technologies complement each other and why they’re stronger together.
NoSQL systems allow for rapid development using flexible models through schema-less documents. However, these models still need to be defined to enforce consistency within the domain. Conceptual, logical, and physical data modeling is important, and often overlooked in the development process, regardless of the database platform. Once the models for a domain have been defined, data is then needed to start building the application. Have you ever spent time writing throw away code to generate or import a dataset? In this session, Aaron will cover how Shop.com/Market America uses Couchbase Server and Couchbase Mobile, including learnings on data modeling fundamentals and different ways to rapidly generate vast amounts of random / fake data to test models.
N1QL gives developers and enterprises an expressive, powerful, and complete language for querying, transforming, and manipulating JSON data. In addition to SELECT, you have INSERT, UPDATE, DELETE, MERGE statements to modify many documents with a single statement. This talk will teach you how these operations work, and provide ten application use cases to demonstrate effective use of these statements.
Cvent, Inc. is the world's leading provider of cloud-based software for meetings and event management. From online event registration to meeting site selection to web surveys, we manage it all. Couchbase is a key technology in our next-generation software platform with eight clusters deployed. But to maintain fast go-to-market for our features, deploying using microservices is a must. This is for not just application changes but changes to the database itself. In this session, I will go through how to manage a deployment pipeline for microservices. I will also cover best practices, configuration, development, index management, and deployment from our real-world experience.
Since being open sourced, Apache Kafka has been widely adopted by organizations ranging from web companies like Uber, Netflix, and LinkedIn to more traditional enterprises like Cerner, Goldman Sachs, and Cisco.
These companies use Kafka in a variety of ways:
1) As a pipeline for collecting high-volume log data to load into Hadoop.
2) As a means of collecting operational metrics to feed monitoring/alerting applications.
3) For low-latency messaging use cases.
4) To power near real-time stream processing.
In this talk you will hear how companies are using Apache Kafka, learn how its unique architecture enables it to be used for both real-time processing and as a bus for feeding batch systems like Hadoop, and explore where it fits in the Big Data ecosystem.