July 2015 Summaries

To delete all records from a Couchbase bucket that have a key starting with a particular value, create a Couchbase view and execute a range query using the Couchbase Node.js SDK. The process involves creating a development view through the Couchbase Dashboard or by code, then connecting to a locally hosted Couchbase instance and opening a specific bucket in a Node.js application. A ViewQuery object is created from the design document and view, with a range query specified to capture documents with keys starting with a particular prefix. The results are looped through and each document is deleted one by one using the `remove` method, which is non-blocking and allows for concurrent deletion. This approach can be applied to Couchbase server 2.5 and Java client 1.4 as well, but requires modifications to the Java code. Another alternative is to set a random TTL on each object between now and X days from now, allowing the Couchbase cluster to handle deletion with reduced load.

The NoSQL Now Conference will take place on August 18th to 20th in San Jose, CA. Plushcap will be presenting a talk titled "SQL Gene in NoSQL: How the New SQL Extensions are Making Flexible Data Models Query-able". The presentation focuses on the role of SQL in NoSQL databases and explores how new SQL extensions, such as N1QL, enable real-time interaction with big data.

In the developer preview for the 2.2.0 release of the Couchbase SDK, several enhancements have been introduced, including extended support for N1QL and Multi-Dimensional Scaling (MDS), improvements to Sync and Async APIs, and supportability enhancements through metrics. The N1QL DSL functionality has been expanded to include a wide variety of functions, while MDS now supports Memcached buckets more effectively. The APIs have been modified to ensure consistency, with "parameterized" queries being standardized across documentation. The transition from "hot" to "cold" observables aims to improve retry mechanisms, and new overloads in the synchronous API provide greater flexibility. Additionally, always-on latency and runtime metrics have been added to the SDK to aid in application monitoring, and various dependency upgrades have been implemented. The team is also working on extending the Kafka Connector and has made changes to the DCP in the core-io library, with further improvements and documentation planned as the release approaches general availability. Feedback from users is encouraged to refine these updates and ensure a successful full release.

The text provides a detailed guide on setting up a sample travel application using Couchbase Server and Spring Boot for Java, which was demonstrated at Couchbase Connect 2015. It outlines the prerequisites, including Apache Maven, JDK 1.7, Couchbase Server 4.0, and an IDE like IntelliJ IDEA. The process involves creating a new Java project, configuring Maven dependencies, setting up a Couchbase cluster and bucket, and implementing RESTful APIs for user registration, login, and flight information retrieval. It also covers handling Cross Origin Resource Sharing (CORS), creating database interaction classes, and testing the API endpoints using cURL. The guide concludes by suggesting the potential for integrating a front-end using frameworks like AngularJS, with the full project available on Couchbase Labs' GitHub channel.

In today's rapidly evolving economic landscape, businesses must be adaptable and flexible to survive and thrive, much like a chameleon that changes its colors to blend into its environment. The shift towards a digital, information-driven age demands that companies, particularly established ones, place data management at the core of their operations to effectively engage and respond to customer needs. While newer companies may naturally align with this digital shift, traditional businesses often face challenges in adapting, as illustrated by the struggles of companies like HMV and Woolworths. These "dinosaur" organizations have learned that to avoid obsolescence, they must integrate digital innovations and rethink their approach to data, emphasizing scalability and speed. Future-proofing through strategic investment in flexible data platforms is crucial for maintaining relevance and meeting the accelerating demands of a global customer base.

Banks and financial institutions are struggling to keep pace with the demand for mobile banking services due to outdated infrastructure that was originally designed for simpler tasks. As customers increasingly use mobile applications multiple times a day, the reliance on old mainframes has led to performance issues and a strain on resources. This has forced banks to focus on maintenance rather than developing new services, risking customer dissatisfaction and potential loss of business. To remain competitive and meet customer expectations, banks must invest in modern infrastructure that can handle dynamic data scaling and ensure consistent performance, allowing them to offer enhanced services and future-proof against the ongoing digital transformation in the financial sector.

In 2015, the proliferation of connected devices, termed the "internet of everything," reached approximately 20 billion, revolutionizing various industries, especially the automotive sector, which is now a major generator of big data. As cars transform into intelligent, connected devices, they produce vast amounts of data useful for insurers and advertisers, but this requires the automotive industry to upgrade its data infrastructure quickly. The increasing diversity and volume of data present both challenges and opportunities for manufacturers, necessitating the use of non-relational (NoSQL) databases over traditional SQL databases due to their flexibility, scalability, and speed. NoSQL databases are particularly well-suited for managing dynamic and unstructured data, enabling manufacturers to handle big data efficiently without detracting from their core expertise in car production. This adaptation is crucial for maximizing profit while minimizing investment in response to evolving market demands.