As work from home has ballooned in 2020, virtual methods for communicating with colleagues have become more critical than ever. Same goes for all the useful productivity and collaboration tools at our disposal. The emerging downside is the difficulty of finding needed information among so many tools. Compounding the problem is the tendency for info to get siloed off by department.

We recently announced the general availability of our Elasticsearch Service API. APIs help to automate tasks such as creating and scaling deployments, integrating with existing workflows, and testing. The Elasticsearch Service API supports the Open API Specification, which allows you to use tools like Swagger to generate software development kits (SDKs) in any programming language. You can import the API spec onto Postman and create a Postman Collection to create a test suite.

In a previous blog, we saw that the seemingly simple job of an Elasticsearch client — moving data between the calling code and the cluster — is actually quite complicated under the hood. Naturally, as much as we try to make the default behaviour of the client optimal for the majority of scenarios, there are situations where you want to configure, customize, or enable/disable certain features.

The official Go client for Elasticsearch is one of the latest additions to the family of clients developed, maintained, and supported by Elastic. The initial version was published early in 2019 and has matured over the past year, gaining features such as retrying requests, discovering cluster nodes, and various helper components. We also provide comprehensive examples to facilitate using the client.

We recently released our new training Quick Start guides for the products in the Elastic Enterprise Search solution: Elastic Workplace Search and Elastic App Search. Each product is built on the Elastic Stack, so you can enjoy its speed, scale, and relevance without the heavy development and maintenance requirements of building your own search solution. Each 15-minute video tutorial provides everything you need to start creating powerful search experiences for your workplace, websites, and apps.

In Elasticsearch 7.9, we’ll be introducing a new “wildcard” field type optimised for quickly finding patterns inside string values. This new field type addresses best practices for efficiently indexing and searching within logs and security data by taking a whole new approach to how we index string data. Depending on your existing field usage, wildcards can provide: The most exciting feature of this new data type is its simplification of partial matches.

The ELK stack is an industry-recognized solution for centralizing logging, analyzing logs, and monitoring your system use and output. However, the challenges of maintaining your own stack overcoming common Elasticsearch problems need to be considered. The popularity of the ELK Stack can be boiled down to two, key tenets. First, the management and analysis of logs is an incessant issue for developers, SMBs, and enterprises alike.

The core responsibility of the UK's Driver and Vehicle Licensing Agency (DVLA) is to maintain more than 48 million driver records, more than 40 million vehicle records, and to collect approximately £6 billion ($7.75 billion) a year in Vehicle Excise Duty. The agency is at the forefront of public digital services, and has made significant progress in transforming its IT systems into new cloud-based platforms.

Welcome to another blog in our series on cost management and optimisation in Elasticsearch Service. In previous installments, we looked at hot-warm architecture and index lifecycle management as ways of managing the costs associated with data retention and at managing replicas as a means of optimising the structure of your Elasticsearch Service deployment. Be sure to check out the other blogs in the series for additional tips to help you as you build out your deployment.

Elasticsearch is a complex piece of software by itself, but complexity is further increased when you spin up multiple instances to form a cluster. This complexity comes with the risk of things going wrong. In this lesson, we’re going to explore some common Elasticsearch problems that you’re likely to encounter on your Elasticsearch journey.