Elastic Training helps UK Driver and Vehicle Licensing Agency better serve motorists

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.


Service monitoring and availability made simple with Elastic Uptime and Heartbeat

In the world of IT, availability can mean a lot of things. Your website is available if it is up, responding in a timely manner, sending the correct headers, and serving a valid certificate. Your network is available if the correct hosts are online, responding to ICMP pings, and responding to TCP requests on specific ports. Your API endpoint is available if it returns the correct values when sent specific requests.


Optimizing costs in Elastic Cloud: Availability zones and snapshot management

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.

Technical deep dive into Elastic Agent + Ingest Manager

This talk will dive into the technical details behind the recently announced Elastic Agent + Ingest Manager. After a quick overview of all the components involved and a demo, we explore how all the parts work together behind the scene. Some noteworthy parts to trigger your interest are "new indexing strategy", "constant_keywords", "datastreams" and a few more.

Structuring Elasticsearch data with grok on ingest for faster analytics

As well as being a search engine, Elasticsearch is also a powerful analytics engine. However, in order to take full advantage of the near real-time analytics capabilities of Elasticsearch, it is often useful to add structure to your data as it is ingested into Elasticsearch. The reasons for this are explained very well in our schema on write vs. schema on read blog post, and for the remainder of this blog series, when I talk about structuring data, I am referring to schema on write.


Building a Python web application with Elastic App Search

This post is a brief summary of a presentation I gave recently where I deploy Elastic App Search, show off the ease of setup, data indexing, and relevance tuning, and take look at a few of the many refined APIs. It’s also written up in a codelab with step-by-step instructions for building a movies search engine app using Python Flask. The app will work on desktop or mobile and is a fast, simple, and reliable way to query the information.

Powering Khoros Community Platform using Elasticsearch

At Khoros, we provide a platform for brands to build a community around their customers. Behind the scenes, this community platform is powered by Elasticsearch for operations such as free text search, fetching data for our custom query language, and building customizations. Some of the biggest communities have millions of users and greater than 100 million documents. Come and take a look into how we index these millions of documents in a reliable and efficient way to power our community platform!

Optimizing costs in Elastic Cloud: Replica shard management

This is part of our series on cost management and optimization in Elasticsearch Service. If you’re new to the cloud, be sure to think about these topics as you build out your deployment. If you are yet to start, you can test out the content here by signing up to a 14-day free trial of Elasticsearch Service on Elastic Cloud.


Protect your Elasticsearch deployments against attacks like "meow bot" - for free

The issue of unsecured databases is growing. In 2019, 17 percent of all data breaches were caused by human error — twice as many as just a year before. And the IBM/Ponemon 2019 report found that the estimated probability of a company having repeated data breaches within two years grew by 31 percent between 2014 and 2019. Why is this happening?