Allison Richardet

Last night, I helped my child with homework: we learned about word forms. We worked through the word "metal", including "metallic", "metalling" and "metalled." While this is an elementary school topic, it struck me: this is a problem we solve as engineers building search systems. If a user searches for "metallic", they should find documents containing "metal." Likewise, if someone queries "running", they should also be presented with results associated with "run."

Welcome to the world of text analysis. We will walk through an example using word forms and how Lucenia helps with this analysis to improve search.

Theme parks present unique challenges when it comes to ensuring guests with the best experience. From the massive numbers of guests to countless experiences and restaurants, theme parks require technology that centralized computing alone struggles to address. Theme parks are an ideal use case for edge computing: with daily visitor counts above 100,000 during peak seasons, and the need for sub-second response times for location-based queries. In this blog, we will look at the benefits of edge computing in theme parks and take advantage of Lucenia's search capabilities.

This tutorial will guide you through the process of building a log aggregation pipeline with Lucenia and FluentBit. We will start by setting up our services with docker compose, and then utilize OpenSearch Dashboards to visualize our data.

In Part 1 of this series, we discussed how Lucenia and FluentBit are transforming how DevOps teams handle log management. Today, we will work with a local demo to get hands-on experience. We are building a foundation for production-grade log pipelines prepared to handle billions of events daily.

Picture this: it's your 2 am wake-up call! Your phone is buzzing with a series of non-stop alerts as your platform experiences failure after failure. You are drowning in a sea of log files scattered across legacy systems and newly implemented microservices.​

Sound familiar? If you're a DevOps engineer, you know raw log files are a source of misery.​

The challenge isn't just collecting logs; it's making sense of them. Today's distributed systems generate a chaotic stream of unstructured data that is difficult to understand and act upon. At Lucenia, we aim to transform this noise into actionable insights - both for organizations' engineers and AI agents. Let's talk about how to turn raw log data into a proactive, actionable, and intelligent observability system.

Opensearch is a popular search engine. With the recent introduction of Lucenia Search, developers are considering migrating their data from OpenSearch to Lucenia Search to take advantage of features such as simplified search, cost efficiency, and security needs.

This tutorial will guide you through the process of migrating your data from OpenSearch to Lucenia Search. In just a few steps, we will take advantage of storing our data in a Lucenia Cluster. To start off, we will create an OpenSearch cluster using docker compose. Then, migrate our data from OpenSearch to Lucenia Search.

Wherever you are with your search needs, this blog post will provide you with valuable insights and guidance on migrating your data from OpenSearch to Lucenia Search.

Let's Migrate!

In today's rapidly evolving digital landscape, 82% of organizations using cloud infrastructure report managing cloud spending is their top challenge. Consequently, the ability to do more with less is a crucial differentiator for businesses and the number one tenet of Lucenia's search offering. Cloud-native practices have emerged as a cornerstone for organizations looking to maximize scalability and efficiency by optimizing their operations and reducing costs. At the same time, search technologies like Lucenia have become essential tools for businesses looking to unlock the value of their data. Cloud-native design encourages cost reduction and scalability and enables companies to focus their resources where they can have the most significant impact.