Wes Richardet

Businesses use AI to find insights, but there's a problem: AI models need context to thrive. Without understanding your specific business domain, AI always gives you an answer but these answers are often useless. Your institutional knowledge, built over the years, is hard to feed into a generic AI model.

Think about it. Your business has unique processes, special terms, and specific workflows that define how you operate. Historical patterns, customer preferences, and regional requirements shape every decision you make. All this knowledge exists in different places, some in databases, some in documents, and some in your employees' heads.

Generic AI models don't know these things because they're trained on general data and give general answers. You need specific answers that match your reality. Lucenia solves this by giving AI the context it needs through hybrid search capabilities. It connects AI to your actual business data, your specific domain knowledge, and your real operational context.

In our data driven world, having great search capabilities is crucial for a positive user experience and smooth operations. We've all had frustrating experiences with search results in our daily lives. Either the results aren't what we need, loading times take forever, or we can't find what we're looking for in large datasets. It can be a real headache! This raises the question, what does "great search" really look like? It's more than just fast benchmarks and keyword matching. Great search is intelligent, lightning fast, cost effective and secure by default all while integrating with your existing infrastructure. Building this doesn't have to be a slow and cumbersome process. Lucenia delivers an AI Search Engine for private clouds and enables you to build and deploy superior search capabilities with remarkable speed.

Apache Parquet has become a popular format for modern data lake architectures, and it is widely used in Apache Iceberg, Delta Lake, and other analytics-driven storage solutions. Its columnar storage format is designed for high-throughput analytical queries, making it a reasonable choice for batch processing, aggregation-heavy workloads, and structured data exploration. However, when it comes to powering search applications - where low-latency retrieval, relevance ranking, and hybrid search (numeric, text, vector, and geospatial) are critical - Parquet is far from efficient. The fundamental assumptions that make it designed for analytics - compressed column storage, sequential reads, and late materialization - become bottlenecks in search-heavy environments. Let's break down why Parquet, despite its strengths, is ill-suited for search use cases.

Lucenia: Simplifying Search for Developers​

At Lucenia, we're on a mission to revolutionize search operations for developers, empowering you to build amazing applications faster and easier than ever.

Let's get started with Lucenia in just a few simple steps.

In this tutorial, I'll walk you through three easy steps to deploy Lucenia, our fast, enterprise-grade search engine, on Amazon EKS (Elastic Kubernetes Service). With the help of our Helm chart, you'll have a fully functional Lucenia cluster up and running in no time.

Let's get started!

The demand for scalable and reliable search solutions is critical in a data-driven world. Finding the right search engine can be challenging when dealing with large unstructured datasets, complex queries, spatial information, or a mix. Enter Lucenia, a versatile hybrid search platform engineered to meet the most demanding search requirements with exceptional efficiency. Many search solutions become an operational nightmare as data grows. However, Lucenia scales with your data and delivers high-performance search capabilities across various use cases. We prioritize ease of use, security, and the ability to fit into any search use case without forcing users to cloud specific solutions.

We were thrilled to attend last week's USGIF GEOINT Conference in Orlando, Florida. GEOINT 2024 was Lucenia's first conference as a company, and we were still determining what to expect when going to the conference. Still, we arrived eager to see where the community needed from a search perspective in their mission. Among the attendees were industry experts, government officials, and thought leaders, and we connected with various people from different industries. We shared our knowledge and expertise in geospatial search and vector data storage with attendees, and we were excited to see the interest and enthusiasm from the community. Several things stood out to us during the conference, and we wanted to share some of our key takeaways with you.

As cloud storage costs have decreased, copying and storing data in multiple locations has become increasingly common. This reduction in storage cost has resulted in a proliferation of databases and data warehouses. However, we are now undermining those cost savings by adding redundant storage to handle more complex workloads. Additionally, the cost of maintaining these databases have also increased, driving a consolidation of the modern data stack. Cloud architecture has shifted from large, centralized databases to distributed systems, with many businesses transitioning from shared databases to streaming systems. These systems move data to local disks, enhancing fault tolerance in distributed systems. Tools like Apache Kafka have made it easier to facilitate data movement between systems and enable data to be distributed across multiple read-optimized views. Streaming architectures, made possible by inexpensive storage, have enhanced the adaptability and scalability of the modern data stack.