Elasticsearch Archives

Why PostgreSQL Search Isn’t Enough: A Case for Purpose-Built Retrieval Systems

eCommerce, Elasticsearch, OpenSearch, Qdrant, Semantic Search, Vespa / Insights

Instacart’s recent blog posts and InfoQ coverage paint a picture of a simplified, cost-effective search architecture built entirely on PostgreSQL. It’s a clever consolidation — but also a cautionary tale. For most organizations, especially those with complex catalogs, high query diversity, or real-time ranking needs, this approach is not just suboptimal — it’s misleading. Postgres is a relational database, not a retrieval engine. Treating it like one introduces architectural debt that will eventually come due. While the engineering effort is commendable, the approach is dated and fundamentally flawed for organizations […]

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After App Search: A Modernization Blueprint for AI-First Organizations

AI, App Search, Elasticsearch, Migration / Insights

Essential considerations for a successful migration away from App Search, emphasizing the importance of assessing your current implementation and aligning search with your business goals. Discover how modernizing your search technology can enhance relevance, expand use cases, and operationalize insights.

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What’s New in Elasticsearch 9.0: Key Innovations in Search, Observability, and Security

Elasticsearch, Enterprise AI, Insight Engine, News / Insights, News

Elasticsearch 9.0 is finally GA, and it’s packed with powerful new features that push the boundaries of search, observability, and AI-driven analytics. Whether you’re building semantic search applications, managing massive datasets, or monitoring LLM performance, this release delivers innovations designed to meet modern data challenges head-on. Here’s a breakdown of the most impactful updates in Elasticsearch 9.0 from our perspective: Better Binary Quantization (BBQ) – Now Generally Available One of the standout features in Elasticsearch 9.0 is the GA release of Better Binary Quantization (BBQ), Elastic’s proprietary vector quantization technique.

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Navigating the Vector Embedding Maze: Cost-Effective Strategies for Elasticsearch

AI, Elasticsearch, Vector Embedding / Insights

This article shares the finding from a recent analysis of the general costs associated with popular options for vector embedding for a typical website use case.

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Announcing our HTTP/S Elasticsearch Ingestion Plugin for Integrating with LLMs

AI, Elasticsearch, News, Semantic Search / News

Announcing MC+A’s new HTTP Ingest Elasticsearch Plugin, enabling remote HTTP/S calls to external services for content enrichment and Named Entity Recognition within Elasticsearch ingest pipelines. Simplify Elasticsearch workflows with large language models and custom prompts.

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Is your Elastic Cloud Cluster Right Sized?

Elasticsearch, Enterprise Technology, Insight Engine / Insights

How and when you right size your cluster can lead to better utilization of your cloud spend Elastic Cloud cluster for ELK Stack = Easy Elastic Cloud makes deploying, operating, and scaling the Elasticsearch Stack (ELK) in the cloud easy. The Elastic Cloud is run by Elastic, the maker of Elasticsearch and related products. It runs in all the major public cloud providers (aka Hyperscalers) and through its management console, you can start with the logical minimal infrastructure needed and then scale it up to hundreds of nodes to service

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Intelligence Investigation using RAG and LLM

Elasticsearch, Insight Engine, Large Language Model, Retrieval Augmented Generations, Webinar / Webinar

Watch this on-demand webinar where MC+A demonstrates a Retrieval Augmented Generation (RAG) solution powered by a local Large Language Model (LLM) and machine learning securely and privately. We demo how the solutions can act as a catalyst in expediting the investigation process.

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Elasticsearch 8 NLP and Beyond

Elasticsearch, NLP / Insights

An introduction to the exciting Elasticsearch NLP features. We’ll cover language detection, NER and prediction.

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Streamlining Real-Time Data with Debezium and Elasticsearch

AI, eCommerce, Elasticsearch / Customer Success, Insights

In the ultra-competitive vertical of B2C ecommerce search, real-time data processing is a cornerstone technology that ensures the most relevant product information is available for consumption by the search system to provide the best experience to users.

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