Data Panda lifts records from your CRM, ERP, ecommerce, product and log sources into Elasticsearch on a known schedule. Once it lives in one cluster, your search experiences, your Kibana dashboards and your vector queries all read indices that match the rest of the business instead of each app shipping its own crawler.
Elasticsearch was started in 2010 by Shay Banon, built on the Apache Lucene search library and exposed through a JSON over HTTP API. The company behind it, Elastic NV, listed on the NYSE in October 2018 under the ticker $ESTC and is headquartered in Mountain View. The engine is the core of the Elastic Stack, where Logstash and Beats handle ingest and Kibana handles the UI, the combination most people still call ELK.
Inside the cluster, data lives in indices, which are split into shards and copied into replicas across nodes. Documents are JSON, mappings define how fields are analysed, and the Query DSL covers everything from a basic match to fuzzy, geo and nested queries. Aggregations turn the same indices into an analytics surface for log, metric and event data, which is why the ELK stack became the default observability pattern for a generation of engineering teams. Since 8.x, dense-vector fields and a kNN search API made Elasticsearch a credible store for semantic and hybrid search next to BM25. The licensing story to be aware of: in 2021 Elastic moved from Apache 2.0 to a dual SSPL plus Elastic License v2, which prompted AWS to fork the engine as OpenSearch, and in 2024 Elastic added AGPLv3 as a third option to make the source open-source again under OSI terms. We treat the cluster as a destination, land the data on a cadence the cluster can absorb, and shape mappings and shards to the workload instead of the default.
Kibana dashboards read indices that match the rest of the business, not a side copy of it.
Operational records land in Elasticsearch on a schedule the cluster can absorb without queue backlog.
Dense vectors live next to text fields so hybrid retrieval runs on the same indices a search box queries.
Internal apps, customer portals and back-office tools query one Elasticsearch cluster instead of three.
A list of concrete reports, automations and AI features we have built on Elasticsearch data. Pick the one that matches your situation.
Why is our Elasticsearch cluster slow even though we keep adding nodes?
Almost always shard sizing or mapping. Default mappings often analyse fields nobody searches and create thousands of tiny shards that pin heap. Reviewing mappings against actual queries, consolidating shards toward the recommended size and putting an ILM policy on time-series indices usually gives more headroom than a bigger node.
Should we run Elasticsearch ourselves or move to Elastic Cloud or OpenSearch?
Self-hosted suits teams with platform engineering capacity and a residency case for keeping the cluster on their own infrastructure. Elastic Cloud takes the operations cost away and tracks new releases first. OpenSearch makes sense when AWS lock-in and the AGPL/SSPL conversation matters more than the latest Elastic features. Most BE/NL clients pick on residency, ops appetite and the AI feature roadmap they want to follow.
Can we use Elasticsearch as the retrieval layer for our RAG or AI search use case?
Yes, and the hybrid story is one of its stronger arguments. Dense-vector fields with kNN search live next to BM25 in the same index, so RAG and AI search return results that already pass the same filters as the keyword query. We make sure embeddings refresh in step with the source records, so the model is reading the same record version your business is.
Finance gets a stable view of cluster spend per index family, so observability, app search and AI retrieval each carry their own cost line. Tier transitions and shard cleanup show up in the bill instead of in surprise overage.
Sales and customer-facing teams hit one search bar that returns product, customer and ticket records on the same ranking. Catalogue search on the webshop, internal lookup in the back-office and the support agent view all read the same indices.
Platform and SRE leads track shard count, heap pressure, refresh interval and ILM transitions in one Kibana view. Elasticsearch stops being the cluster nobody understands and becomes a sized, tiered system with a known cost per index.
These are the 1 tables we currently pull from Elasticsearch into your warehouse. Query them directly in SQL, join them to the rest of your stack, or build reports on top.
Missing a table you need? We can extend the sync. Tell us what is missing and we will build it for you.
You keep the reporting tool you already have. We connect it to the warehouse where your Elasticsearch data lives.
OAuth authentication. Read-only by default. We sign a DPA and your admin keeps the keys.
Data flows into your warehouse on your schedule. Near real time or nightly, your call. You own the data.
We build the first dashboard, workflow or AI feature with you, then hand over the keys. Or we stay on for ongoing delivery.
We set up the foundation. Your team builds on top.
Best fit Teams that already have a BI analyst or data engineer and want to own the build.
We build the whole thing, end to end.
Best fit Teams without in-house BI or dev capacity. You tell us what you need and we deliver it.
You do. It lands in your warehouse, on your cloud account. We don't resell or aggregate it. If you stop working with us, the warehouse stays yours and keeps running.
Near real time for most operational systems. For heavier sources we schedule hourly or nightly. You pick based on what the reports need.
No. If you don't have one, we help you pick one and set it up as part of the first delivery. Common starting points are Snowflake, Microsoft Fabric, or a small Postgres start.
We size shards toward the practical 10 to 50 GB range Elastic itself recommends, consolidate the small ones, and put an ILM policy on time-series indices so old data moves to warm or cold tiers on time. Mappings get reviewed against the queries that really run, so analysers and stored fields are not paid for nothing. Most clusters that have been growing for a year or two see heap pressure drop in the first cleanup cycle without a node added.
Both are credible. Elasticsearch under SSPL or AGPLv3 keeps you on the engine Elastic ships first, with the latest vector and AI search features. OpenSearch under Apache 2.0 fits teams who want the AWS-managed path or who have a procurement reason to keep an OSI-style permissive licence. We have clients on each, and the migration in either direction is workable when you have shaped the indices and ingest properly to begin with.
For most BE/NL workloads it can. Dense-vector fields with the kNN search API let you store embeddings alongside text and run hybrid queries in one round trip. A dedicated vector database earns its place when index sizes go into the billions of vectors with very tight latency budgets. Below that, the operational gain of one cluster, one auth model and one Kibana usually wins.
We review your Elasticsearch setup and the systems around it. Together we pick the first thing worth building.