Data lakehouse and federated analytics on Starburst — Azerbaijan implementation partner.
Federated SQL across object storage, databases and warehouses, with a semantic layer your agents can query directly.
The pipeline tax.
Every new question becomes a new pipeline, and every pipeline becomes a permanent obligation. The analytics backlog is not a staffing problem — it is an architecture that charges rent on curiosity.
Answers arrive a quarter late
A question that needs data from two systems waits for an ingestion job to be built, scheduled, monitored and reconciled before anyone can look at it.
Copies multiply
Each copy drifts from its source, needs its own access rules, and shows up in the wrong jurisdiction during a residency review.
Cost grows with duplication
Storage and compute are paid twice, then a third time for the reconciliation logic that keeps the copies plausible.
Agents cannot reach trusted data
AI agents are pointed at whatever extract is nearest, because there is no governed surface that answers questions across the estate.
Cost of inactionThe organisation pays for the same data several times, cannot honour residency commitments with confidence, and every AI initiative starts by rebuilding access to data it should already have.
Send the question to the data.
Starburst is a Trino-based lakehouse platform that federates SQL across sources and exposes one governed, semantically described surface — for analysts, applications and agents alike.
Query federation
High-performance MPP SQL across object storage, RDBMS and NoSQL, joining across systems without an ETL step in between.
Open table formats
Apache Iceberg and Delta Lake as the storage contract, so the data stays readable by tools you have not chosen yet.
AI semantic layer
Business context and metadata surfaced with the tables, so agents find and use trusted datasets rather than guessing at column names.
Accelerated performance
Warp Speed smart indexing, caching and workload analysis tune the hot paths without a separate optimisation project.
Unified governance
Fine-grained access control across Iceberg and Delta tables, aligned with the catalog and policies governed in OvalEdge.
On-prem, sovereign cloud, or both.
Yukon Labs deploys Starburst on the customer perimeter — on-premises or in your own cloud tenancy — so no data and no query text leaves the jurisdiction you operate under.
We size the cluster, connect the sources, model the semantic layer with your domain owners and tune the caching strategy against your real workloads rather than a benchmark.
Replication does not disappear; it becomes a deliberate decision with a stated reason, applied where physics or SLA demands it and nowhere else.
One query, many engines.
A federated query is planned once and executed close to each source. The coordinator splits the work, pushes down what each system can do best, and assembles the result — the data itself never lands in a new copy.
1 · Parse and plan
The coordinator builds a distributed plan across every connector the query touches, with the semantic layer resolving business terms to physical tables.
2 · Push down
Filters, projections and aggregations run inside each source system, so only the rows that matter cross the wire.
3 · Execute in parallel
Workers process fragments concurrently, with caching and smart indexing absorbing the repeated access patterns.
4 · Govern the result
Access policy is applied at the surface, so the same query returns a different, correct result depending on who asked.
Frequently asked questions
What is a data lakehouse, in plain terms?
A data lakehouse stores your data as open files on cheap storage while behaving like a warehouse when you query it.
The warehouse was reliable but locked your data in one vendor's system. The lake was cheap but had no guarantees, and most became swamps. The lakehouse adds a metadata layer over the files — an open table format — that restores transactions, schema control and time travel.
What is Starburst, and how does it relate to Trino?
Trino is an open-source distributed SQL engine with no storage of its own; Starburst is its enterprise distribution, from the company founded by Trino's creators.
Starburst adds what regulated institutions need: row- and column-level access control applied across every source, 50+ supported connectors, parallel extraction from JDBC sources, caching and commercial support. Running open-source Trino is legitimate if your access-control needs are simple.
Is Yukon Labs an official Starburst partner in Azerbaijan?
Yukon Labs implements and operates Starburst deployments in Azerbaijan as a delivery partner: cluster sizing, connector configuration, access policy design, directory integration and handover — or ongoing operation.
What matters more than a badge in an evaluation is whether anyone in-region has deployed the product against a core banking system and a supervisory access-control requirement. A tool nobody local has deployed is a tool you will operate alone.
Do we have to move our data in order to use Starburst?
No — Starburst queries data where it already lives, so nothing has to be moved. That is the point of the architecture: it reads from each source reading from each source at query time. There is no persistent copy inside the engine.
That also makes it easier to defend on data residency than centralisation: nothing is relocated, so the architecture itself raises no cross-border transfer question.
How does Starburst compare to Databricks and Snowflake?
Starburst, Databricks and Snowflake optimise for different things. If your data is already consolidated in one platform, that platform's engine is hard to beat on its own data. If it sits across many systems you cannot consolidate, Starburst is the strongest of the three and it is not close.
For supervised institutions there is a prior filter: Databricks and Snowflake have no realistic air-gapped deployment.
What is Apache Iceberg, and do we need it?
Iceberg is an open table format — a metadata layer over Parquet files that gives you atomic commits, schema evolution, time travel and row-level deletes.
You need it to build a lakehouse; you do not need it to start using Starburst, which also reads Delta, Hudi, Parquet and ORC. The format debate is settled in Iceberg's favour. The live question in 2026 is which *catalog* you commit to.
Can Starburst be deployed on-premise?
Yes — Starburst Enterprise can be deployed on-premise, running on Kubernetes in your own data centre: coordinator and workers are containers, and object storage can be MinIO or Ceph rather than a cloud service.
Nothing requires internet connectivity at runtime. This is the normal deployment for Azerbaijani banks and government institutions.
How many data sources can Starburst federate at once?
There is no practical limit, and a single query can join across as many as it needs. Open-source Trino ships 30+ connectors; Starburst extends that past 50.
The real constraint is the slowest source in a given query — which is why Starburst's parallel connections to a JDBC source matter more in a legacy estate than the connector count does.
Are federated queries slower than querying a warehouse?
Federated queries are slower than a well-tuned single-source warehouse query — but that is usually the wrong comparison. The alternative to a federated query is not a fast query; it is a three-week wait for a pipeline.
Trino pushes filters and aggregations down into each source, so it lands far closer to native performance than people expect. Measure total time to answer, not query latency.
Does Starburst replace our BI tool?
No — Starburst does not replace your BI tool. It is a query engine, and your BI tool connects to it as it would to a database.
What changes is reach: the same dashboards can query across the warehouse, the core system and object storage without a pipeline being built first. Most first deployments migrate a set of existing reports precisely so the difference is measurable.
What does a lakehouse migration typically cost and how long does it take?
A full lakehouse migration is a multi-year programme that frequently does not survive a change in priorities — which is why we recommend not doing one.
Federate first: a query engine over the estate as it is delivers value in the first quarter and shows what people actually query. Then migrate individual datasets only where there is a reason. A first federated deployment is six to ten weeks.
How does access control work across federated sources?
Starburst applies one policy layer across every connected source, including sources whose own security model cannot express the rules you need.
Row- and column-level policies map to your existing directory roles, so a user sees exactly what they are entitled to regardless of which system the data came from. One consistent policy is far easier to evidence to a supervisor than the same rules re-implemented in six systems.