Modern ELT, change-data-capture, and real-time streaming wired into one governed lakehouse — so the model on top reads from a foundation that's always current and always trustworthy.
Every model on top of your data — every dashboard, every forecast, every agent — is only as good as the pipeline feeding it. When integration is brittle, everything downstream feels brittle too.
SSIS packages, scheduled SQL jobs, and a Power Automate flow nobody wants to touch. Every source change is a ticket and an apology.
Overnight ETL means the dashboard people open at 9am misses the order that came in at 2am. Streaming is the obvious fix — except no one owns the platform.
When finance asks "where does this number come from?" the answer involves four hops, three nicknames, and a Friday-afternoon notebook on someone's laptop.
Eight focused capabilities. We pick the smallest set that lands clean data into your gold layer — and skip the rest.
ERP, CRM, SaaS, files, APIs, Kafka, and lakehouses — wired into Fabric, ADF, or your tool of choice with retries, schema checks, and lineage.
Bronze → Silver → Gold patterns in dbt, Power Query, Spark, or T-SQL — version-controlled and tested like the rest of your code.
Sub-minute change tracking from operational databases without putting load on production — log-based, snapshot-safe, idempotent.
Eventstream, Event Hubs, and Kafka pipelines for the use cases that genuinely need sub-second freshness — no streaming-for-streaming's-sake.
Dependencies, retries, alerting, and SLA tracking via Fabric Pipelines, Azure Data Factory, or Airflow when the stack calls for it.
Great Expectations, dbt tests, and contract checks at the source boundary. Bad rows quarantined, not silently swallowed.
Microsoft Purview or Unity Catalog wired into the build process so lineage is generated, not maintained.
Retire SSIS, Informatica, and bespoke .NET jobs onto a maintainable modern stack — without a Big Bang weekend.
The reference flow we build on top of. Substitute any one block for your stack — the contract between layers stays the same.
Most data integration mistakes come from picking the wrong pattern. Here's how we think about the choice — and when each one earns its keep.
Hourly or daily extracts to bronze, governed transforms to gold. Simple, cheap, debuggable. The right default for the 80% of analytics that don't need sub-minute latency.
USE WHEN · BUDGETS · CLOSE · MGMT REPORTINGLog-based replication from operational databases — minutes-fresh data without taxing the source. The middle ground that solves most "we need it faster" requests.
USE WHEN · OPS DASHBOARDS · CUSTOMER-360Event-driven pipelines on Eventstream, Event Hubs, or Kafka. Sub-second freshness for the workloads that actually need it: fraud, IoT, real-time inventory.
USE WHEN · IoT · FRAUD · REAL-TIME OPSFour phases with one sign-off each. Pipelines in production by the end of week six.
Inventory of sources, current jobs, schemas, and the questions they're meant to answer.
Reference design — pick the pattern (batch / CDC / streaming) per source. One readable diagram.
Three priority pipelines into production with tests, lineage, and alerting from day one.
Sunset legacy ETL incrementally. Hand off the runbook.
Outcomes worth aiming for. Specific numbers depend on your tenant; we baseline them in discovery.
Every downstream model — reports, forecasts, agents — pulls from the same trusted dataset.
The right freshness for each use case — daily where that's enough, sub-minute where it isn't.
Source-to-report lineage in Purview. "Where does this number come from?" is a one-click answer.
Tested transforms, idempotent loads, sane alerting. Pipelines that survive a long weekend.
Source-controlled, peer-reviewed code. Onboarding a new analyst takes days, not months.
A four-to-six-week engagement that lands three production pipelines into a governed lakehouse — with tests, lineage, and a runbook your team can extend.
Engagements scoped per tenant. Fixed-price options available for foundations work; T&M for build-out and migration.
The questions we get most often during scoping. If yours isn't here, write to info@arkimetrix.com.
No. Fabric is our default because most of our clients already live in the Microsoft estate, but we've built happy stacks on Snowflake + dbt, Databricks, and BigQuery too. The pattern transfers.
We catalogue them, group by source system, and migrate in slices — not all at once. Each new pipeline replaces a clearly-defined set of legacy jobs, which then get retired. No big-bang weekends.
Probably not. Most "we need real-time" requests are actually "we need fresher than overnight" — and CDC solves that for a fraction of the operational cost. We'll pick the pattern per source during architecture.
Schema contracts at the source boundary, automated drift detection in CI, and quarantine for non-conforming rows. Sources change — pipelines shouldn't break silently.
Yes — most engagements are co-builds. We bring patterns and a senior pair-programmer; your engineers own the code by the end.
A 30-minute scoping call. We'll look at your top three sources, the worst job you have, and the freshness your stakeholders actually need. We'll tell you whether we're a fit — and what we'd do first.