Data, consolidated.
Reports that match.
Data warehouses, lakehouses, semantic layers, BI dashboards, and the pipelines that connect them. Governed at the source, consistent across reports, and ready for both BI and AI consumption.
Why the numbers stop matching.
The most expensive moment in any BI program is the one where two leaders pull up two dashboards showing two different numbers for the same metric. It happens because each dashboard tool is wired to the source data independently, with its own joins, its own filters, and its own definition of what counts.
The way through is a single modeled layer that defines the metrics once, with lineage from raw source through to dashboard. The BI tool becomes a presentation surface rather than the keeper of truth. That same modeled layer then feeds the AI systems that need governed, well-typed data.
Apollo's data work runs governance-first from day one. Source systems are mapped before any modeling starts, metric definitions live in code that gets reviewed and versioned, and lineage is observable end to end. When two reports disagree, the team can trace the discrepancy back to a specific transformation in minutes.
Metric definitions. Two teams build dashboards from the same warehouse, define "active customer" differently, and now leadership has two truths to choose from.
Data quality monitoring on the warehouse itself. Pipelines silently dropping rows or producing wrong joins are the most common cause of trust erosion, and they're invisible in the BI tool.
Lineage and access controls are not optional. Regulators expect to see who can read what, and how each reported number was calculated, with the audit trail to back it up.
Five shapes of data work.
Most engagements land in one of these patterns. Each has its own decomposition of source-to-consumption flow, its own quality contract, and its own operational shape.
Warehouse & Lakehouse Architecture
The foundation: a single governed store of the data the business runs its reporting and AI workloads on. We pick the platform for the workload and the team, not for the trend on the analyst report.
Pipelines (ETL / ELT)
Source-to-warehouse ingestion plus the in-warehouse transformations that turn raw data into the modeled layer. With tests, observability, and idempotent reruns so the team can recover from any individual run without re-importing the world.
Master Data & Data Quality
The middle work most BI programs miss: making sure "customer 1234" in the CRM and "cust_1234" in the billing system are actually the same customer, and the warehouse knows it. With drift detection that surfaces problems before they reach the dashboard.
BI & Semantic Layers
Dashboards built on top of a governed semantic layer. Metric definitions live in source-controlled code, not in a workbook on someone's laptop. The same metric calculates the same way in every report it appears in.
Real-time Data & Streaming
When the business needs to act on the data in real time, not wait for the overnight batch. Change-data-capture from operational systems, streaming pipelines, and real-time materialized views. All running on the same governance, lineage, and metric definitions as the warehouse, so the real-time numbers and the dashboard numbers are designed to tell the same story.
A data platform, end to end.
Simplified, but representative of how we lay out a data platform. The modeled layer is the centerpiece. Every downstream system, whether a Tuesday-morning dashboard or an AI inference pipeline, reads from the same governed definitions.
Metrics, defined once. In code.
Metric model with lineage
Below is a simplified version of a metric model we'd ship. The transformation lives in source-controlled SQL, the inputs are versioned references to upstream models, and the output is the single definition every downstream consumer reads from.
Once leadership starts asking "why are these two numbers different," the answer is in a file someone can read, with lineage to back it up.
-- Daily customer order facts. The single definition. -- Every downstream report reads from this model. {{ config(materialized='incremental', unique_key='order_date_customer') }} WITH normalized_customers AS ( SELECT customer_id, LOWER(TRIM( customer_email)) AS email_key, created_at FROM {{ ref('stg_customers') }} WHERE NOT is_test_account ) daily_orders AS ( SELECT DATE(ordered_at) AS order_date, customer_id, SUM(amount_usd) AS gross_revenue, COUNT(*) AS order_count FROM {{ ref('stg_orders') }} WHERE status = 'completed' GROUP BY 1, 2 ) SELECT CONCAT(o.order_date,'_', o.customer_id) AS order_date_customer o.order_date, o.customer_id, c.email_key, o.gross_revenue, o.order_count FROM daily_orders o INNER JOIN normalized_customers c USING (customer_id)
Four phases. Metrics first.
Apollo's standard methodology, applied to the specific failure modes of data programs. Each phase produces working software, and the metric definitions are agreed before any pipeline gets written.
Map the data. Agree the metrics.
Source systems profiled, data lineage from current reports traced, and the metrics that actually drive decisions identified. You leave with a written assessment of where the numbers come from today and where they disagree.
Architecture & governance plan.
Warehouse or lakehouse selection, modeled-layer design, access and lineage model, BI tool decision. The proposal names the trade-offs we'd make and why, and what the cost envelope looks like at typical query volumes.
Pipelines and metrics, in iterations.
Source-to-warehouse pipelines, modeled layer, semantic definitions, BI dashboards, quality monitoring. Two-week iterations. Each shipped metric arrives with its tests, documentation, and a dashboard the relevant stakeholders have signed off on.
Monitoring on. Hand off.
Quality monitoring running in production, drift detection wired to alerts, refresh-cadence and on-call runbooks documented. Knowledge transfer to your team, or a managed support agreement on the other side. Your call.
The shortlist we work from.
What we deliver on. We pick specifically for the workload, the data volumes, and the team that will own it, and we'll explain why in any proposal.
Warehouses & lakehouses
Pipelines & transformation
BI & semantic
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Tell us about your data.
Send a paragraph about what you're trying to fix or build: the sources you have, the questions you can't answer today, the dashboards that exist and the ones that don't. We'll reply within one business day, either with a 30-minute call or with an honest "this isn't the right fit; here's who you should call instead."