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Not a dump of tables: a model where "revenue" is the same for everyone.

THE ANALYTICAL BRIDGE

Batch and streaming ingestion
Windowed loads or low-latency flows: frequency follows the decision, not streaming fashion.
ETL and ELT
Transform before or after load, depending on the stack. Versioned business logic, not scattered scripts.
Warehouse and data marts
Denormalised read model (facts and dimensions). BigQuery, Snowflake, Fabric or dedicated marts when multi-source and history require it.
dbt and governed transformations
Versioned SQL, tests and docs. The warehouse stays trustworthy when the team changes.
Semantic layer
Shared metrics once (Power BI Semantic Model, LookML, dbt Metrics, Fabric). The same "revenue" in every report.
Metric governance
Owners, who approves definitions, who can change them. Without this, the Single Source of Truth decays at the first hotfix.

Operating line

This deep dive is part of Data analytics.

Other deep dives

Same operating line, other parts of the perimeter.

Why choose Syncronika

  1. Decisions before tools

    Warehouse and BI earn their keep if they shorten time from question to decision. No brochure platform.

  2. OLTP AND OLAP KEPT APART

    The ERP writes the present; the analytical model explains the past. No heavy reports on production Postgres.

  3. SAME INTEGRATION TEAM

    Pipelines and APIs in the same perimeter as analytics. Fewer handoffs, fewer conflicting numbers.

  4. AI AFTER THE RULES

    Classification and anomalies when definitions and pipelines hold. Not a copilot on dirty sources.

We are digital partnersto ambitious, winning brands

See our work

We are digital partners for companies that want to grow in a structured way.

FAQ

What we’re often asked

FAQ

What does the data platform include?

Ingestion (batch or streaming), ETL/ELT, modelling into a warehouse or data mart, versioned transformations (dbt or equivalent), semantic layer and BI wiring. Goal: a Single Source of Truth, not a dump of tables.

Do we always need a data warehouse?

It becomes useful when decisions depend on multiple systems, you need reliable history, you want to version KPIs, or the operational database is no longer the right place for analysis. It is not mandatory on day one.

What is the semantic layer?

The layer where "revenue", "qualified lead" and other metrics live once (Power BI Semantic Model, LookML, dbt Metrics, Fabric). It stops every report reinventing the formula.

How does it relate to reporting and integrations?

Integrations and source quality feed the platform; executive reporting reads from it. Same data analytics perimeter, with the engineering piece made explicit.