We design and implement pipelines, data lakes and data architectures that bring your sources together, in order of priority, into a reliable analytics layer prepared for BI and AI.
These problems cost money every month and get more expensive as they grow.
Data in silos: CRM, ERP, spreadsheets and third-party APIs that never talk to each other.
Handcrafted pipelines built on SQL scripts that break every time a table changes.
Reports that take hours to run because they query the production database directly.
Dirty, duplicated and inconsistently formatted data, with each system following its own convention.
Without a reliable historical base, predictive models never leave the drawing board and AI answers with the wrong data.
No governance: nobody knows who owns each piece of data, or what the official definition of a KPI is.
We implement the medallion architecture (Bronze, Silver and Gold layers), ingesting from multiple sources along one of two paths: on Microsoft Fabric, with Data Factory pipelines and OneLake storage; or on Azure, with Azure Data Factory and Azure Data Lake Storage Gen2. Transformation runs in dbt or in Azure Databricks notebooks.
The Gold layer feeds the Power BI semantic model, where the measures and the official definition of each KPI live. Code and schema are versioned. Every pipeline has orchestration, execution logs, failure alerts and technical documentation: nothing works as a black box.
A well-built data lake becomes the common base for every analytics tool you already use.
A validated architecture and delivery in short iterations, instead of a months-long big bang.
An inventory of all data sources, volumes, update frequency and priority analytics use cases.
Choosing the architecture (lakehouse, data warehouse or hybrid) based on volume, data types and the team that will maintain it, along with the tools and the governance model.
Ingestion pipelines for the priority sources running with orchestration, logs and alerts configured.
Cleansing, enrichment and dimensional modeling for the analytics use cases defined in the discovery stage.
Connection to Power BI, a data catalog with an owner for each table, monitored quality rules, role-based access and training for the client data team.
Illustrative scenarios to guide the assessment, not projects delivered by Yottaflow.
Scenario
12 sources with no integration: ERP, CRM, marketplace, a logistics API and cost spreadsheets. The monthly report took 5 days and arrived inconsistent.
Solution
A data lake with medallion architecture on Azure, automatic ingestion pipelines, a Gold layer modeled for Power BI and a scheduled daily refresh.
Scenario
IoT sensors generated temperature, vibration and pressure data with no structured storage. Data was lost and corrective maintenance was expensive.
Solution
A telemetry ingestion pipeline on Azure, a feature layer on Azure Databricks and anomaly detection models integrated into the maintenance dashboard.
Scenario
Operations auditing required cross-referencing data from 4 distinct systems with no structured history, a process that took 3 weeks.
Solution
A data warehouse with incremental ingestion, data lineage documented in Microsoft Purview and an audit dashboard with drill-down by operation, date and owner.
We design the architecture to take on new sources and more volume without rebuilding the base, and we record the decisions so that its evolution is planned.
We start from the Microsoft ecosystem (Microsoft Fabric, Azure Data Factory, Power BI) and only bring in Azure Databricks or dbt when the volume or the team calls for it.
Data catalog, lineage and an owner for each table defined from the design phase, not as documentation written afterwards.
The first pipelines for the priority sources go into production in stages, on a timeline agreed after the sources are surveyed.
What it costs: data engineering projects start with the Assessment or go straight to Implementation. See plans and price ranges
Book a free discovery call. In 30 minutes we map your data sources and what can be built in stages.