Architecture and industrialization of ETL/ELT pipelines on Databricks, Azure Data Factory, or equivalents. Data quality, observability, governance.
Most companies starting a data initiative quickly find themselves with fragile, undocumented pipelines maintained by one or two people. Classic symptoms: late morning reports because a batch crashed at 3am, different numbers between two dashboards in the same business domain, impossible to trace the origin of a number, data team saturated by ad hoc extraction requests. Industrializing data pipelines with a lakehouse architecture and formal governance solves these problems at the root.
All sectors that have accumulated operational data: banking and insurance (regulatory reporting DORA, Solvency II, credit risk), distribution and retail (sales tracking, logistics, marketing), telecom (network analytics, customer life), industry (supply chain, quality, predictive maintenance), healthcare (patient journey, HDS compliance), public sector (statistics, performance indicators). Data maturity varies, but all converge toward the Databricks lakehouse or Microsoft Fabric pattern as the modern target.
The lakehouse (Databricks, Microsoft Fabric, recent Snowflake) combines the advantages of both previous approaches: economical storage of the data lake (Parquet on cloud storage) plus analytical SQL performance of the data warehouse (Delta Lake, Iceberg). It is today the default pattern for new data architectures. Microsoft has pushed this trend with Fabric which unifies Power BI, Data Factory, Synapse, and Data Activator in a single experience. See our AI & Data expertise for detailed patterns.
Fragmented data flows, silos, high latencies
Modern data architecture, mixed BI + ML + streaming workloads, open source priority. Lakehouse market leader, multi-cloud (Azure, AWS, GCP).
Microsoft ecosystem dominant, native Power BI integration desired, end-to-end Microsoft solution wanted. Microsoft's new flagship product.
Pure SaaS platform, native compute-storage separation, operational maturity. Suited to modern data-driven companies.
Google Cloud ecosystem, massive analytical volumes, serverless preference. Google's historical platform, very robust.
Technological sovereignty sought, internal tool mastery, cloud budget to optimize. Open source full-stack approach.
A data pipeline industrialization program is structured over six to eighteen months for a first operational lakehouse, then in continuous mode for evolution. The typical team combines a senior data architect, a data engineering tech lead, three to six Python/SQL data engineers, one to two analytics engineers (dbt modeling), a data governance lead (cataloging, security, quality), a data-oriented DevOps. For an initial scope of five to fifteen data sources with twenty to fifty pipelines, plan eight to twelve months with a cell of six to ten people.
Starting with the tool rather than the business use cases. Data architectures that fail start with Databricks or Fabric and then look for what to run on them.
Mandatory Intake phase to scope the 3 to 5 prioritized business use cases (financial reporting, commercial indicators, regulatory compliance). The target architecture flows from use cases, not the other way around. Validation of expected business gains before any tool installation.
Ingesting all possible data into the lakehouse without a target data model. Result: an ungoverned data swamp, hard to exploit.
Medallion architecture (bronze, silver, gold) with dimensional model in gold layer. Each business indicator has a unique documented definition, an identified owner, testable quality rules. Unity Catalog (Databricks) or Purview (Microsoft) for centralized governance.
Neglecting data quality. A pipeline that delivers wrong data on time is worse than a late pipeline.
Automated quality tests at each stage (great_expectations, dbt tests, Databricks Delta Live Tables expectations). Quality rules defined with business: completeness (mandatory fields), validity (formats, ranges), consistency (totals, invariants), freshness (update delays). Anomaly alerts with downstream pipeline blocking if critical threshold.
Building monolithic pipelines that reprocess everything at every execution. Cloud costs explode, latencies degrade, maintenance becomes a nightmare.
Incremental processing with Change Data Capture (Debezium, Databricks CDC), temporal partitioning (by day, hour according to frequency), job idempotence. Pipelines are split into independent steps orchestrated by Airflow or Databricks Workflows, allowing partial restart and fine observability.
Forgetting data governance in the initial scoping. Without cataloging, access policy, sensitive data classification, the lakehouse becomes a regulatory risk (GDPR, DORA, AI Act).
Integrated governance from day 1 with Unity Catalog (Databricks) or Microsoft Purview. Each dataset has an owner, a classification (PII, confidential, public), complete lineage, RBAC access rules. Tracked access audits. GDPR and Quebec Bill 25 compliance verified on datasets containing personal data. See the Dynamics 365 Bill 25 path for sectoral subtleties.
Data analytics platform for a national telecom operator. Harmonization of figures between national business, regional units, and subcontractors. Industrialized Power BI and Google Cloud Platform pipelines.
Pentaho to Power BI decision migration for a North American public organization. Data model enrichment, refresh industrialization, access governance.
The cloud budget of a lakehouse depends on data volume and analysis intensity. For a lakehouse of 10 to 50 TB with daily pipelines, plan 5,000 to 25,000 euros per month of cloud bill (Databricks or Fabric plus storage). For a strategic lakehouse of 100 to 500 TB with ML workloads, budget 30,000 to 150,000 euros per month. These costs include storage, compute, networking. Common optimizations: auto-scaling, spot instances for non-critical workloads, Delta Lake compression, smart partitioning.
Databricks if: multi-cloud ecosystem, advanced ML workloads, mature data team preferring open source. Fabric if: dominant Microsoft ecosystem, prioritized native Power BI integration, end-to-end Microsoft solution desired, M365 E5 license already in place. Both are excellent, the choice is made on global cloud strategy more than technical features. Databricks is more mature for ML, Fabric is more integrated on the BI and Copilot side.
Migration in three phases. Phase 1: audit of the existing data warehouse — tables, views, stored procedures, ETL jobs. Cataloging of critical datasets and decommissioning of unused ones (often 30 to 50 percent of legacy). Phase 2: lakehouse parallelization — ingestion of sources into the new lakehouse, reconstruction of business views in dbt or Delta Live Tables, parity tests on figures. Phase 3: cutover and decommissioning — migration of Power BI/Tableau reports, progressive decommissioning of legacy. See the Cognos to Power BI path for decision migration cases.
Mandatory classification of datasets containing PII (personally identifiable information) from ingestion. Automatic masking and tokenization in silver/gold layer for non-prod environments. Formal retention policies with automatic purge. Right to be forgotten implemented via special jobs that trace and delete data on demand. Audit trail of all accesses. For Quebec Bill 25, see the specific Dynamics 365 + Bill 25 path.
The typical team: 1 senior data architect, 1 data engineering tech lead, 3 to 6 Python/SQL data engineers, 1 to 2 dbt/modeling analytics engineers, 1 data governance lead, 1 data-oriented DevOps. Nearshore-onshore split: tech lead and architect ideally close to the client, rest of the cell in nearshore Tunis. See the delivery models for contractual formats (nearshore CDR, data competency center).
The budget of a data program depends on the number of sources to connect and their actual accessibility, the number of pipelines to build and maintain, and the quality level required on incoming data. That last point holds the most surprises: reprocessing degraded historical data often weighs more than building the lakehouse itself. Free initial scoping call, 30 minutes to 2 hours.
We frame the trajectory, the budget, and the deliverables in a first thirty-minute conversation. A short POC can be proposed before committing to the full program.
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