Automation & data quality management

for Data Warehouse & Data Lakehouse

We automate data management in heterogeneous landscapes – from the connected source to the consumable data application. Our tiered data quality management creates traceable, auditable processes and reliable key figures for better decisions.

Why automation & data quality are crucial now

Today, companies are faced with the task of transferring many heterogeneous data sources into an analytical data platform quickly, consistently and in compliance with regulations. We rely on metadata-driven templates and frameworks that provide reusable building blocks and realize automation – for classic data warehouse layer models as well as for data warehouse architectures.

With modern orchestration (e.g. Apache Airflow), model-driven transformation(dbt), Python, Spark clusters and established ETL tools in on-premises environments, we automate processes end-to-end – mostly platform-independent and scalable.

At a glance

Decades of experience in consolidation in international groups ensure our customers benefit from the technical and technological expertise of our team. We have the relevant experience in group accounting issues to make your project a success:

  • Automated connection of dozens of data sources

  • Reusable, metadata-driven templates

  • Orchestration & code generation for DWH & Lakehouse

  • Level-based data quality management (incl. SCD)

  • Auditable according to BCBS 239 & SOX

Architecture & Orchestration

Automated data management from the source to the BI level – repeatable, testable, documented.

Automation in the Data Warehouse & Lakehouse

Data Warehouse

  • Layer model (landing, staging, core, marts)

  • Template-based loading and transformation jobs

  • Change data (CDC) & SCD automation

  • Rule-based data lineage & versioning


Data Lakehouse

  • Automatic orchestration & code generation

  • Delta /Iceberg tables, ACID & Time Travel

  • dbt based models & tests as standard

  • Scalable execution on Spark clusters

Data quality management – graded & auditable

Our data quality approach is layered and systematically covers the typical quality views: master data checks, format and plausibility checks, data cleansing, syntactic and semantic checks as well as SCD automation. We visualize the results in a separate DQ data model and provide key figures for monitoring and control.

Regulations & tests

Standardized dbt tests, user-defined Python checks and SQL checks per level. Rules are versioned, documented in a traceable manner and can be rolled out in a CI/CD-controlled manner.

DQ-KPIs & Scorecards

Visualization in the DQ data model: completeness, consistency, timeliness, validity, unambiguity, etc. – including trend and cause analyses.

Compliance & Audit

Auditable traceability in accordance with BCBS 239 and Sarbanes-Oxley (SOX): lineage, control reports, dual control principle, technical and functional approvals.

ML-supported data quality monitoring

Based on the DQ history, we establish machine-learning-supported processes (e.g. anomaly detection) that signal deviations at an early stage and make data quality maturity levels measurable.

Methodology & Templates

We accelerate projects with metadata-driven templates for loading, transformation and test routes. This allows us to automatically generate code, orchestration plans and documentation – sustainably maintainable and expandable.

Typical deliverables

  • Source adapters & CDC pipelines (Airflow, Python, ETL Suite)

  • dbt models incl. tests, seeds & snapshots

  • Spark jobs for large amounts of data

  • DQ regulations, scorecards & operational reports

  • CI/CD setups incl. automatic documentation

Technology stack (excerpt)

  • Apache Airflow, dbt, Python, Spark

  • Delta Lake / Apache Iceberg

  • Cloud DWH & Lakehouse: Azure, AWS, GCP

  • ETL/ELT: classic on premises tools

  • BI tools for reporting & self-service

Your added value

  • Scalable automationAccelerateddevelopment through code generation, reuse and standards – with clear SLAs and operational indicators.

  • Reliable data quality Transparent KPIs, reproducible audit trails and audit trails – the basis for stable reports and analyses.
  • Investment security Platform-independent, cloud-capable, tried and tested on-premises. Modernization without lock-in.

Experience & References

Since 1997, DATA MART has been supporting medium-sized companies and international corporations in modernizing their data architectures and implementing business requirements with suitable data modelling and BI tools. Our users benefit from robust, future-proof solutions and a team that combines stability and innovation.

DATA MART Consulting

FAQ.

Frequently asked questions answered briefly

Yes, we integrate into existing layer models and use existing ETL tools where appropriate. At the same time, we enable a gradual migration to Lakehouse components – without a big bang.

Via versioned sets of rules, technical and functional approvals, complete data lineage and reproducible controls. Reports document results and measures – compliant with BCBS 239 and SOX.

Yes, our templates are platform-independent and support cloud and on-premises scenarios as well as hybrid architectures.