Automation & data quality management
for Data Warehouse & Data Lakehouse<\/h2><\/div>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.<\/p>\n<\/div><\/i><\/div><\/div><\/div><\/div>Why automation & data quality are crucial now<\/h3><\/div>Companies today are faced with the task, many heterogeneous data sources<\/strong> quickly, consistently and compliantly into one analytical data platform<\/strong>. We rely on metadata-driven templates and frameworks<\/strong> that provide reusable building blocks and realize automation – for classic data warehouse layer models<\/em> as well as for Data Lakehouse Architectures<\/em>.<\/p>\nWith modern orchestration (e.g. Apache Airflow<\/strong>), model-driven transformation(dbt<\/strong>), Python<\/strong>, Spark clusters<\/strong> and established ETL tools<\/strong> in on-premises environments, we automate processes end-to-end – mostly platform-independent and scalable.<\/p>\nAt a glance<\/strong><\/p>\n<\/div>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 to make your project a success:<\/span><\/p>\n<\/div>- <\/i><\/span>\n
Automated connection of dozens of data sources<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
Reusable, metadata-driven templates<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
Orchestration & code generation for DWH & Lakehouse<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
Level-based data quality management (incl. SCD)<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
Auditable according to BCBS 239 & SOX<\/p>\n<\/div><\/li><\/ul><\/div><\/div>
![](\"https:\/\/www.datamart.de\/wp-content\/uploads\/2025\/06\/predictive-analytics-beratung.jpg\" "\\"predictive-analytics-beratung\\"")
<\/span><\/div><\/div><\/div><\/div><\/div>Architecture & Orchestration<\/h3><\/div>Automated data management from the source to the BI level – repeatable, testable, documented.<\/p>\n
Automation in the data warehouse & lakehouse<\/strong><\/p>\nData Warehouse<\/strong><\/p>\n<\/div>- <\/i><\/span>\n
Layer model (landing, staging, core, marts)<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
Template-based loading and transformation jobs<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
Change data (CDC) & SCD automation<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
Rule-based data lineage & versioning<\/p>\n<\/div><\/li><\/ul>
<\/strong>Data Lakehouse<\/strong><\/p>\n<\/div>- <\/i><\/span>\n
Automatic orchestration & code generation<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
Delta \/Iceberg tables, ACID & Time Travel<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
dbt based models & tests as standard<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
Scalable execution on Spark clusters<\/p>\n<\/div><\/li><\/ul><\/div><\/div>
Data quality management – graded & auditable<\/h3><\/div>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. <\/p>\n<\/div>
Regulations & tests<\/strong><\/p>\nStandardized dbt tests<\/em>, user-defined Python checks<\/em> and SQL checks<\/em> per level. Rules are versioned, documented in a traceable manner and can be rolled out in a CI\/CD-controlled manner. <\/p>\nDQ-KPIs & Scorecards<\/strong><\/p>\nVisualization in the DQ data model: completeness<\/em>, consistency<\/em>, timeliness<\/em>, validity<\/em>, unambiguity<\/em>, etc. – including trend and cause analyses.<\/p>\nCompliance & Audit<\/strong><\/p>\nAuditable traceability in accordance with BCBS 239<\/strong> and Sarbanes-Oxley<\/strong> (SOX): lineage, control reports, dual control principle, technical and functional approvals.<\/p>\nMethodology & Templates<\/strong><\/p>\nWe accelerate projects with metadata-driven templates<\/strong> for loading, transformation and test routes. This allows us to automatically<\/em> generate code<\/em>, orchestration plans<\/em> and documentation<\/em> – sustainably maintainable and expandable. <\/p>\nTypical deliverables<\/strong><\/p>\n<\/div>- <\/i><\/span>\n
Source adapters & CDC pipelines (Airflow, Python, ETL Suite)<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
dbt models incl. tests, seeds & snapshots <\/p>\n<\/div><\/li>
- <\/i><\/span>\n
Spark jobs for large amounts of data<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
DQ regulations, scorecards & operational reports<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
CI\/CD setups incl. automatic documentation <\/p>\n<\/div><\/li><\/ul>
<\/strong><\/p>\nTechnology stack (excerpt)<\/strong><\/p>\n<\/div>- <\/i><\/span>\n
Apache Airflow, dbt, Python, Spark<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
Delta Lake \/ Apache Iceberg<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
Cloud DWH & Lakehouse: Azure, AWS, GCP<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
ETL\/ELT: classic on premises tools<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
BI tools for reporting & self-service<\/p>\n<\/div><\/li><\/ul><\/div><\/div><\/div><\/div>
ML-supported data quality monitoring<\/h3><\/div> <\/strong><\/p>\nBased on the DQ history, we establish machine-learning-supported<\/strong> processes (e.g. anomaly detection) that signal deviations at an early stage and make data quality maturity levels<\/em> measurable.<\/p>\nYour added value<\/strong><\/p>\nScalable automation<\/strong><\/p>\nAccelerated development through code generation, reuse and standards – with clear SLAs and operational indicators.<\/p>\n
Reliable data quality<\/strong><\/p>\nTransparent KPIs, reproducible audit chains and audit trail – the basis for stable reports and analyses.<\/p>\n
Investment security<\/strong><\/p>\nPlatform-independent, cloud-capable, tried and tested on-premises. Modernization without lock-in. <\/p><\/p>\n<\/div><\/div><\/div>
![](\"https:\/\/www.datamart.de\/wp-content\/uploads\/2025\/09\/datenbereinigung-was-ist-das.jpg\" "\\"datenbereinigung-was-ist-das\\"")
<\/span><\/div><\/div><\/div><\/div><\/div>Experience & References<\/h3><\/div>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 modeling and BI tools. Our customers benefit from robust, future-proof solutions and a team that combines stability and innovation.<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div>
DATA MART Consulting<\/p><\/div>
FAQ.<\/span><\/h2><\/div><\/div><\/div>Frequently asked questions answered briefly<\/p>\n<\/div><\/div><\/div>
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.<\/p>\n<\/div><\/i><\/div><\/div><\/div><\/div> Companies today are faced with the task, many heterogeneous data sources<\/strong> quickly, consistently and compliantly into one analytical data platform<\/strong>. We rely on metadata-driven templates and frameworks<\/strong> that provide reusable building blocks and realize automation – for classic data warehouse layer models<\/em> as well as for Data Lakehouse Architectures<\/em>.<\/p>\n With modern orchestration (e.g. Apache Airflow<\/strong>), model-driven transformation(dbt<\/strong>), Python<\/strong>, Spark clusters<\/strong> and established ETL tools<\/strong> in on-premises environments, we automate processes end-to-end – mostly platform-independent and scalable.<\/p>\n At a glance<\/strong><\/p>\n<\/div> 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 to make your project a success:<\/span><\/p>\n<\/div> Automated connection of dozens of data sources<\/p>\n<\/div><\/li> Reusable, metadata-driven templates<\/p>\n<\/div><\/li> Orchestration & code generation for DWH & Lakehouse<\/p>\n<\/div><\/li> Level-based data quality management (incl. SCD)<\/p>\n<\/div><\/li> Auditable according to BCBS 239 & SOX<\/p>\n<\/div><\/li><\/ul><\/div><\/div> Automated data management from the source to the BI level – repeatable, testable, documented.<\/p>\n Automation in the data warehouse & lakehouse<\/strong><\/p>\n Data Warehouse<\/strong><\/p>\n<\/div> Layer model (landing, staging, core, marts)<\/p>\n<\/div><\/li> Template-based loading and transformation jobs<\/p>\n<\/div><\/li> Change data (CDC) & SCD automation<\/p>\n<\/div><\/li> Rule-based data lineage & versioning<\/p>\n<\/div><\/li><\/ul> Automatic orchestration & code generation<\/p>\n<\/div><\/li> Delta \/Iceberg tables, ACID & Time Travel<\/p>\n<\/div><\/li> dbt based models & tests as standard<\/p>\n<\/div><\/li> Scalable execution on Spark clusters<\/p>\n<\/div><\/li><\/ul><\/div><\/div> 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. <\/p>\n<\/div> Regulations & tests<\/strong><\/p>\n Standardized dbt tests<\/em>, user-defined Python checks<\/em> and SQL checks<\/em> per level. Rules are versioned, documented in a traceable manner and can be rolled out in a CI\/CD-controlled manner. <\/p>\n DQ-KPIs & Scorecards<\/strong><\/p>\n Visualization in the DQ data model: completeness<\/em>, consistency<\/em>, timeliness<\/em>, validity<\/em>, unambiguity<\/em>, etc. – including trend and cause analyses.<\/p>\n Compliance & Audit<\/strong><\/p>\n Auditable traceability in accordance with BCBS 239<\/strong> and Sarbanes-Oxley<\/strong> (SOX): lineage, control reports, dual control principle, technical and functional approvals.<\/p>\n Methodology & Templates<\/strong><\/p>\n We accelerate projects with metadata-driven templates<\/strong> for loading, transformation and test routes. This allows us to automatically<\/em> generate code<\/em>, orchestration plans<\/em> and documentation<\/em> – sustainably maintainable and expandable. <\/p>\n Typical deliverables<\/strong><\/p>\n<\/div> Source adapters & CDC pipelines (Airflow, Python, ETL Suite)<\/p>\n<\/div><\/li> dbt models incl. tests, seeds & snapshots <\/p>\n<\/div><\/li> Spark jobs for large amounts of data<\/p>\n<\/div><\/li> DQ regulations, scorecards & operational reports<\/p>\n<\/div><\/li> CI\/CD setups incl. automatic documentation <\/p>\n<\/div><\/li><\/ul> <\/strong><\/p>\n Technology stack (excerpt)<\/strong><\/p>\n<\/div> Apache Airflow, dbt, Python, Spark<\/p>\n<\/div><\/li> Delta Lake \/ Apache Iceberg<\/p>\n<\/div><\/li> Cloud DWH & Lakehouse: Azure, AWS, GCP<\/p>\n<\/div><\/li> ETL\/ELT: classic on premises tools<\/p>\n<\/div><\/li> BI tools for reporting & self-service<\/p>\n<\/div><\/li><\/ul><\/div><\/div><\/div><\/div> <\/strong><\/p>\n Based on the DQ history, we establish machine-learning-supported<\/strong> processes (e.g. anomaly detection) that signal deviations at an early stage and make data quality maturity levels<\/em> measurable.<\/p>\n Your added value<\/strong><\/p>\n Scalable automation<\/strong><\/p>\n Accelerated development through code generation, reuse and standards – with clear SLAs and operational indicators.<\/p>\n Reliable data quality<\/strong><\/p>\n Transparent KPIs, reproducible audit chains and audit trail – the basis for stable reports and analyses.<\/p>\n Investment security<\/strong><\/p>\n Platform-independent, cloud-capable, tried and tested on-premises. Modernization without lock-in. <\/p><\/p>\n<\/div><\/div><\/div> 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 modeling and BI tools. Our customers benefit from robust, future-proof solutions and a team that combines stability and innovation.<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div> DATA MART Consulting<\/p><\/div> Frequently asked questions answered briefly<\/p>\n<\/div><\/div><\/div>Why automation & data quality are crucial now<\/h3><\/div>
<\/span><\/div><\/div><\/div><\/div><\/div>Architecture & Orchestration<\/h3><\/div>
<\/strong>Data Lakehouse<\/strong><\/p>\n<\/div>Data quality management – graded & auditable<\/h3><\/div>
ML-supported data quality monitoring<\/h3><\/div>
<\/span><\/div><\/div><\/div><\/div><\/div>Experience & References<\/h3><\/div>
FAQ.<\/span><\/h2><\/div><\/div><\/div>