Datumo

DATUMO sp. z o.o. is a consulting company focused on big data platforms and cloud migration services. The organisation is headquartered in Poland. Project descriptions and client references indicate operations with European data-driven companies and teams across multiple sectors.

Services and capabilities

• Data platform design and development: architectural design and implementation of data platforms on Google Cloud Platform, Microsoft Azure and Snowflake, including data lakes and data warehouses built on BigQuery and Snowflake.
• Data platform migration: planning and executing migrations to modern cloud services; developing migration tools and implementing data synchronization during platform transitions.
• Platform modernization and DevOps automation: deploying schedulers (for example Apache Airflow), applying GitOps/DataOps practices, provisioning developer environments with Terraform, and automating platform operations.
• Data pipelines engineering: designing and automating ETL/ELT workflows using technologies such as Spark, Databricks, DBT, BigQuery and Airflow; implementing data quality, reconciliation and KPI validation processes.
• Real-time systems and streaming: architecting and building event-based streaming pipelines with components such as Kafka, Event Hub, Spark Streaming, Druid and related connectors; implementing scaling and fault-detection mechanisms for streaming workloads.
• Observability and cloud cost management (FinOps): building monitoring and alerting solutions for resource utilization and cost, conducting cloud and Snowflake/Databricks cost analyses, and tuning Spark jobs for efficiency.
• AI model deployment and MLOps: deploying, monitoring and maintaining machine learning models in production; developing custom models and providing ongoing model support and tuning.

Industry focus

• Life sciences: collaboration with organisations in the life science sector on data and analytics projects, delivered alongside domain specialists.
• Transportation and mobility: projects for taxi and mobility services involving end-to-end data platform implementation, data integration, and support for real-time use cases.
• E-commerce and marketplaces: work for online marketplace platforms including platform improvements, data processing and operational support.
• Digital platforms and gaming-related marketplaces: implementation of platform services, knowledge transfer and enhancements to core data processes.

Technology and delivery approach

• Technology stack commonly used: Google BigQuery, Snowflake, Databricks, Apache Spark, Airflow, Kafka, Event Hub, Druid, DBT, Azure Data Factory and Terraform.
• Delivery patterns: architecture and implementation of cloud-native data platforms, migration planning and tooling, automation of pipeline deployment, and operationalization of monitoring and alerting systems.
• Data engineering practices: emphasis on ETL/ELT automation, data lineage and cataloguing, KPI validation and reconciliation, and performance tuning of compute workloads.
• Operational practices: adoption of GitOps/DataOps and DevOps workflows, automated provisioning of environments, regular reporting on resource utilization and cost, and ongoing knowledge transfer and training for client teams.
• AI and MLOps: deployment and maintenance of machine learning models in production, continuous monitoring and model fine-tuning, and integration of AI pipelines with cloud data platforms.