We select and implement technologies that accelerate the delivery of data solutions, reduce errors, and prepare your data platform for AI. All without vendor lock-in and with a clear, measurable ROI.
Coalesce enables visual development of data pipelines with column-level lineage and automatic documentation. It allows teams to quickly build consistent data models and onboard new team members in days, not weeks.
WhereScape automates the full data warehouse lifecycle in cloud and on-premise, from reverse engineering to deployment and documentation. The result is consistent code and metadata as a single source of truth.
Snowflake provides flexible, scalable performance without infrastructure management, with support for structured and unstructured data and an AI-ready architecture powered by Snowflake Cortex. You only pay for what you use.
Dawiso connects technical metadata with business context, including data ownership, meaning, and usage. Without this context, any AI initiative is built on an uncertain foundation.
BiG EVAL automatically tests data quality across the entire pipeline, including reconciliation, regression testing, anomaly detection, and referential integrity checks. It ensures provable and reliable data quality.
Dbt is a platform for SQL-based transformations with version control, testing, and automatically generated documentation. It enables building data pipelines directly in SQL, with an open-source foundation and strong support for cloud environments.
Databricks provides a unified platform for data engineering, analytics, and AI. Its lakehouse architecture combines the benefits of data lakes and data warehouses in a single environment, with native support for Python, SQL, and machine learning.
Datavault Builder brings a complete environment for Data Vault 2.0, covering modeling, code generation, testing, and deployment. It enables fast and consistent data warehouse development, even for smaller teams.
Valutspeed is a no-code platform for automating Data Vault modeling and data pipeline generation. It bridges the gap between business design and technical implementation, from conceptual models to production-ready code.
As a cloud‑native solution, Agile Data Engine is a metadata-driven platform for automating data warehouse development. It offers built-in CI/CD, automated schema management, and full data lineage from source systems to the business layer.
Monte Carlo is a data observability platform that uses machine learning to detect anomalies in data. It monitors changes in freshness, volume, schema, and distribution, helping identify issues before they impact users.
Astronomer provides a managed platform for Apache Airflow that simplifies data pipeline orchestration. It provides enterprise-grade monitoring, CI/CD, and infrastructure management without the need for in-house operations.
Acceldata is a data observability platform covering the entire data stack across cloud, on-premise, and hybrid environments. It monitors data, pipelines, and infrastructure, providing visibility into both performance and costs of data platforms.
We systematically design and implement functional data warehouse (DWH) enhancements for Komerční banka. Beyond standard BI services, we've developed a new information management strategy for the Global Competence Centre unit. Additionaly, we provide Komerční banka with our Dawiso platform, enabling the management of metadata from over 170,000 DWH tables, hundreds of reports, and thousands of terms across various dictionaries—all in one centralised location.
As a trusted long-term partner in strategic information management planning, we’ve supported Kooperativa in setting up effective BI development processes and, more recently, in implementing a new data portal. Powered by our proprietary Dawiso platform, this portal serves as a central hub for all data-related information.
At Stora Enso, we were entrusted with implementing and managing WhereScape to ensure seamless integration within the complex environment of a large multinational company. We established best practices, designed the structure and functionality of the data warehouse, and have continuously supported its long-term development through necessary updates and ongoing system improvements.
Modern data platforms take over routine work and give people the space to focus on what truly creates value. Talk to our team to learn how to bring this approach into your organization.
