Building a data warehouse
and big data

We build a data warehouse and big data architecture, supporting advanced
analytics, ensuring the storage of resources from dispersed sources,
streamlining the reporting process and conducting accurate analyzes.

  • Services
  • Building a data warehouse

Business data modeling

By modeling the data, you will document your information resources,
how you use them, and the requirements of the business.

Data modeling makes,
Your organization:

Creates platforms for cooperation between IT and business.
It has the ability to improve business processes by defining data needs.
It minimizes data chaos, reduces the risk of data redundancy, while increasing data integrity.
Increases the speed and efficiency of data processing and analytics through resource planning and technology scaling.

Conceptual models

The conceptual data model defines the overall structure of a company's data. It is used to describe the company's business model, as defined by the company's stakeholders and data architects.

Logic models

The logical data model is based on the conceptual model and refers to individual business objects, described by specific data attributes within each entity and specific relationships between these attributes. This model helps make decisions to develop data and meet the information needs of the company described by the physical model.

Physical models

A physical data model is a specific implementation of a logical model (entity model) described by metadata. It is created by database developers and administrators in close cooperation with data architects. It is developed with a specific technology and tools in mind to support databases, data warehousing and data interfaces that enable data to be managed on business platforms and in applications, in the manner chosen by business users.

Building a data warehouse and big data

Oracle, Microsoft , PostgreSQL, Hadoop, Vertica

A data warehouse is a specialized database that collects and stores resources from distributed sources. Its aim is to properly structure them so that they are properly divided thematically. Thanks to this, data warehouses can be used not only for data archiving, but also for reporting and conducting in-depth analyzes.

Big data is a solution for large and diverse data sets coming from many sources, enabling advanced data analysis.

Proper data collection and processing allows you to obtain valuable information
that can be used in the implementation of goals contributing to the development of the company. 

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Benefits data warehouse and big data

Dostęp do kluczowych informacji

Zapewnia jedno źródło prawdy i łatwy dostęp do najważniejszych informacji, przy okazji dbając o ich wiarygodność, bezpieczeństwo i poufność.

Integration from multiple sources

Combine data from many different sources to provide a unified view in the data warehouse.

Real-time data analysis

Dzięki wydajnym silnikom analitycznym umożliwia szybką rekcję na zapytania, porządkuje i usprawnia procesy analityczne.

Thematic classification

Tematycznie porządkuje różne obszary analityczne, ułatwiające szybszy dostęp do niezbędnych informacji.

We support clients in all phases of the project

From consulting, through designing data warehouses and big data and implementation of solutions,
to the implementation of a ready system and its further development.

Consulting of technology selection

Designing a data warehouse and big data

Implementation of
a data warehouse

Data warehouse maintenance


When building data warehouses and big data, we use various solutions
depending on the data volume, expectations and financial possibilities of the client

  • In the case of large data volumes, we offer hybrid solutions based on proven big-data, commercial and open-source technologies.

  • We use open-source ETL tools, paid ETL tools or high-performance online replication tools to power the data warehouse.

  • We use tools from Oracle, Microsoft, Pentaho, Vertica and elements of the Hadoop ecosystem.

Data transformations
and processing

ETL is the process by which data extracted from any source is transformed
into the appropriate format for further processing and storage.

narzędzia ETL

ETL processes are the core processes for feeding and maintaining data in data warehouses. Therefore, data volumes that are growing day by day, as well as increasingly demanding analytical processes and ensuring data trust, require support from ETL tools (such as Oracle Data Integrator, SQL Server Integration Services, Azure Data Factory).

Using such databases and ETL tools greatly simplifies the task of managing data and metadata, while streamlining the data warehouse.

Our Partners


We recommend Vertica's most advanced data warehouse, which allows organizations to keep up with the size and complexity of massive amounts of data. By replacing your traditional enterprise data warehouse with Vertica Analytics, you can change the dynamics of your industry (retail, healthcare, telecommunications, energy and more).

Vertica - wielozadaniowa platforma danych

Vertica columnar architecture
of the data warehouse

VERTICA is well-known standards - SQL language, ACID transactions, JDBC interface. The platform also works with popular data extraction, transformation, and loading (ETL) and business data analysis (BI) products. The biggest innovation is the way it works. VERTICA has been designed with great emphasis on minimizing the time of writing and reading operations from hard drives, and also provides standard support for grid computing environments. It is a solution for the 21st century, created especially for today's complex BI and machine learning applications that perform a lot of data reading operations.

Column architecture

Data warehouse architecture - storing data in a column layout significantly improves query execution (20 to 100 times faster) because it eliminates unnecessary I / O operations on disks and in memory.

Distributed processing

It provides high data availability and improves search performance because the queries are executed on the projections with the most appropriate column set and sort order for a given question.

Intense compression

High compression - tables take up 90% less space. The innovative query engine works directly on compressed data, which means fewer processor cycles are needed to process the compressed table.

Necessary mechanisms for security

The built-in data warehouse design tool provides the necessary mechanisms for data security (redundancy) so that a failure does not disrupt the operation of the entire system. This approach avoids any degradation in database performance.

Valuable information and useful insights discovered in your data every day

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