Data Platform
Data Platform
Build high-performance end-to-end data processing and analytics solutions in a data-centric architecture
High performance data management system for a wide range of applications from classic regulatory issues to machine learning and artificial intelligence
Transparent process of data management and use. Minimizing errors with growing volume and complexity
Minimization of unit development costs and reduction of time-2-consumer
High performance data management system for a wide range of applications from classic regulatory issues to machine learning and artificial intelligence
Transparent process of data management and use. Minimizing errors with growing volume and complexity
Minimization of unit development costs and reduction of time-2-consumer
Orchestration, Data Processing & Transformation
The components provide orchestration, flow management, data processing and transformation tasks.
Depending on the tasks to be solved and the amount of data, the most suitable products are selected for the efficient and transparent implementation of data streaming and batch processing within the platform.
Data Lake
The Data Lake is optimized for scalability to handle multiple terabytes and even petabytes of data. Data usually comes from several heterogeneous sources and can be structured, semi-structured, or unstructured.
The concept behind the data lake is to keep all data in its original state without any transformations. Data in Data Lake is catalogued, transparent, manageable, and available for further transformation and use.
Operational Databases
An operational database is a database used for near-real-time data processing tasks, necessary to support operational activities and solve fast analytical tasks, data processing and transformation tasks immediately after they are received.
Analytical Databases
An analytical database is necessary to implement a historical data warehouse transformed into a single model for a specific subject area, as well as data in the form of data marts or spaces prepared for use with BI tools and analytical applications.
BI & Reporting tools
The BI & Reporting tools provide efficient data management for users of various categories, covering tasks from simple reports to advanced analytics.
Data Catalogs & Data Quality
Data cataloging tools provide transparency and a common understanding of data, its storage, transformation and use within the platform as a whole.
Data catalogs provide collection and management of various types of metadata, as well as a unified business description (business glossary), dependency tracking (lineage), creation and presentation of unified catalogs of analytical indicators, objects, reports, and data products. Particular attention is paid to data quality control and data profiling as the basis for the efficient and useful use of data.
Orchestration, Data Processing & Transformation
The components provide orchestration, flow management, data processing and transformation tasks. Depending on the tasks to be solved and the amount of data, the most suitable products are selected for the efficient and transparent implementation of data streaming and batch processing within the platform.
Data Lake
The Data Lake is optimized for scalability to handle multiple terabytes and even petabytes of data. Data usually comes from several heterogeneous sources and can be structured, semi-structured, or unstructured. The concept behind the data lake is to keep all data in its original state without any transformations. Data in Data Lake is catalogued, transparent, manageable, and available for further transformation and use.
Operational Databases
An operational database is a database used for near-real-time data processing tasks, necessary to support operational activities and solve fast analytical tasks, data processing and transformation tasks immediately after they are received.
Analytical Databases
An analytical database is necessary to implement a historical data warehouse transformed into a single model for a specific subject area, as well as data in the form of data marts or spaces prepared for use with BI tools and analytical applications.
BI & Reporting tools
The BI & Reporting tools provide efficient data management for users of various categories, covering tasks from simple reports to advanced analytics.
Data Catalogs & Data Quality
Data cataloging tools provide transparency and a common understanding of data, its storage, transformation and use within the platform as a whole.
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Multi-component and flexible architecture, corresponding to the specifics of tasks and business scale
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Distributed data storage and processing to optimize infrastructure costs as data grows
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Standardized models and flexible data organization approaches (DeltaLake, DataVault 2, Anchor, Hybrid) to increase development speed and eliminate bottlenecks in loading new data
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Data catalogs and glossaries to provide transparency and shared understanding of data for IT and business users
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Data lifecycle management, effective understanding of the origin and use of data
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Horizontal scaling and cloud technologies to support unlimited and efficient solution scalability
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Support for DEV and DataOps processes for agile development methodologies and seamless evolution of the data platform
- Databricks is cloud-native Data Intelligence Platform. Databricks pioneered the lakehouse architecture
- State-of-the-art managed Spark ETL processing in cost-efficient way by Databricks compute
- Unified security, governance, and cataloging using Unity catalog
- Unified data storage for reliability and sharing by Delta lake utilizing Azure ADLS storage
- Databricks SQL for effective serving of BI reports and other SQL-queries
- Managing of a full MLOps lifecycle with Feature Store and MLflow of any complexity incl. LLMs
- Snowflake is a leader in cloud analytical databases
- Allows usage of any major hyperscalers (AWS, Azure, GCP)
- Combines strong sides of best-in-class solutions like Informatica (IDMC) and dbt (both - Core&Cloud)
- Easy to use by BI and analytical teams with strong SQL knowledge
- Good integration with majority of BI and AI tools
- Rduces lock-in effect
- Loading data without transformations or with minimal transformations
- Quality control processes in Hadoop
- The data presentation layer is represented as a series of Postgres RDBMS instances implemented using a virtualization system
- Hadoop is designed to serve analytical workloads
- The Postgres RDBMS is designed to serve mixed workloads, both transactional and analytica
- Hadoop is used as the main data store, simplifying data management processes in terms of metadata integration and access control mechanisms. It also offers a rich set of data mining tools, including machine learning and artificial intelligence methods
- Greenplum is a massive parallel relational database that serves as a data storage and data processor
- Data transformation (ETL) is performed prior to loading into the data storag
- The lack of loosely integrated components allows you to use the standard audit tools and descriptions of metadata offered by the Greenplum DBMS, without the need for subsequent consolidation of metadata and the development of third-party access management synchronization processes
- Business glossary functions are implemented through a third-party product that provides these functions
- For tasks of search analytics, machine learning, artificial intelligence, comprehensive data mining, Hadoop is used to isolate the workload generated by these processes from the main processes
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Ensuring high-quality quality data for business tasks to maximize their effectiveness
- Improving data availability and reducing development time
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Transparency of change processes and a single tool for a holistic understanding of data at the organization level
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Foundation for business transformation in deep analytics, artificial intelligence and machine learning
Case Studies
