Technologies & Strategy
Modern technologies play a key role in our efficient and precise data processing approach.
We use advanced tools and platforms to ensure accuracy, speed, and reliability at every stage, empowering you to rely on your data for timely decision-making and business growth.
Data Processing
Data Analytics
Data Visualization
We use a range of platforms for extracting, transforming, and preparing data for analysis, enabling efficient handling of complex ETL processes:
Azure Data Factory: A cloud-based platform for data integration and orchestration that supports automated ETL pipelines—extracting from diverse sources, transforming for analysis, and loading into data warehouses or centralized systems.
Microsoft SSIS (SQL Server Integration Services): A robust ETL tool designed for data extraction, transformation, and loading from diverse sources.
Microsoft Fabric – Dataflows Gen2 & Notebooks: These tools provide advanced data transformation capabilities within the Fabric ecosystem. Dataflows Gen2 uses Power Query for transformation, while Notebooks allow the use of Python, SQL, and Spark for complex analytical tasks.
Microsoft Fabric - Data Pipelines: Used for orchestrating large-scale data workflows includes
Mirroring for automatic real-time synchronization
Copy Data for scheduled data replication, between systems and much more.
This toolset ensures flexible and reliable data processing, keeping data consistent, accurate, and ready for analysis and reporting.
Microsoft Fabric is a comprehensive analytics solution that offers a unified platform for data processing, analysis, and visualization. It accommodates a range of programming languages and tools, such as Python, SQL, and Spark, enabling users to choose the best methods for their analytical requirements.
Azure Synapse Analytics: Formerly Azure SQL Data Warehouse, Synapse combines data warehousing with advanced analytics capabilities. It offers both serverless and dedicated capacity options based on client needs. It combines big data processing, data integration, and analytics through SQL, Spark, and Data Explorer.
SQL Server Analysis Services (SSAS): A powerful tool for advanced data analysis, allowing the creation of OLAP cubes and tabular models.
SQL Server Reporting Services (SSRS): A tool for producing customized PDF reports from various data sources, with options for automated delivery via email or shared locations.
Data visualization consists of several essential steps:
Data Preparation & Modeling
We collect, clean, and transform data using M or Power Query, then build a data model that connects tables through logical relationships to enable efficient analysis.
Key performance indicators (KPIs) are defined, and measures are created using DAX (Data Analysis Expressions), in close collaboration with the client to ensure alignment with business goals.
Design & Interactivity
Our visualizations focus on simplicity and clarity, enabling easy understanding of the data.
We prioritize placing filters in the filter pane, while keeping only the essential slicers on the report canvas to maximize workspace usability.
We ensure interactivity and enable drill-down analysis through hierarchies for deeper insights.
Testing, Optimization & Distribution
Before sharing with users, every report is tested and optimized for performance.
The final report is published to the Power BI Service with access permissions and automated refresh schedules configured.
We ensure secure access and data protection by applying Row-Level and Object-Level Security, making sensitive content visible only to the intended users.
Administration plays a key role and is an integral part of every business report.
Best Practices We Follow
Clarity & Focus
We emphasize key metrics and adjust the complexity of visuals based on the client’s preferences. Visualizations are designed to be intuitive and help users make decisions quickly and confidently.
Creative Design
We apply color palettes that reflect the client’s brand identity. We use clean fonts, tones, and visual styles that ensure both readability and a pleasant user experience.
Data Model Optimization
We follow best practices in data modeling to ensure report efficiency. This includes reducing model complexity and removing unnecessary data to maintain optimal performance.
Microsoft Fabric architecture
Link to the source page: Microsoft Fabric