Challenge:
A cost-effective solution providing scalable storage capacity for enterprise Big Data sets.

Solution:

To transfer rarely used data and part of ETL processes to Hadoop. Also, to utilize data virtualization to build a data mart for BI.

Result:
An increase in ROI of 200% and 500% for levels 1 and 2, respectively. Cost reduction of up to 75% on data organization and provision for further analytics.

Challenge:
Creation of a multifunctional center for aggregation, processing and presentation of data along with secured real-time exchange of enterprise data.

Solution:

The structure of functional components:

• Data Broker (Kafka) – ensures real-time data exchange.
• Big Data Platform (Hortonworks / Cloudera Hadoop Data Platform, Hadoop Data Float) – stores all data coming through a data broker.
• Logical Datawarehouse (Tibco Data Virtualization) – a logical data warehouse, a tool for business users to access and manage data. Also, it enables fast data access for online monitoring of the end-to-end technical process (from data collection to data usage), and provides data for technical monitoring.
• Data Governance – data management, includes data quality and security.

Result:
Scalable architecture built on Apache Kafka for real-time data exchange, 24/7. The number of processed events is about 2-3 million per day, and up to 200 events per second. Optimization of data governance, with regard to data quality and security.

Challenge:

To provide the customer with an effective tool for measuring sales department staff KPIs.

Solution:

• Building of Azure Cloud infrastructure. Azure Blob Storage containers formed the basis of the architecture. Process automation of uploading local files into blob containers along with data enrichment.
• Creation of Azure Analysis Services model based on business logic and customer’s data to measure employee KPIs.
• Building of Power BI business analysis dashboards, integration between Power BI and the AAS model. Process automation of daily data uploads, recalculation of both individual and total KPIs with regard to the date, metrics, city, and branch.
• Three set AD access groups, defined AAS data filtering rules, proof tested process of user transaction from one group into another.

Results:

Creation of a powerful tool for measuring sales department staff KPIs. Building of a robust Azure Analysis Services model and Power BI business analysis dashboards. In-depth analytics leveraging the insights of ML, Azure Cognitive Services and EDWH data. A single reliable reporting method has been ensured.