Our team of qualified and experienced Data Engineers and Consultants will create high-performance infrastructure and optimize your data to help you make better decisions and achieve your business goals.

Data Engineering

Design and build data repositories to discover better insights with diversified data engineering services

Architect the right data solutions.

Architecture Design

Leverage expert advice to select the right cloud or on-premises architecture incorporating ETL/ELT pipelines, data-warehouse/data-mart/data-lake, BI tools, and ongoing governance processes.

Conceptual and logical data modelling

Creation of data integration and quality management policies and rules

Design of a DWH, data marts, and OLAP cubes

Design of dashboards for different user roles with specific KPI sets

Creation of data security and governance standards and policies

ETL Pipelines

Enable critical decision-making with clean and fresh data.

Building robust data pipelines to continuously stream in clean and standardized data from different sources is a must to support on-demand business intelligence.

Experienced data engineers with a wealth of experience building ETL/ELT pipelines

Data Integration: Consolidating structured and unstructured data from different sources

Data Transformation: Effortlessly converting raw data from one format into another format or structure that’s cleansed, validated, and ready-to-use.

Data Storage: Fetching data from multiple sources and storing them in one place using on-premises or cloud storage for analytical reporting.

ETL/ELT DATA SERVICES

Proof of Concept (POC) Roadmap.

Technological solution’s roadmap of advanced analytics (POC), which is a detailed plan for testing and evaluating new technologies/tools in the advanced analytics field. This is to prove the existence & implementation of a continuous improvement mechanism. specifications as per (NDMO)

Proof of Concept

Proof of Concept (POC) Results.

• An Executive Summary: A brief overview of the main objectives & key results.

• The Methodology: A Description of the methodology used in conducting the POC & performing the periodic tests of the technological solution.

• The Results: A detailed analysis of the POC results & the periodic testing results, including the solution’s strengths & weaknesses, predictions accuracy, and any limitations or issues encountered.

• Recommendations: Any insights or recommendations derived from the results.

• The Conclusion: A conclusion that summarizes the POC results & highlights the business implications.

• The Future Directions: A discussion of the project’s potential next steps, including any recommendations for further development, testing, or deployment.

• Appendices: Supporting materials such as technical documentation, programming code samples, data visualizations, and interfaces.

• The Scope: Specifying the scope of the new technological solution’s POC, including the technologies being tested, data sources, and the target audience.

• The Success Criteria: Identifying the success criteria and metrics that will be used to assess success.

• The Timeline: Setting realistic timetables, including start & end dates and milestones.

• Resources: Determining the resources needed to implement the new technological solution’s POC, including technologies, data, employees, and budget.

• Risks & Challenges: Identifying potential risks & challenges associated with the new technological solution’s POC and develop a mitigation plan.

• The Communication Plan: Developing a communication plan to keep stakeholders informed about the progress and the results of the new technological solution.

• Next Steps: Determining the next steps following a POC, such as upscaling the new technological solution, performing extra tests, or additional implementations.