Data Engineering

Our Data Engineering services encompass the design and development of robust data architectures, as well as Big Data operations, migration, and advanced analytics. We create scalable data models that ensure high data quality and governance while enabling efficient processing and analysis of large datasets. Our comprehensive approach includes seamless data integration, modernization through migration to cutting-edge platforms, and the use of powerful big data tools to drive insightful analysis and support your business goals.

130+ employees

Data Architects

Data Engineers

Data Analysts

Big Data Operations Managers

Data Scientist

Machine Learning Engineer

Business Intelligence (BI) Developer

Data Governance Specialist

Tools

Case Study

Create a comprehensive Big Data platform integrated with various services for managing and analyzing large datasets. This platform supports scalable data storage, real-time processing, and advanced analytics.

Technologies

Apache Hadoop, Spark, Kafka, AWS Redshift

Key Benefits

The platform allowed clients to efficiently handle vast amounts of data, improving overall data management and processing.
The implementation of real-time processing technologies led to a 40% reduction in the time required to generate actionable insights, facilitating quicker data-driven decisions.
With advanced analytics, clients gained deeper insights, enabling more informed and effective decision-making.
The platform’s scalable data storage and processing capabilities ensured that clients could manage growing data volumes without compromising performance.

Migration of the data platform to a modern Big Data architecture using Apache Spark and Hadoop. This project involved transferring legacy data systems to a scalable and efficient big data platform, enabling advanced analytics and real-time data processing.

Technologies

Apache Spark, Hadoop.

Key Benefits

Migration to Apache Spark and Hadoop cut data processing times by 50%, enabling faster insights and better decision-making.
The scalable architecture allows seamless integration of new data sources, supporting future growth without performance loss.
Real-time analytics enable proactive business strategies, boosting operational efficiency and competitiveness.
The modern architecture offers advanced analytics for deeper insights and more informed decisions.

Implementation of a data warehouse solution using Snowflake to consolidate data from multiple sources. This project aimed to streamline data analytics and reporting processes by providing a single source of truth.

Technologies

Snowflake, SQL

Key Benefits

Snowflake implementation reduced data silos and inconsistencies.
Faster and more accurate analytics reduced reporting time by 30%.
Reliable data enabled better-informed decisions.
Efficient data management improved operational efficiency and competitiveness.

Create a real-time data processing system using Apache Kafka and Apache Flink. This project was designed to handle real-time data streams for better decision-making and operational efficiency.

Technologies

Apache Kafka, Apache Flink.

Key Benefits

Real-time data streams enhanced decision-making.
Apache Kafka and Flink improved operational efficiency and response times.
Real-time processing improved agility and responsiveness.
Faster service and responsiveness boosted customer satisfaction and competitiveness.

Data Engineering