Data engineering services

With data engineering, businesses centralize all data in a single place, mitigating the risk of inconsistencies. It also lays the foundation for enabling real-time access to business intelligence, generating data insights, and adopting AI and data platforms. You can also use data engineering services to ensure your data operations remain effective with robust data pipelines. They clean and prepare structured and unstructured data for data-driven decision-making.

Yes. If you need a team of data engineers or data scientists to assess your current data flows and data management processes, identify data silos, refine the data infrastructure, or explore the available datasets, we are here for you. Our services include data strategy development, data governance assessment, and data migration planning. If you have a specific request, don’t hesitate to discuss it with us directly.

We use technologies like Apache Kafka, AWS Glue, and Google Dataflow to build data pipelines for data ingestion from multiple internal and external sources, including legacy systems. Our data integration engineering services involve conducting a comprehensive discovery phase to select the most suitable methods for data integration, reconciliation, refreshment, and data lifecycle management.

Yes, our data teams can build cloud-native data engineering solutions for Amazon Web Services, Google Cloud Platform, and Microsoft Azure. Those scalable solutions include data lakes and warehouses. They analyze vast datasets, enable seamless data processing, and support real-time data streaming. We can also assist you with setting up a hybrid data infrastructure that uses AWS, Azure, or GCP.

Yes. If you prefer to dedicate your in-house resources to core business functions instead of maintenance and support, we will take up these responsibilities after the solution’s launch. Our maintenance services include continuous solution monitoring and optimization, run-of-the-mill maintenance, and feature updates for new capabilities.

We identify the full scope of industry regulations and internal requirements protecting sensitive data before designing the pipelines and selecting the tech stack. In line with them, we implement data encryption, auditing, governance, validation, backup and recovery, and access management practices. Our expertise includes ensuring compliance with ISO and SOC2 standards and regulations like the GDPR.

Depending on your business goals, we optimize your data pipelines to support real-time data processing, support big data analytics, or enable seamless sync for large data volumes. We can also review and optimize your cloud data infrastructure to maximize its cost-efficiency. It guarantees high performance as data volumes, velocity, and variety increase.

Digital transformation initiatives require high-quality data to succeed. Carefully designed and implemented data pipelines ensure data consistency, security, and integrity, providing other digital solutions with high-quality data. A robust data architecture also facilitates access to the company’s data assets for generating real-time actionable insights and powering informed decisions.

Thanks to robust data engineering services, companies centralize raw data and ensure data integrity and quality. With that data, they gain business insights into their operations and customers and make data-driven decisions to achieve business goals. Data engineering is also valuable for automating time-consuming tasks and minimizing errors.

Hire data engineers to ensure your data infrastructure remains reliable and scalable. Also, you’ll maximize business continuity as the number of users, tools, and use cases grows. An optimized data infrastructure uses storage and computing resources more effectively, reducing infrastructure costs in the long run. Finally, data engineering prepares the infrastructure for AI/ML adoption.