Data and Analytics

Data Engineering and Warehousing

Ascella's Data Engineering and Warehousing solutions bring data from various platforms and departments together to create a single source of truth.

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Ascella offers a range of services to help companies build data warehouses that integrate with the rest of their technological infrastructure to support decision-making processes.

We have extensive experience in building data warehouses that leverage both structured and unstructured data from various systems. We pride ourselves on our ability to extract value from data through well-planned data engineering and warehousing processes.

What we do

 Requirement Gathering

Our team of subject matter experts work closely with clients to identify their business requirements so that we can develop a solution that meets them precisely.

 Data Governance and Integration

We achieve seamless integration between various systems by identifying the gaps in client’s data infrastructure and establishing a roadmap for improvement upfront.

 Data Modeling

We work with business stakeholders to develop a model that represents their business requirements in a relational database schema. We also define the physical layout of tables and indexes based on performance characteristics.

 Physical Environment Setup and Hosting

We help set up an environment where our solutions can be hosted securely, while also taking care of security concerns like compliance with industry standards like HIPAA and PCI DSS.


We design and develop ETL processes to extract data from various sources, transform it into an appropriate format for reporting, store it in the warehouse database and maintain its integrity over time. We also build tools that can be used by other analysts through web services or an API (application programming interface).

 Query Optimization

We analyze queries generated by end users to identify opportunities for performance improvement such as high cardinality columns or complex joins. We often develop query optimization algorithms using machine learning techniques like decision trees or random forest classifiers.

 Quality Assurance

We ensure that any changes made during testing don't impact existing functionality or cause new bugs once launched in production.