Your data is there. Let’s make it work for you.

Data engineering connects, transforms, validates, stores and distributes information so it can be used in applications, reports or artificial intelligence projects. It replaces manual tasks with repeatable processes and makes data available according to the project’s needs.

It may include source analysis, integrations, ETL or ELT pipelines, database design, storage and data quality checks. We can also deploy and maintain the processes. Tasks, deliverables and support are defined around your needs, without requiring a complete data platform.

No. We can start by reviewing the sources, formats and processes you already use. We identify access, quality or update issues and prioritise the work to help you use the information available.

Yes, provided the systems allow the required access. We review APIs, permissions, formats and source limits to design the integrations. We can connect internal applications, external services, databases and files without replacing all your tools.

A pipeline is a sequence of processes that moves and prepares data from its sources to a destination. It can help if you regularly import files, combine information from different applications or update data manually. We define frequency, transformations and checks around the intended use.

In ETL, data is extracted, transformed and then loaded into its destination. In ELT, it is extracted and loaded first, then transformed in the destination environment. The choice depends on sources, volume, storage and processing requirements; we review your situation before proposing an approach.

Yes. We can build processes to import, transform and validate files automatically. We review their structure, how they arrive, how often they change and what should happen when fields are missing or errors occur. The flow can include logs and notifications to support tracking.

It depends on how you want to use the information. A database may be enough for a specific application or integration. A data warehouse organises data for analytical queries; a data lake can store information in different formats. We assess your sources, queries and volume before recommending an architecture.

Yes. We build processes to collect, transform and update the data used by your analytics tools. We agree on indicator definitions, quality checks and update frequency so reports use consistent information.

We define checks for formats, required fields, duplicates, ranges and relationships between data. We can add logs and alerts to detect issues and support review. Checks and error handling are agreed according to the project’s requirements.

Yes. We can design periodic processes or flows that process information as it is generated. We choose the approach around the required update frequency, volume, sources and operating cost. Not every project needs real-time processing.

Yes. We can review existing processes, fix errors, optimise queries or add quality checks. We can also modernise parts of the system gradually. Work can focus on a single integration or process without building a new platform.

Yes. We can build extraction, transformation and validation processes to prepare and update the datasets used by AI models and applications. We define formats, quality rules and update frequency with the team that uses the information.

We review which systems and data each process needs with you. We agree on permissions and credential handling, aiming to limit access to the tasks required. Security requirements and responsibilities are defined around the project environment.

The quote depends on sources, integrations, volume, transformations and update frequency, as well as deployment and maintenance needs. We can work hourly, with a monthly allocation or on a fixed-scope project. Financial terms and scope are agreed before we start.

We can maintain and improve the processes we build, monitor their operation and adapt them when sources or requirements change. Scope, response times and included tasks are agreed around the project. 24/7 support with an SLA is contracted and priced separately.