Data Lake implementation for companies that want to use their data, not just store it
We consolidate information from your systems into a governed data lake, expose it via microservices, and make it ready for your organization to use in analytics and artificial intelligence.
Request a diagnosis of your data architecture
Companies in the US and Latin America already trust us
We design, build, and operate the data platforms used daily by business teams, analysts, and applications.
Data lake, microservices, and AI under a single architecture
Your data already exists, but it doesn't talk to itself: It is scattered across ERPs, CRMs, and spreadsheets. The data lake brings it all together in a single, governed location.
Storing data is not the same as using it: A cross-cutting microservices layer securely delivers that data to your applications, teams, and partners.
AI needs that foundation: Models and agents consume the same governed data via microservices—without parallel copies or direct access.
Data Lake: unified and governed data
Microservices: data access via APIs and events
AI: models and agents in production
Software outsourcing: teams in Panama, the USA, and Colombia.
Who builds and operates the platform?
Rootstack has a proven track record as a software outsourcing company. We design, integrate, and maintain every layer from start to finish. Your data is always in expert hands.
What must the platform solve?
Ensure that every piece of data has a clear origin and owner, and that any application or AI model can consume it without custom integrations, and that it scales when the business demands it.
Data working together: What does your company gain?
A single version of the truth: Finance, sales, and operations look at the same number. No one manually consolidates reports across systems.
Data-driven decisions today: Information arrives in minutes, not at month-end, and teams take action before a problem escalates.
New applications in less time: Data is already served via microservices. An app, a portal, or a partner connects to what already exists.
Production-ready AI: With reliable data and a secure way to consume it, your AI use cases move beyond the pilot stage and into the business.
We strive 100% to achieve excellence for our clients
Rootstack helped a retail company consolidate sales, e-commerce, and loyalty data into a data lake. A customer microservice now provides the same view to both marketing and customer service.
Tech Lead
Event-based ingestion was implemented from the core banking system to the data lake, using APIs that feed the risk engine. Analysis no longer relies on overnight batch loads and instead takes place at the time of the request.
CTO
A generative AI assistant was developed that queries inventory, orders, and deliveries via data microservices. The team receives answers in natural language without having to request a report from IT.
Project Manager
Rootstack helped a retail company consolidate sales, e-commerce, and loyalty data into a data lake. A customer microservice now provides the same view to both marketing and customer service.
Tech Lead
Event-based ingestion was implemented from the core banking system to the data lake, using APIs that feed the risk engine. Analysis no longer relies on overnight batch loads and instead takes place at the time of the request.
CTO
A generative AI assistant was developed that queries inventory, orders, and deliveries via data microservices. The team receives answers in natural language without having to request a report from IT.
Project Manager
Rootstack helped a retail company consolidate sales, e-commerce, and loyalty data into a data lake. A customer microservice now provides the same view to both marketing and customer service.
Tech Lead
Event-based ingestion was implemented from the core banking system to the data lake, using APIs that feed the risk engine. Analysis no longer relies on overnight batch loads and instead takes place at the time of the request.
CTO
A generative AI assistant was developed that queries inventory, orders, and deliveries via data microservices. The team receives answers in natural language without having to request a report from IT.
Project Manager
What happens after you write to us?
1
Diagnosis of your data
An inventory would be created of their data sources, how they are currently integrated, and which use cases offer the highest return.
2
Architectural design
The data lake zones, priority microservices, and the cloud are defined—as well as what falls outside the scope, which matters just as much as what is included.
3
Phased implementation
One source is integrated at a time, starting with the lowest-risk use case, so that the business sees value from the very first delivery.
4
Delivery and support
What remains is the documentation, the knowledge transfer to your team, and support during the agreed-upon period.
The technologies with which we connect, automate, and protect
Frequently asked questions about our services
1
Difference between data lake and data warehouse
The data warehouse stores structured data ready for reporting. The data lake stores data of any format at a lower cost, and the lakehouse architecture combines the best of both.
2
Do we need to replace the systems we already have?
Not necessarily. In most cases, existing systems (ERP, CRM, core systems, databases) are connected as data lake sources rather than being replaced, which reduces risk and cost.
3
How long does a data lake project take?
The initial diagnosis takes 15 days, the design about 4 weeks, and the phased implementation between 6 and 12 weeks, depending on the number of sources to be integrated.
4
How much does it cost?
It depends on the number of sources to be integrated and their condition. The assessment concludes with a data map, a proposed architecture, and an estimate, enabling data-driven decisions.