Data Engineering
Enterprise data engineering: automated pipelines that collect, integrate and deliver data from multiple sources — up to date, secure and ready for analytics and AI.
Data flowing like connected highways: every piece of information in the right place at the right time
Organizing and structuring data is the foundation of any effective data strategy. Digiage's Data Engineering service collects, integrates and processes information from multiple sources to make it accessible, secure and ready to use.
Beyond optimizing the flow, we ensure data is up to date, properly stored and protected, raising the quality of analytics, reporting and decisions.
What a data pipeline is and how Digiage builds yours
Pipelines are automated flows that connect systems, organize data and deliver the right information at the right time, with no manual steps along the way.
-
Collection and integration
We connect internal systems, platforms and external databases and bring the data into a centralized architecture.
- ERP
- CRM
- applications
- APIs
-
Transformation
We cleanse, standardize and organize information, eliminating redundancy and ensuring quality.
- standardization
- quality
- governance
-
Delivery
We deliver up-to-date, trusted data to dashboards, reports, advanced analytics and AI models.
- BI
- reports
- AI
From bottleneck to strategic asset: data modernization at a payments giant
One of Brazil's largest digital transaction companies, processing roughly 8% of the country's GDP, rebuilt its data foundation with Digiage without stopping operations for a single day. The legacy and new platforms ran side by side until cutover.
- Databricks Lakehouse
- Delta Lake
- Unity Catalog
- Databricks SQL Serverless
- 4.500+tables modernized
- 2.700+processes migrated to production with full stability
- 1.393obsolete processes eliminated
- 0days of operational downtime
- FoundationNew architecture and governance standards
- Controlled coexistenceLegacy and new environments side by side
- Enterprise adoptionTeams move to the new platform
- Value creationAnalytics, conversational data and AI agents
Benefits of data engineering
Technology, automation and governance to strengthen your data infrastructure — efficiently, at scale and aligned with business goals.
- Data in inconsistent formats and standards
Structured, standardized data for strategic use
- Spreadsheets and manual loads
Automated data collection, transformation and delivery
- Siloed systems that don't talk to each other
Diverse sources integrated into a centralized architecture
- Slow reports and rework
Better analytics performance and less manual effort
- Different numbers for the same question
Decisions based on up-to-date, trusted data
- Undocumented, uncontrolled routines
Lower operational risk and stronger data governance
Results achieved with some of our clients
What changes when data flows without bottlenecks.
Faster, more accurate decisions
Organized information, accessible in real time.
Security and continuous updates
Data protected against failures and vulnerabilities.
Lower operating costs
Automated processes that save time and resources.
Ready for innovation
A solid foundation for new technologies and continuous process improvement.
Data engineering on the leading data and cloud platforms
We build data pipelines and architectures on market-leading technologies such as Databricks, Microsoft Azure and AWS, ready to scale with your business.
- Databricks Best Use Case Latam · 2025 and 2026
- Databricks Best Professional of the Year Latam · 2026
What sets us apart
Agile methodologies
Frameworks that enable fast implementations tailored to business needs.
Efficient, secure processing
Data processed with performance, protection and control at every stage.
Specialized team
A multidisciplinary team of certified professionals experienced across hundreds of platforms and tools.
Tailored approach
Solutions aligned with each client's strategic goals.
Data engineering FAQ
Didn’t find your question? Talk to an expert.
What is data engineering?
It is the discipline that builds the infrastructure to reliably collect, integrate, process and serve data from multiple sources. Without it, analytics, reporting and AI initiatives rely on incomplete, outdated or inconsistent data.
What is a data pipeline?
It is an automated flow that moves data from source to destination: it collects information from systems, transforms and organizes it, and delivers it ready for use in dashboards, reports or AI models — with no manual steps.
What is a data lakehouse?
It is an architecture that combines the flexibility of a data lake, which stores large volumes of data in multiple formats, with the structure and governance of a data warehouse. We explain it in detail in our article on data lakehouses as a hybrid solution.
Can you modernize a data platform without stopping operations?
Yes. With a controlled-coexistence migration, the legacy environment and the new platform run side by side until cutover. That is how we delivered the case study for one of Brazil's largest payments companies: more than 2,700 processes migrated with zero days of downtime.
How does data engineering relate to artificial intelligence?
AI models depend on reliable, up-to-date, well-organized data — and data engineering is what provides that foundation. Explore our AI, machine learning and advanced analytics solutions.
Data that reaches the right destination at the right time is the foundation for fast decisions, analytics and AI.
talk to an expertCase studies and articles on data engineering
Real-world stories and insights on pipelines, architecture and data modernization.
From bottleneck to strategic asset: how a payments giant scaled its data
Modernizing the data foundation with Databricks, with zero days of operational downtime.
Read the case study
Using data lakehouses as a hybrid solution
How this architecture combines flexibility and governance to turn data into strategic advantage.
Read the full article
Data Mesh: simplifying data and accelerating business
How to decentralize data management to gain scale and speed across business units.
Read the full article