Data engineering consulting: the foundation for BI today and AI tomorrow
We design, build and support data architectures for mid-sized and large companies on Microsoft Fabric, Databricks, Snowflake and Azure, as well as on-premises environments with Apache Airflow and Kafka. The platform is chosen to fit each client's situation. From the ERP to data ready for analysis and AI, with your data always in your company's environment, for clients in Brazil and abroad.
Microsoft Partner · 100+ certificates of technical capability signed by clients
What a data engineering consultancy does
A data engineering consultancy pulls data out of the systems where it originates (ERP, CRM, spreadsheets, APIs), centralizes it in a single repository with validated business rules and quality checks, and keeps the loads running every day. The result is reliable data for Power BI, for business units and for artificial intelligence.
Active BI does this work from the assessment through ongoing support, on Microsoft Fabric, Azure, Databricks, Snowflake or on-premises. Your data always stays in your company's environment, and we work remotely across Brazil.
When the problem is not the report, but the data foundation
The same problem looks different to those who make decisions and to those who maintain the environment. If any of these sound familiar, the answer lies in data engineering.
For the business
- "Every department has its own spreadsheet and no number matches the system."
- "The leadership report arrives late or with the wrong number."
- "Every new question means someone spends days cross-checking spreadsheets."
- "We want to use AI on our data, but we know the current foundation can't support it."
For IT and data teams
- "The load runs overnight and, when it fails, nobody knows until leadership opens the report."
- "Pulling data from the ERP has become a project: every new query depends on a busy specialist."
- "The environment is old, slow and hard to maintain, and nobody wants to touch it."
- "Cloud costs grow every month and nobody can explain what is driving them."
What you can engage us for in data engineering
Each workstream can be engaged on its own or combined into one project, under the same technical leadership. When it is not yet clear where to start, the Information Assessment sets the order.
| If you... | The service is | What we deliver |
|---|---|---|
| See the symptoms, but don't know how big the problem is or where to start | Information Assessment | Current maturity, mapped sources and a roadmap prioritized by business value, in 4 to 8 weeks. |
| Have data scattered across ERP, spreadsheets and systems that don't talk to each other | Data integration and centralization | A single repository (Data Warehouse or Lakehouse) with business rules applied and quality validated on entry. |
| Need to pull data from SAP, TOTVS, Salesforce or APIs | SAP, TOTVS and APIs | Connections and loads designed for each source, without overloading the source system. |
| Need a foundation that can grow without rebuilding everything | Data architecture | Design and implementation of a Data Warehouse, Data Lake or Lakehouse, with layers, security and cost under control. |
| Have loads that fail silently or run late | ETL/ELT pipelines | Orchestrated loads with monitoring, alerts and reprocessing. |
| Have numbers that don't match, or questions about LGPD (Brazil's data protection law) or who can access what | Data governance and quality | Data dictionary, an owner for each piece of information, quality rules, catalog and access control (Microsoft Purview, where it makes sense). |
| Have a legacy environment that is slow, expensive or hard to maintain | Modernization and migration | Phased migration of on-premises SQL Server, Power BI Premium per capacity and legacy tools to Fabric or Azure. |
| Want to use AI on company data | Data preparation for AI | Clear semantics, documentation, quality and permissions, including for documents such as contracts and PDFs. |
| See cloud or capacity costs rising with no explanation | Cost and capacity optimization | Consumption analysis, capacity and load tuning, with cost explained by department or process. |
| Already have the environment and need someone to run it | Data support and operations | Daily monitoring, fixes and continuous improvement with an SLA. |
| Need to strengthen your in-house team | Specialist staffing | Data engineers, Fabric specialists and AI specialists on your team, with technical oversight from Active BI. |
Want to see how the data reaches the business? See Power BI consulting, the layer leadership sees.
What you receive at the end of a project
A data engineering project ends with an environment your team understands and can maintain, not a black box.
Architecture design
Layers, flows, security and platform, documented and approved with your team.
Data dictionary and business rules
What each field means, where it comes from and how it is calculated.
Monitored pipelines
Orchestrated loads with failure and delay alerts and a reprocessing routine.
Consumption layer
Tables and models ready for Power BI, business units and AI.
Acceptance evidence
Objects, data volumes and processes formally validated by you, stage by stage.
Operations manual and knowledge transfer
Your team knows how to operate, evolve and fix the environment after delivery.
The data journey in five phases, from assessment to AI readiness
The phases show how a data platform matures over time. Not every company starts with the first one: the Information Assessment shows which phase the environment is in today. AI readiness is built into the design of every architecture we deliver, with access control and traceability.
Information Assessment
Study of the strategic dimensions and review of existing structures, processes and architectures, consolidating current maturity and the evolution plan.
Environment setup
Configuration of the Lakehouse or Data Warehouse, ingestion model, data catalog and integrations with the mapped sources.
KPIs and analytical consumption
Consolidated consumption layer, refined business rules and integration with Power BI, with visualizations that follow best practices.
Monitoring and automation
Alerts for critical indicators, process automation, an environment health plan and production management.
AI readiness
Structured, documented data with clear semantics, ready to be consumed by agents, Copilot and Active IA with traceability.
Data Warehouse, Data Lake or Lakehouse: when to use each
It is the first architecture decision and the one that raises the most questions. A practical summary of what we recommend in our projects:
| Criterion | Data Warehouse | Data Lake | Lakehouse |
|---|---|---|---|
| Data type | Structured, from ERP and other systems | Any format: files, logs, documents | Both, in the same place |
| Who consumes it | BI and management reports | Data scientists and engineering | BI, data science and AI |
| Strength | Consistency and performance for business queries | Low cost to store large volumes | One copy of the data serving every use |
| Watch out for | Less flexible for unstructured data | Turns into a "swamp" without governance | Requires layer discipline from the start |
| When we recommend it | Focus on BI, known sources, lean team | Historical archive and high-volume raw data | BI and AI on the same foundation, as in Microsoft Fabric |
ETL or ELT?
In ETL, data is transformed before it enters the repository. In ELT, it lands raw and is transformed inside the platform. In the cloud, ELT is usually the default choice: it preserves the original data, makes auditing easier and uses the platform's processing power. ETL is still useful when the source has volume constraints or the data must be filtered before it leaves, for security reasons.
Data engineering or BI: which comes first?
If numbers differ between reports, the problem is almost never the report. Starting with BI on a disorganized foundation only moves the place where the error shows up. The path is to organize the foundation first and then build Power BI on top of it. When time is short, both workstreams run in parallel, with the foundation prioritizing the indicators leadership uses most.
How to prepare company data for artificial intelligence
An AI answering questions about company data amplifies any ambiguity in the foundation. If two tables define "customer" differently, the error that used to stay hidden in a report shows up as a wrong answer, delivered with confidence. Preparing data for AI means treating semantics, quality and permissions as part of the project, not as a footnote in the documentation.
This is the link between data engineering and Active BI's Artificial Intelligence practice, including Active IA.
AI-ready data checklist
- Single definitions for business terms (customer, revenue, order).
- Documented data dictionary and calculation rules.
- Quality measured on entry, with an alert when it drops.
- Inherited permissions: the AI only sees what the user is allowed to see.
- Documents (contracts, PDFs, emails) organized and indexed for retrieval.
- Traceability: every answer shows where the data came from.
Data governance and quality in practice
Governance is not a committee or a document in a drawer. In our projects, it becomes rules enforced in the environment itself: who owns each piece of information, how it is calculated, who can access it and what happens when quality drops.
Owner and definition
Every key indicator and table has an owner and a single definition, recorded in the data dictionary.
Monitored quality
Completeness, duplication and consistency rules run on every load, alerting whoever needs to act.
Access and LGPD
Personal data identified, access by role and by business unit, and a catalog with Microsoft Purview when the scale calls for it.
Platforms: Microsoft Fabric, Databricks, Snowflake and others
Microsoft Fabric is our default recommendation when the company already operates in the Microsoft ecosystem. The right architecture, however, depends on what already exists in your environment. That is why we maintain active practice on the main market platforms, including fully on-premises scenarios.
Microsoft ecosystem
Microsoft Fabric, Azure Synapse, Azure Data Factory, SQL Server, Microsoft Purview and Power BI, covering ingestion, storage, governance and consumption in the same ecosystem.
Other platforms and on-premises
Databricks, Snowflake, BigQuery and, in on-premises or hybrid scenarios, Apache Airflow for orchestration and Kafka for data streaming.
How we run each project: formal acceptance at every stage
No phase moves forward without formal acceptance. You validate objects, data volumes and processes before any deliverable is considered complete, and you receive full documentation of infrastructure, processes and the data model at the end.
Technical discovery by source
Discovery by source: objects, source and target infrastructure, security, permissions and functional requirements.
Environment configuration
Networking, storage, catalogs, integrations and connectivity tests, cycle by cycle per environment through production.
Development and testing
Extraction and load pipelines built to best practices, with unit and integration tests and performance tuning.
Acceptance
Validation of objects, data volumes, processes and environment, with formal client sign-off before moving on.
Documentation
Details of infrastructure, sources, data volumes, test evidence and the final data model design.
In a typical mid-sized project, these stages run over three to four months, with infrastructure in the first month, ingestion and loads in the following months, and acceptance and documentation running in parallel at the end. The detailed schedule is defined when the scope is sized.
How much a data engineering project costs
Cost depends on the number of sources, data volume, refresh frequency and the condition the data arrives in. There is no list price that fits every environment. That is why Active BI sizes each proposal after a diagnostic conversation, with no commitment.
Fixed-scope assessment
4 to 8 weeks, ending with a prioritized roadmap and sizing of the next phases.
Phased project
Scope, deliverables and acceptance defined per phase. A typical mid-sized project takes 3 to 4 months.
Ongoing squad or support
Hour bank or monthly contract to evolve and operate the environment, with an SLA. See plans →
What drives the investment
- Number and type of sources: ERP, CRM, databases, spreadsheets, APIs.
- Data volume and refresh frequency (daily or near real time).
- Data quality at the source and the number of business rules.
- Security requirements, LGPD and segregation by business unit.
- The chosen platform and what already exists in the environment.
- Need for support after delivery.
What an Active BI proposal includes
- Proposed architecture and the rationale for the platform.
- Sources, objects and data volumes in scope.
- Phases, deliverables and acceptance criteria.
- Roles involved and estimated hours per phase.
- Assumptions, risks and what is out of scope.
The platform cost (Microsoft Fabric, Azure, Databricks or Snowflake) is contracted separately, directly by your company, and stays in your environment. We help size capacity so you don't pay for more than you use.
Data engineering projects we have delivered
Different environments, same method: integrate, centralize, monitor and document.
Data foundation integrated and modernized on Microsoft Fabric
A manufacturer of insulated bottles, cups and outdoor gear that needed to organize and integrate data from multiple sources to raise its analytics maturity.
- Sources integrated into a unified platform
- Source mapping and a dictionary of the integrated data
- Foundation ready for analytical consumption
ERP and spreadsheet data centralized in a single repository
A company with management information split between the ERP and each department's spreadsheets, without a reliable consolidated view.
- Single repository with business rules applied
- Automated loads instead of manual consolidation
- Consistent indicators across departments
Monitored pipelines and a reliable analytics layer
An environment with fragile manual loads that delayed reports and depended on a few people to keep running.
- Orchestrated pipelines with failure alerts
- Documented consumption layer for Power BI
- Operations that don't depend on a single person
Is your data scattered across multiple systems? Tell us which sources you have and what is blocking your analysis today. We will point out where to start.
What our clients say
Our partnership with Active BI is providing invaluable support to Lenovo throughout our digital transformation journey. Professionalism, technical proficiency and proactive communication.
The Active BI team is extremely punctual and responsible, committed to its deliverables and solutions. Both the technical and management teams are excellent.
Active BI has highly skilled, up-to-date professionals who deliver fast, effective solutions. We also highlight their clear communication, availability and ability to simplify complex topics. An excellent service that we recommend without hesitation.
How to choose a data engineering consultancy
The criteria we recommend evaluating in any provider, including Active BI:
Platform-neutral
Fabric, Databricks, Snowflake or on-premises: the recommendation has to come from your situation, not from a commercial partnership.
Formal acceptance
Each stage validated by you, with data volumes and evidence, before moving on.
Documentation delivered
Data dictionary, architecture and operations manual are part of the scope, not an extra.
Monitoring included
A pipeline without alerts is a problem postponed. Ask how you will find out about a failure.
Knows your ERP
SAP, TOTVS and others have their own extraction quirks. Ask for concrete examples.
Data in your environment
The data should stay in your company's tenant, with access you control.
Further reading
Data engineering FAQ
What does a data engineering consultancy do?
What is the difference between data engineering and BI?
Data Lake, Data Warehouse or Lakehouse: which should we choose?
ETL or ELT?
How do we centralize data from multiple systems and spreadsheets?
Do you integrate SAP, TOTVS and Salesforce?
How much does a data engineering project cost?
Does the data stay in my company's environment?
How do we prepare company data for AI?
What changes in a data engineering project when the goal includes AI?
Do we need to move everything to the cloud to have a good data architecture?
Do you work only with Microsoft Fabric?
How long does a data engineering project take?
How do you make sure data loads don't fail silently?
Can my company's team maintain the environment after delivery?
Does Active BI work remotely?
Let's design your company's data architecture
Tell us which systems need to be integrated and what is blocking your analysis today. From there, we recommend the best starting point, which may be an assessment or a direct implementation scope.
- You tell us your sources and the problem
- A specialist replies within 30 minutes, during business hours (Brasília time)
- A 30-minute conversation with a data architect
No commitment and no follow-up call sequence.
Talk to a data architect
2 steps, under 40 seconds
Step 1 of 2