CEA Brasil
BrazilRedesign of the data processing behind the operation, focused on information accuracy and processing cost.
Published result
AWS published a 61% reduction in accuracy errors and data processing costs 4 to 5 times lower.
Data Analytics
Your data has answers. We help you find them.
Modern data architecture in the lake house pattern: a data lake on Amazon S3 with unified governance and consumption by multiple profiles.
The context
The data problem in most companies is not a shortage of data. It is that the data lives in seven places, with three different definitions of revenue, and nobody trusts the number that comes out. Before talking about a dashboard or a predictive model, the work is to establish a reliable, governed source.
AWS calls this pattern modern data architecture, or lake house: a data lake on Amazon S3 at the center, with unified governance and specialized analytics services around it. The advantage over the traditional data warehouse is decoupling storage from processing: you keep everything in open formats and pick the right tool for each question, instead of forcing every question through a single engine.
Governance is what separates a data lake from a data swamp. AWS Lake Formation centralizes access control, permissions and the audit trail over the lake. Without that layer, the repository grows until nobody knows what is inside, where it came from or who can access it, and the data project loses internal credibility.
When this solution applies
If two or more of these scenarios sound familiar, it is worth a conversation.
- Two departments present different numbers for the same indicator
- Important reports still come from spreadsheets consolidated by hand
- Analytical queries compete with the transactional database and degrade the application
- There is relevant historical data nobody can query today
- There is no traceability of data origin or transformation
- AI initiatives stalled because there is no reliable data foundation to feed them
Benefits
- AWS Data Lake
- Real-time insights
- Integrated BI
- Data governance
Request a proposal
Initial assessment at no cost
Technical stack
AWS services we use
The exact composition depends on your environment and on the requirements identified during the assessment. This is the baseline we work from for this solution.
Amazon S3
Data lake storage layer in raw, curated and enriched zones, with encryption and lifecycle policies
AWS Lake Formation
Unified governance: fine-grained access control, permissions and audit trail over the lake
AWS Glue
Data catalog and serverless ETL for ingestion and transformation
Amazon Athena
SQL queries directly over the data lake, with no infrastructure to provision
Amazon Redshift
Data warehouse for analytical workloads at petabyte scale
Amazon EMR
Distributed processing with Spark for heavier volumes and transformations
Amazon Kinesis and Amazon MSK
Streaming data ingestion for cases that require low latency
Amazon QuickSight
Business intelligence and dashboards for the end consumer of the data
Amazon DataZone
Business catalog and governed sharing across domains
Apache Iceberg tables on Amazon S3
Open table format with schema evolution and time travel
Amazon SageMaker
Machine learning consuming the governed data lake directly
Methodology
How we build it
Business questions first
We start from the decisions the company needs to make and work backwards to the data required. A data lake built without a defined question becomes a repository with no consumer.
Source and quality inventory
We map source systems, volumes, update frequency and known quality issues. Bad data replicated at scale is still bad data, it only costs more.
Governance model
We define lake zones, the cataloging standard, access policy by domain and who owns the definition of each indicator.
Ingestion pipelines
We implement batch or streaming ingestion according to the real latency requirement, with error handling, reprocessing and monitoring.
Consumption layer
We enable the different profiles: analysts with SQL in Athena, business teams with dashboards in QuickSight, data science in SageMaker.
Cost governance and evolution
We apply lifecycle policies, partitioning and columnar formats, and track cost per query and per data domain.
Deliverables
What you receive
- Documented data architecture with defined zones and flows
- Data lake on Amazon S3 provisioned with encryption and lifecycle rules
- Data catalog in AWS Glue with governance through Lake Formation
- Ingestion pipelines in production with monitoring and reprocessing
- Data dictionary with agreed definitions of the indicators
- QuickSight dashboards for the priority questions
- Access policy by domain and user profile
- Analytics cost dashboard by query and by area
Indicators
What we measure
Time to insight
Interval between the business question and the reliable answer being available
Cost per query
Effect of partitioning and columnar formats on processing cost
Data freshness
Lag between the event at the source and its availability for analysis
Catalog coverage
Share of datasets cataloged, with owner and lineage recorded
References
Case studies published by AWS
Market references documented by AWS itself in equivalent scenarios. Each card links to the original story published on the AWS site.
Read on the AWS site
Coca-Cola Andina
Latin AmericaBuild of an analytics platform on AWS to support operational decisions and improve customer service.
Published result
AWS reports gains in operational efficiency and customer satisfaction.
INVISTA
GlobalData that was isolated in plants around the world was consolidated into a data lake on AWS, part of it built with AWS Lake Formation, in a transition from business intelligence to artificial intelligence.
Published result
AWS describes the elimination of data silos across the global units.
Itaú Unibanco
BrazilUse of Amazon SageMaker Studio to standardize and speed up the machine learning model development cycle over the data of the bank.
Published result
According to AWS, model development time fell from 6 months to 5 days, with productivity gains from standardization and reduced costs.
The cases above are customer stories published by Amazon Web Services. The results cited belong to the companies mentioned and serve as a reference for what is technically achievable on AWS. They do not represent results delivered by Núvoli, nor a guarantee of equivalent performance, since every environment has its own context.
Technical basis
Official AWS documentation behind this solution
Our approach is not opinion. It follows guidance documented by AWS, and you can check the source yourself.
Want to assess data analytics in your environment?
The first step is an assessment of your current setup, at no cost and with no commitment. You come out of it with risks mapped and priorities defined, even if you decide not to move forward with Núvoli.