Unique Data Platform — WFS BigData platform built on PySpark, Airflow and SQL. Available in two topologies: Hybrid (PaaS + SaaS) or Databricks (SaaS). Unified structure, high performance and WFSLib cutting dev time by 70%.
Six pillars that replace dozens of loose tools with a cohesive, governed, scale-ready platform.
Defines projects, storage areas and AI-powered Data Catalog. Everything organized, documented and discoverable.
Full history preservation with Delta Lake versioning. Rollback any data, anytime.
Scalability, volume and speed without limits. Spark, Synapse Serverless, Delta Lake — Azure Cloud, on-premises or Databricks.
Proprietary library with ready extraction patterns. Cuts 70% of development, standardizes quality.
Web environment for manual file uploads with automatic validation. Business areas send data without IT.
Native integration with WFS Portal Insights via Live Connection. Data always up-to-date, no schedulers.
Pick the model that fits your maturity and current ecosystem. WFSLib and governance are the same in both.
Combination of Azure-managed PaaS services with SaaS layers — Spark Cluster (PaaS), Data Lake Gen2, Synapse Serverless and Airflow. Lowest-cost topology, ideal for operational efficiency and flexibility.
UDP runs natively on Databricks Lakehouse — leverages Unity Catalog, Photon Engine and Databricks' unified Spark stack. Ideal for companies already on or standardizing on Databricks.
Data from TOTVS, VTEX, APIs, databases and Portal enter via Spark Streaming in real time and progress through governed layers to consumption. Customizable structure for other database standards and new data sources as your business needs evolve.
The UDP architecture can be customized for other database standards (Oracle, SQL Server, PostgreSQL, MySQL, MongoDB, DB2, etc.) and any input source — SaaS, ERPs, CRMs, REST/GraphQL APIs, files, messaging (Kafka, EventHub), IoT and more. WFSLib eases the extension without losing governance.
From the first data at the source to the final Data Science insight — we cover all 5 stages in one platform, with governance and WFSLib.
Operational and infrastructure
Query performance
Dev time with WFSLib
DEV→QA→PRD with rollback
Manufacturing
Unified quality view
Energy
Operational big data
Logistics
Real-time telemetry
Three progressive phases with break-even in 12 to 15 months.
2-3 months
3-4 months
2-3 months
All the team's Python and Airflow knowledge is leveraged — UDP uses the same market tools.
Spark and UDP architecture training by WFS, with real cases and practical mentoring.
PySpark is similar to Python — accelerated learning and productivity from the first weeks.
All data respects the same source of truth. No duplication, no divergence, no "which number is correct?".
Delta Lake allows releasing data to any area without failures. Permissions by row, column, project and user.
Total history of access and changes by area. Meets compliance, LGPD/GDPR and internal audit.
DEV → QA → PRD with automatic validation and rollback. No bad data reaches production.
Definition of a dedicated team and resources for the transformation.
UDP validation using your own data, in an isolated environment.
Total Cost of Ownership study with a customized migration plan.
30-minute conversation with a WFS architect. We present UDP in any mode (Hybrid PaaS+SaaS or Databricks SaaS), discuss your scenario and propose a POC.
Schedule POCWFS UDP (Unique Data Platform) BigData is WFS's proprietary platform for building data lakes and lakehouses at enterprise scale. It combines a medallion architecture (bronze/silver/gold) on Spark, Delta Lake and Synapse with WFSLib — a proprietary library that cuts pipeline development time by up to 70% and delivers 10× the performance of traditional implementations.
In production at companies such as Volvo, Shell, OBDI and Britânia, processing hundreds of TB per day. Cases include unifying manufacturing data, integrating Brazilian ERPs (TOTVS, SAP) with cloud analytics, and modernizing legacy DWs (Teradata, Netezza) to cloud-native architecture.
Full support for Microsoft Azure (Synapse, Fabric, Databricks, ADLS Gen2), AWS (EMR, Glue, Redshift, S3), Google Cloud (Dataproc, BigQuery, GCS) and on-premises environments (Cloudera, Databricks on-prem, Hadoop). Also supports hybrid and multi-cloud architectures for data sovereignty cases.
WFSLib is a proprietary library with ready-made components for common pipeline patterns: incremental ingestion, deduplication, data quality, optimized partitioning, slowly changing dimensions, change data capture, observability. Teams using WFSLib ship new pipelines in days instead of weeks, with consistent quality and technical standards.
Yes. Migration projects from Teradata, Netezza, Oracle DW to modern lakehouses (Databricks, Snowflake, Synapse) with stored-procedure refactoring, data-parity validation, phased cutover and training. Typical cases deliver the first domain in 8-12 weeks and full migration in 6-12 months.