Data Lake / Data Warehouse Modernization · Real-time Analytics
Gerdau needed a better way to support streaming industrial analytics and self-service data for steel production because the company needed faster, cheaper, and more scalable processing for manufacturing, sustainability, and business data spread across multiple teams.
Reported impact
Gerdau improved streaming industrial analytics and self-service data for steel production with measurable results including 40% shaping global infrastructure with ironclad manufacturing, 80% cost reduction for data processing, and 300 in new developments for streaming solutions, while giving teams a faster path from governed data to production decisions.
Gerdau uses Databricks Data + AI Platform to support streaming industrial analytics and self-service data for steel production. The implementation replaced a legacy state where the company needed faster, cheaper, and more scalable processing for manufacturing, sustainability, and business data spread across multiple teams. Mill operations data and streaming manufacturing data move through production data pipelines and land in Delta Lake, giving teams one trusted data foundation. Unity Catalog and Delta Sharing governs access, lineage, and compliance across sensitive datasets. Agent Bricks operationalizes models, agents, and analytics that deliver industrial analytics and streaming data products.
Mill operations data and streaming manufacturing data land on a shared platform, pipeline services clean and standardize them, governance controls enforce trusted access, and AI, model-serving, and analytics services deliver industrial analytics and streaming data products.
But they required users needing to be proficient in Python and Spark. That made it difficult for engineering to maintain and drive adoptions within the business.
Data analytics plays a critical role in what we can do in our mills. They help us figure out new ways to use machinery to achieve better results.
We wanted to build with open source because of its flexibility and unlimited potential,
With Unity Catalog, we have established data governance standards across our manufacturing processes,
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
Related
Databricks Sample Mar 31, 2026 Trackunit Construction is one of the least digitized industries in the world, with stagnant productivity despite heavy IT investment. Internally, Trackunit had to handle 6 million connected machines generating 3 billion data… 3 billion data points processed daily Agent Bricks / Mosaic AI · Databricks AI/BI · Databricks Data Intelligence Platform Databricks Sample Mar 26, 2026 HP Indigo HP Indigo's industrial print business was managing 3,500+ data volumes (~10TB) across hundreds of jobs spanning ERPs, manufacturing platforms, and field data sources, with critical data tracked in manual spreadsheets.… 3,500+ data volumes (~10 TB) under management Databricks AI/BI · Databricks AI/BI Genie · Delta Sharing Databricks Sample Jul 28, 2026 OpenAI OpenAI’s data platform powers one of the fastest-scaling products in tech history. ChatGPT, the OpenAI API, and the internal tools used by thousands of OpenAI employees require a foundation that can withstand sudden… $400K/month Storage costs eliminated by marketing team Databricks Data Warehouses · Databricks Jobs · Databricks SQL 