The lakehouse, built for plants, planners, and SAP
Bristlecone builds Databricks lakehouses for manufacturers and consumer brands. We bring SAP, MES, and planning data onto Delta Lake, govern it with Unity Catalog, and feed it back into the systems where decisions get made.
Very little of it reaches the people who plan, schedule, and run the plants in a form they can use. That's the gap we close with Databricks.
Plants generate the data. Most lakehouses never reach the plant.
Manufacturers sit on MES, PLC, and SAP data that rarely lines up. Plants run different systems with different data models, so enterprise views of scrap, downtime, or OEE stay out of reach.
On its own, it doesn't solve meaning. SAP fields need business context before Genie or an AI agent can use them, and planning platforms need clean, timely data back.
We build the Databricks foundation around those jobs: harmonize plant and ERP data, govern it once in Unity Catalog, and send it to the planners and applications that act on it.
Already running Databricks? Get more from it
Many organizations already have a lakehouse and a backlog of promising work that never reached production. We help strengthen governance and data quality, add business context, consolidate separate warehouses, bring compute costs down, and build the operations needed to move existing Databricks work into managed, measurable use.
Standing you can check
- Enterprise Databricks delivery experience across supply chain data and analytics.
- Active in the Databricks Data + AI community.
- Leader in the IDC MarketScape: Worldwide Supply Chain, SAP & All Other Ecosystem Services, 2025-2026.
- Supply chain focus since 1998, with 1,000+ customer engagements.
- Part of the Mahindra Group.
What we do on Databricks
Every service works on its own or as part of one program.
Assess and plan
We deliver lakehouse strategy and assessment with a target TCO and FinOps model, an enterprise data assessment with data quality scoring, and an AI readiness assessment.
Migrate to the lakehouse
We've consolidated separate processing and warehouse workloads onto Databricks SQL and Photon, migrated Cloudera workloads including catalog and data science, moved on-premises warehouses to Delta Lake, and repointed downstream BI tools like Looker and Power BI.
Engineer pipelines
Batch and streaming pipelines on a medallion design, with bronze, silver, and gold layers on Delta Lake, Delta Live Tables and Databricks Workflows, and Kafka and Spark streaming from plant systems. We also integrate the lakehouse with Kinaxis Maestro.
Govern with Unity Catalog
One metastore across development, test, and production, with lineage, discovery, PII detection, and lakehouse monitoring. We extend the same governance to control what AI agents can read.
Analytics and BI
Governed answers for planners and executives, through Databricks SQL warehouses and Power BI semantic models built on Databricks gold tables.
Run and optimize
Operate the platform and keep compute costs in line, with embedded data engineering teams on multi-year terms, support and maintenance, and data FinOps. Photon and autoscaling help cut processing costs.
SAP, MES, and shop-floor data on one lakehouse
The pattern repeats: many plants, homegrown MES, SAP as the system of record, and a business that wants one view of scrap, downtime, and throughput.
- Plant data at scale. Kafka and Spark streams from machines and MES land in Delta Lake alongside SAP transactions.
- One governance layer. Unity Catalog holds a single metastore, lineage, and access policies across environments.
- A master data registry. Plants, machines, molds, and materials are mapped once, so KPIs compare across sites.
- Back to planning. Clean data flows to planning platforms such as Kinaxis Maestro through a medallion design.
In delivery, that looks like raw landing in Delta Lake with schema capture, harmonized plant, material, and transaction data with quality checks, and gold-layer OEE, scrap, downtime, and planning-ready data products served through Databricks SQL, Power BI, planning platforms, and applications.
Accelerators built from delivery
- Edge Data Readiness Accelerator: metadata-driven, cloud-agnostic ingestion and transformation.
- Kinaxis Maestro integration accelerator: Delta Live Tables, PySpark, and modular mappings that feed Kinaxis from the lakehouse, reducing integration time by 40–60%.
- Assessment frameworks: enterprise data assessment, data quality, migration, and lake design frameworks.
What a governed lakehouse lets you do next
- Plan: planning-ready data feeds to Kinaxis and other platforms, plus demand and inventory analytics.
- Source: spend and supplier analytics on SAP data.
- Make: OEE, scrap, and downtime analytics across plants, product genealogy, and predictive maintenance.
- Deliver: shipment and logistics cost analytics.
- Service: warranty and aftermarket analytics.
Secure by design. Predictable to run.
We run one Unity Catalog metastore across environments, with lineage, discovery, PII detection, and monitoring, and extend it to control what AI agents can read.
Photon, autoscaling, and consolidation of separate warehouses cut compute costs, and data FinOps is part of the run service.
From governed data to AI
We take customers from a governed lakehouse to AI in four steps:
- Ground: land SAP and plant data in Delta Lake, with lineage in Unity Catalog.
- Govern: give SAP fields business meaning, so Genie and agents use verified definitions.
- Activate: build models and agents with MLflow and Mosaic AI, released only after evaluation.
- Decide: put answers in front of planners through Genie, BI, and planning systems.
What leaders ask about Databricks
Practical answers to the questions that often shape the first conversation.
Does Bristlecone deliver Databricks programs?+
Yes. We've delivered Databricks lakehouse programs under signed, multi-year agreements since 2023.
Can you consolidate separate processing and warehouse platforms onto Databricks?+
Yes. We've consolidated separate processing clusters, standalone warehouses, and Cloudera workloads onto Databricks.
Do you only work on Databricks?+
No. We also deliver on Snowflake, AWS, Microsoft Azure, and Google Cloud.
How do we start?+
With a four-week assessment. You keep every artifact.