Snowflake, built around how your supply chain runs
Bristlecone is a Snowflake Select services partner and a pure-play supply chain firm. We bring SAP and other enterprise data onto Snowflake, govern it, and put it to work on planning, sourcing, manufacturing, and logistics decisions.
They choose it because planners, buyers, and finance teams need numbers they can trust, fast. Our job is to make sure the platform delivers that: the right data, with the right business meaning, governed and affordable from the start.
The platform is rarely the problem
When Snowflake programs stall, it's usually for reasons the platform can't fix on its own. Supply chain data arrives without business context. Governance trails adoption. AI pilots never connect to a decision anyone actually owns.
Pool and cluster tables, business rules buried in custom ABAP, and reports that only make sense when reconciled to transaction codes all have to survive the move.
Which inventory, supplier, or logistics call should get better, and what data does it need? Then we build the Snowflake foundation to serve it, with governance and cost controls in place before usage scales.
Already running Snowflake? Get more from it
If Snowflake is already live, the opportunity is usually in what sits on top of it. We help organize existing data into trusted, reusable data products with supply chain meaning, tighten governance, bring consumption under control, and put the platform to work on the analytics and AI use cases that justified it in the first place.
Standing you can check
- Snowflake Select services partner, with a Data Engineering workload specialization.
- Solution areas in Data Modernization and Enterprise Data Warehouse and Lakehouse.
- 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 Snowflake
Every service works on its own or as part of one program, from the first assessment through steady-state operations.
Assess and plan
We assess the data estate across systems, process, data, people, and organization, score data quality, and build a Snowflake strategy with a consumption model tied to real use cases. For migrations, we assess the complexity of legacy SQL and reports up front, including SnowConvert-based analysis.
Bring SAP and enterprise data onto Snowflake
Move data and pipelines without losing the logic that makes the numbers right. That includes data from SAP ECC, S/4HANA, and BW, as well as databases such as SQL Server, Postgres, and MongoDB, and on-premises data warehouses. We use change data capture with tools like HVR and SAP ODP, and we reconcile record counts and business transactions before cutover.
Engineer data pipelines
Batch and near-real-time pipelines that are inexpensive to run and easy to change, using Snowpipe and Snowpipe Streaming, Streams and Tasks, and layered raw, curated, and consumption models in dbt, orchestrated with Airflow. We handle semi-structured data and schema changes, and use Iceberg tables where an open table format matters.
Govern, secure, and assure quality
Controls go in before data sharing and AI scale: role-based access, masking and row-access policies, object tagging, and an audit trail through Access History and query logs. We integrate catalog and lineage tools such as Atlan, Collibra, or Alation, and set up Secure Data Sharing across business units and trading partners.
Modernize analytics and BI
We build a governed semantic layer in Snowflake that serves Tableau, Power BI, Sigma, and Streamlit, rationalize and migrate BI estates, and bring prebuilt KPI frameworks for spend, inventory, cost to serve, S&OP, warehouse, and logistics.
Run and optimize
Keep the platform healthy and the bill predictable after go-live. Managed DataOps covers pipeline, platform, metadata, access, and retention operations, with 24x7 support where SLAs require it. Cost control is built in, and we transfer capability to your team through shadowing, runbooks, and a planned glide path.
Where SAP meets Snowflake, we've done the hard parts before
Four problems decide whether these programs succeed:
- Proprietary SAP structures. Pool and cluster tables such as BSEG, and non-Unicode conversions, need special extraction handling.
- Logic hidden in ABAP. Business rules in custom code are rebuilt in dbt SQL or Snowpark so nothing gets lost.
- Trust in the numbers. We reconcile against business transactions, not just table counts, and rebuild views like P&L and balance sheet to match.
- Reporting speed. Users move from fixed reporting cycles to on-demand, self-service analytics.
DSOs and aDSOs become staging layers built as dbt base models. InfoCubes become dimensionally modeled fact tables. MultiProviders become views or dbt incremental models. Process chains become Airflow or Dagster DAGs, or Snowflake Tasks and Streams. ABAP transformations become modular SQL in dbt, with Snowpark for complex logic. And BEx queries and hierarchies move into a governed semantic layer.
We've delivered SAP to Snowflake programs for a global life sciences instrumentation company, a global consumer products and pet nutrition company, and a global consumer products manufacturer.
Accelerators built from delivery
We reuse what's worked before, so your program starts further along:
- Metadata-driven ingestion and load framework that ingests from databases, flat files, and cloud storage, and automates slowly changing dimension loads.
- Semi-structured data flattening that converts nested data into Snowflake tables, handles schema changes, and validates each load.
- Audit, balance, and control framework that reconciles record counts from source to target and captures pipeline run times and errors.
- SAP to Snowflake migration toolkit with automated ELT pipelines and templates for on-premises and SAP datasets.
- Prebuilt KPI frameworks for spend, inventory, cost to serve, S&OP, warehouse, and logistics.
What a governed Snowflake foundation lets you do next
Once the foundation is in place, supply chain use cases follow quickly:
- Plan: demand sensing and forecasting, S&OP scenario analysis, and inventory rebalancing across plants.
- Source: spend analytics, non-compliant PO detection, and tier-N supplier risk visibility.
- Make: OEE and quality analytics from MES and shop-floor data, and predictive maintenance.
- Deliver: logistics cost leakage detection, OTIF tracking, and retailer and supplier POS data unification.
- Return and service: aftermarket and service parts analytics, and complaint trending.
Our supply chain common data model maps SAP and non-SAP fields to one set of business definitions, so KPIs and AI agents use verified columns instead of guessing.
Secure by design. Predictable to run.
We set access, masking, and row-level policies by data domain, tag sensitive objects, and keep an audit trail through Access History and query logs. Designs follow HIPAA and GDPR principles where they apply.
Cost
Snowflake bills by consumption, so cost control starts with design. We right-size warehouses, use auto-suspend, choose transient tables only where recovery needs allow it, and tune the heaviest queries. Tying spend to named workloads and use cases makes consumption easier to explain and govern.
From governed data to AI
We build AI on Snowflake in four steps, so every model or agent rests on data you trust:
- Ground: land SAP and non-SAP data with change data capture into governed Snowflake tables, including Iceberg where it fits.
- Govern: encode supply chain definitions in a semantic layer, so every KPI and agent uses verified fields.
- Activate: build predictive models with Snowpark and GenAI features with Snowflake Cortex, released only after evaluation.
- Decide: put answers where planners and buyers work, through Streamlit apps, BI, and natural-language questions.
What leaders ask about Snowflake
Practical answers to the questions that often shape the first conversation.
Is Bristlecone a Snowflake partner?+
Yes. Bristlecone is a Select tier services partner in the Snowflake Partner Network, with a Data Engineering workload specialization.
Can you migrate SAP BW, not just ECC and S/4HANA?+
Yes. We map DSOs, InfoCubes, MultiProviders, and process chains to dbt models, Snowflake Tasks, and a governed semantic layer.
How do you keep Snowflake costs under control?+
Warehouse sizing, auto-suspend, transient tables, and query tuning are part of the design, and consumption is tied to named use cases so spend has a clear purpose.
Do you only work on Snowflake?+
No. We also deliver on Databricks, AWS, Microsoft Azure, and Google Cloud, and we recommend a platform based on your landscape and workloads.
Do you support the platform after go-live?+
Yes, through managed DataOps, including 24x7 support where SLAs require it.
How do we start?+
Most customers start with a four-week assessment and keep every artifact whether or not they continue.