SAP HANA is the in-memory database platform that powers SAP S/4HANA, SAP BW/4HANA, and custom enterprise applications across finance, manufacturing, logistics, and supply chain. It's fast for SAP-native analytics — real-time financial reporting, material requirements planning, and production analytics run directly on HANA's in-memory columnar engine. But SAP HANA exists in a walled garden.

Connecting HANA data to non-SAP tools requires SAP Data Intelligence, SAP Business Technology Platform (BTP), or custom ABAP extractors — all of which add significant cost and complexity. Sharing HANA data with teams that don't use SAP tools (marketing running Tableau, data science using Python, operations using Power BI) means building export pipelines that duplicate data, add latency, and create governance gaps.

Dremio Cloud connects directly to SAP HANA and queries it alongside your other data sources with standard SQL. No SAP-specific middleware. No data extraction. Your HANA data stays in place and joins with S3, PostgreSQL, BigQuery, Snowflake, or any other connected source in a single SQL query.

Why SAP HANA Users Need Dremio

Break Out of the SAP Ecosystem

SAP Analytics Cloud and SAP BusinessObjects work well with HANA, but connecting HANA data to Tableau, Power BI, Looker, or Python-based analytics requires additional middleware, gateway servers, or data export. Dremio provides a vendor-neutral SQL layer that connects HANA to any BI tool via Arrow Flight (high-performance columnar data transfer) or standard ODBC/JDBC.

Cross-Platform Analytics

Your SAP data covers finance (GL accounts, AP/AR, cost centers) and supply chain (material masters, purchase orders, production orders). But your CRM data is in Salesforce (exported to S3), your support ticket data is in PostgreSQL, and your marketing attribution data is in Google BigQuery. Without a federation layer, combining these with SAP data requires building custom pipelines for each source. Dremio federates across all sources in a single query.

Reduce HANA Memory Pressure

SAP HANA licenses are tied to memory allocation — the more memory provisioned, the higher the license cost. Running analytical workloads in HANA consumes memory resources that compete with transactional OLTP operations. Dremio's Reflections offload repeated analytical queries from HANA's engine, reducing memory pressure and potentially allowing you to right-size your HANA memory allocation.

AI Analytics on SAP Data

SAP's AI capabilities (SAP Joule, embedded analytics) are tightly coupled to SAP applications. Dremio's AI Agent, MCP Server, and AI SQL Functions provide AI analytics that span SAP and non-SAP data sources — enabling cross-functional insights that SAP's tools can't deliver alone.

Prerequisites

• SAP HANA hostname or IP address — the HANA server
• Port number — typically 30015 for single-tenant, or 3XX15 for multi-tenant (XX = instance number)
• Database name (required for multi-tenant HANA systems)
• Username and password — HANA user with SELECT privileges on the schemas and tables you want to query
• Network access — HANA port must be reachable from Dremio Cloud
• Dremio Cloud account — sign up free for 30 days with $400 in compute credits

Step-by-Step: Connect SAP HANA to Dremio Cloud

1. Add the Source

Click "+" in the Dremio console and select SAP HANA.

2. Configure Connection Details

• Name: Descriptive identifier (e.g., sap-hana or erp-analytics).
• Host: HANA server hostname or IP.
• Port: Default 30015 for single-tenant.
• Database: Required for multi-tenant HANA systems.

3. Set Authentication

Master Credentials (username/password) or Secret Resource URL (AWS Secrets Manager).

4. Configure Advanced Options

5. Set Reflection and Metadata Refresh, then Save

Query SAP HANA Data

-- Query material inventory data
SELECT material_id, material_desc, plant, stock_quantity, unit_of_measure
FROM "sap-hana".SAPABAP1.MARD
WHERE plant = '1000' AND stock_quantity > 100
ORDER BY stock_quantity DESC;

-- Financial reporting: GL Account balances
SELECT
  gl_account,
  company_code,
  fiscal_year,
  SUM(debit_amount) AS total_debits,
  SUM(credit_amount) AS total_credits,
  SUM(debit_amount) - SUM(credit_amount) AS net_balance
FROM "sap-hana".SAPABAP1.BSEG
WHERE fiscal_year = '2024'
GROUP BY gl_account, company_code, fiscal_year
ORDER BY net_balance DESC;

Federate SAP with Non-SAP Sources

-- Join SAP material data with external supplier and demand data
SELECT
  m.material_desc,
  m.stock_quantity,
  m.plant,
  s.supplier_name,
  s.lead_time_days,
  s.unit_cost,
  d.forecasted_demand_30d,
  CASE
    WHEN m.stock_quantity < d.forecasted_demand_30d * 0.5 THEN 'Critical - Reorder Now'
    WHEN m.stock_quantity < d.forecasted_demand_30d THEN 'Watch - Order Soon'
    ELSE 'Adequate'
  END AS inventory_status
FROM "sap-hana".SAPABAP1.MARD m
JOIN "postgres-procurement".public.suppliers s ON m.material_id = s.material_id
LEFT JOIN "s3-forecasting".demand.material_forecasts d ON m.material_id = d.material_id AND m.plant = d.plant
WHERE s.lead_time_days < 14
ORDER BY s.unit_cost ASC;

SAP handles material masters, PostgreSQL has supplier details, S3 has demand forecasts — Dremio joins them all.

Build a Semantic Layer

CREATE VIEW analytics.gold.inventory_health AS
SELECT
  m.material_id,
  m.material_desc,
  m.plant,
  m.stock_quantity,
  CASE
    WHEN m.stock_quantity = 0 THEN 'Out of Stock'
    WHEN m.stock_quantity < 50 THEN 'Low Stock'
    WHEN m.stock_quantity < 200 THEN 'Adequate'
    ELSE 'Overstocked'
  END AS stock_status,
  ROUND(m.stock_quantity * s.unit_cost, 2) AS inventory_value_usd
FROM "sap-hana".SAPABAP1.MARD m
LEFT JOIN "postgres-procurement".public.suppliers s ON m.material_id = s.material_id;

In the Catalog, click Edit → Generate Wiki and Generate Tags. Create descriptions like "inventory_health: One row per material-plant combination showing current stock levels, status classification, and estimated inventory value in USD."

AI-Powered Analytics on SAP Data

Dremio AI Agent

The AI Agent lets users ask questions about SAP data in plain English: "Which materials are low stock at plant 1000?" or "What's the total inventory value across all plants?" The Agent reads your wiki descriptions, understands SAP terminology through the semantic layer, and generates accurate SQL.

This is transformative for SAP environments where only specialists know the table structures (MARD, BSEG, VBRK) and field names. The semantic layer translates SAP's technical schema into business language.

Dremio MCP Server

The Dremio MCP Server connects Claude and ChatGPT to your SAP data:

1. Create a Native OAuth app in Dremio Cloud
2. Configure redirect URLs (e.g., https://claude.ai/api/mcp/auth_callback)
3. Connect using mcp.dremio.cloud/mcp/{project_id}

A supply chain manager asks Claude "Show me all critical reorder items combining SAP inventory with supplier lead times" and gets actionable results without knowing SAP table names.

AI SQL Functions

-- Classify inventory risk with AI
SELECT
  material_desc,
  stock_quantity,
  stock_status,
  AI_CLASSIFY(
    'Based on inventory levels and value, recommend a procurement action',
    'Material: ' || material_desc || ', Stock: ' || CAST(stock_quantity AS VARCHAR) || ', Status: ' || stock_status || ', Value: $' || CAST(inventory_value_usd AS VARCHAR),
    ARRAY['Rush Order', 'Standard Reorder', 'Monitor', 'Liquidate Excess']
  ) AS procurement_action
FROM analytics.gold.inventory_health
WHERE stock_status IN ('Out of Stock', 'Low Stock', 'Overstocked');

-- Generate supplier evaluation summaries
SELECT
  s.supplier_name,
  AI_GENERATE(
    'Write a one-sentence supplier performance summary',
    'Supplier: ' || s.supplier_name || ', Lead Time: ' || CAST(s.lead_time_days AS VARCHAR) || ' days, Unit Cost: $' || CAST(s.unit_cost AS VARCHAR) || ', Materials Supplied: ' || CAST(COUNT(m.material_id) AS VARCHAR)
  ) AS performance_summary
FROM "postgres-procurement".public.suppliers s
JOIN "sap-hana".SAPABAP1.MARD m ON s.material_id = m.material_id
GROUP BY s.supplier_name, s.lead_time_days, s.unit_cost;

Reflections for SAP Analytics

SAP HANA is expensive to query for analytical workloads. Create Reflections on your semantic layer views to cache results:

1. Navigate to the view in the Catalog
2. Click the Reflections tab
3. Choose Raw Reflection (full cache) or Aggregation Reflection (pre-computed metrics)
4. Select columns and aggregations
5. Set the Refresh Interval — for SAP data that changes throughout the day, hourly is typical; for period-end data, daily or weekly
6. Click Save

Dashboard queries from Tableau or Power BI hit the Reflection instead of HANA, reducing memory consumption and license pressure. A financial reporting dashboard that queries HANA 96 times per day (15-minute refresh) with a Reflection refreshing every 2 hours consumes HANA resources only 12 times per day — an 87.5% reduction.

Governance on SAP Data

Dremio's Fine-Grained Access Control (FGAC) adds governance that SAP's built-in security doesn't extend to non-SAP tools:

• Column masking: Mask salary data, cost center details, or GL account balances from specific roles. A supply chain analyst sees material inventory but not financial data.
• Row-level filtering: Restrict data by company code, plant, or region based on the querying user's role. A plant manager sees only their plant's data.
• Unified policies: Same governance applies across SAP HANA, PostgreSQL, S3, BigQuery, and all other sources.

These policies apply across SQL Runner, BI tools (Arrow Flight/ODBC), AI Agent, and MCP Server — ensuring consistent access control regardless of how data is queried.

Connect BI Tools via Arrow Flight

Dremio's Arrow Flight connector provides 10-100x faster data transfer than JDBC/ODBC. For SAP data, this eliminates the need for SAP BusinessObjects or SAP Analytics Cloud:

• Tableau: Dremio connector — replaces SAP-specific Tableau drivers
• Power BI: Dremio ODBC or native connector — no SAP Gateway needed
• Python/Pandas: pyarrow.flight for data science on SAP data
• Looker: Connect via JDBC
• dbt: dbt-dremio for SQL-based transformations on SAP data

All queries benefit from Reflections, governance, and the semantic layer.

VS Code Copilot Integration

Dremio's VS Code extension with Copilot integration lets developers query SAP data from their IDE. Ask Copilot "Show me low stock materials at plant 1000 from SAP" and get SQL generated using your semantic layer — without knowing SAP table names like MARD or BSEG.

When to Keep Data in HANA vs. Migrate

Keep in HANA: Transactional data actively used by SAP applications (OLTP), data with SAP-specific processing (ABAP reports, CDS views, BW extractors), master data referenced by SAP transactions, data subject to SAP transport management.

Migrate to Iceberg: Historical analytical data (closed fiscal periods, prior-year orders), datasets consumed by non-SAP tools, data where HANA memory cost exceeds analytical value, data needed for AI/ML workloads outside of SAP, archived transaction data that rarely changes.

For data staying in HANA, create manual Reflections to offload analytical queries. For migrated Iceberg data, Dremio provides automatic compaction, time travel, Autonomous Reflections, and zero per-query license costs.

SAP Landscape Integration

SAP HANA rarely exists in isolation. Dremio helps connect the SAP landscape with non-SAP analytics:

SAP S/4HANA Integration

S/4HANA stores business-critical data in HANA tables. Dremio connects to the underlying HANA database and reads these tables directly, bypassing the need for SAP BTP, SAP Analytics Cloud, or custom OData/RFC extractors. This gives analysts SQL access to S/4HANA data — sales orders, material documents, financial postings — alongside non-SAP sources.

SAP BW/4HANA Bridge

SAP BW/4HANA creates InfoProviders and ADSO tables in HANA. Dremio can query these underlying HANA tables, exposing BW-managed data to non-SAP BI tools. This is valuable for organizations consolidating from SAP Analytics Cloud and BW to a unified BI strategy.

Common SAP + Non-SAP Analytics Patterns

Dremio's federation engine joins SAP tables with non-SAP sources without extracting SAP data to external systems — maintaining SAP as the system of record.

SAP HANA Licensing Considerations

SAP HANA licensing is based on memory allocation (RAM). Every analytical query consumes HANA memory resources. Dremio's Reflections offload analytical workloads from HANA, potentially allowing organizations to reduce HANA memory allocations and associated licensing costs.

Get Started

SAP HANA users can extend their SAP analytics beyond the SAP ecosystem — connect HANA, join it with every other source, and enable AI-driven analytics without SAP-specific middleware or additional SAP licenses. Start with Reflections to offload analytical queries from HANA's in-memory engine, then build a semantic layer for AI Agent access.

Try Dremio Cloud free for 30 days and connect your SAP HANA databases.