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AI ⇄ Database

Pinecone to SAP Hana integration — real-time, two-way sync

Keep Pinecone and SAP Hana in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.

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Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect Pinecone and SAP Hana

Sync the records in SAP Hana into Pinecone and land its embeddings, classifications, and generated fields back on the same rows, in real time and without a pipeline to maintain.

AI systems do not hold customers or invoices the way business apps do. What they hold is derived from your data: the vectors and metadata in a vector store, or the classifications, extracted fields, and generated text a model produces over records it was given. SAP Hana is where those source records actually live. The bridge between the two is the row itself, since an item in Pinecone and the record in SAP Hana it describes are two halves of the same thing, and they drift the moment one is updated without the other.

Stacksync syncs Calculation views, SQL views, Schemas, Sequences in SAP Hana with Namespaces, Collections, Backups, Index statistics in Pinecone in real time. Rows created or changed in SAP Hana flow into Pinecone so inference and embedding run on current data, and the scores, labels, and generated fields Pinecone produces flow back onto the matching rows in SAP Hana, mapped field by field. A change on either side appears on the other within seconds, with no extraction job or webhook plumbing to keep alive.

Because matching is by a stable identifier, every row in SAP Hana stays tied to its AI-side counterpart in Pinecone. Retrieval, enrichment, and generated content always resolve back to the record they came from, so there are no orphaned vectors and no labels describing a version of a row that no longer exists.

Common use cases

  • 01 Migrate or clone vectors between Pinecone indexes, projects, or namespaces - or from another vector store into Pinecone - using batched upserts.
  • 02 Back up a namespace by exporting its vector ids, values, and metadata to object storage or a database on a schedule using the list and fetch operations.
  • 03 Read SYS.TABLES and TABLE_COLUMNS system views to auto-discover schema and generate field mappings when new tables are added to the sync.
  • 04 Detect inserts, updates, and deletes on high-write tables via AFTER triggers or a last-modified column and replicate the deltas downstream near-real-time.

Common sync patterns

One record, one identifier

Each item in Pinecone carries the key of the row in SAP Hana it came from, so results resolve back to the exact record with nothing orphaned or duplicated.

Run the AI on current data

Rows created or changed in SAP Hana flow into Pinecone as they happen, so embeddings, classifications, and prompts run on the latest records instead of a nightly snapshot.

Write results back onto the record

Scores, labels, extracted fields, or generated text produced in Pinecone land on the matching row in SAP Hana, next to the source data your applications already query.

What you can sync between Pinecone and SAP Hana

Representative objects on each side — any object or custom field can map to any target. Schemas are auto-detected; types are converted between the two systems.

Pinecone objects SAP Hana objects How this pairing syncs
Backups Point-in-time snapshots of a serverless index; created, listed, and restored into a new index on the control plane for recovery or cloning. Read as a recovery-asset inventory. System views (SYS schema) Catalog and monitoring views such as SYS.TABLES and TABLE_COLUMNS; read to discover schema and auto-generate field mappings for new tables. Backups is specific to Pinecone and System views (SYS schema) to SAP Hana — each maps to any object or custom field on the other side.
Index statistics Describe_index_stats returns total and per-namespace vector counts, the index dimension, and index fullness; read to size a sync and to detect drift between Pinecone and the source of truth. Column store tables HANA's default table type, optimized for analytics; synced two-way as rows over the SQL interface with the primary key driving upserts and delete tracking. Index statistics is specific to Pinecone and Column store tables to SAP Hana — each maps to any object or custom field on the other side.
Indexes Serverless or pod-based containers holding vectors of a fixed dimension and distance metric (cosine, dotproduct, euclidean); managed on the control plane (api.pinecone.io) via create, list, describe, configure, and delete. describe_index returns the per-index data-plane host. Row store tables OLTP-oriented tables held fully in memory; read and written the same way through the SAP HANA client for high-write operational tables. Indexes is specific to Pinecone and Row store tables to SAP Hana — each maps to any object or custom field on the other side.
Vectors (records) The core data: an id (up to 512 chars), a dense values array, optional sparse_values, and JSON metadata (up to 40 KB filterable per record). Full CRUD on the data plane via upsert, update, fetch, query, and delete, so write is supported here. Calculation views Modeled analytic views over base tables; read-only sources for pushing aggregated or joined results into a warehouse or downstream app. Vectors (records) is specific to Pinecone and Calculation views to SAP Hana — each maps to any object or custom field on the other side.
Namespaces Partitions inside an index; every read and write targets one namespace and vectors across namespaces are isolated. Enumerated with list_namespaces and sized per namespace via describe_index_stats. SQL views Standard database views; read-only projections synced outbound when the source data should not be exposed as raw base tables. Namespaces is specific to Pinecone and SQL views to SAP Hana — each maps to any object or custom field on the other side.
Collections Immutable snapshots of a pod-based index that store its data but not its definition; created, listed, and deleted on the control plane and used to recreate a pod-based index. Serverless indexes use Backups instead. Schemas Namespaces that group tables and views; the connector targets a schema and enumerates its objects from the catalog to build the sync. Collections is specific to Pinecone and Schemas to SAP Hana — each maps to any object or custom field on the other side.

How changes propagate between Pinecone and SAP Hana

Each direction of the sync is driven by what the source system can signal and what the destination accepts — detection, delivery, and expected latency below.

Pinecone SAP Hana Interval-based propagation

DetectionStacksync polls Pinecone for changes on an incremental schedule, reading only records changed since the previous pass. No webhooks and no native change-data-capture feed.

DeliveryEach detected change is applied to SAP Hana as a row-level write, with types converted between the two schemas.

SAP Hana Pinecone Sub-second propagation

DetectionChanges in SAP Hana are captured at the source via change data capture — no polling loop against its API. Polling on a last-modified timestamp column, or AFTER INSERT/UPDATE/DELETE triggers writing to a shadow table.

DeliveryEach detected change is written to Pinecone through its API, with automatic retries and rate-limit backoff.

Rate-limit considerations

  • Pinecone: Data-plane operations are capped per second: query, upsert, update, and delete at 100 req/s per namespace, fetch at 100 req/s per index, and list at 200 req/s per index; breaches return HTTP 429. Hard request limits also apply: an upsert is max 2 MB or 1000 records, metadata max 40 KB per record, and query top_k up to 10,000 with a 4 MB result cap.
  • SAP Hana: No SaaS-style request quota; throughput is bounded by connection limits, statement memory caps, and workload-class admission control (concurrency/memory limits) that share capacity with production queries.
What ships with Pinecone ⇄ SAP Hana

Connect Pinecone and SAP Hana for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Pinecone–SAP Hana connection.

Real-time

Two-way sync

Changes in Pinecone or SAP Hana instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Pinecone or SAP Hana data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.

At scale

Event queues

Handle millions of events per minute without losing a single Pinecone or SAP Hana record.

Observability

Monitoring

Track your Pinecone ⇄ SAP Hana sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Pinecone and SAP Hana.

How the Pinecone and SAP Hana connectors work

Pinecone

Integration surface
Two HTTP APIs: a control plane at api.pinecone.io (manage indexes, collections, backups, and, via the Admin API, projects and API keys) and a per-index data plane at the host returned by describe_index (upsert, query, fetch, update, delete, list). A gRPC data-plane transport is available through the official SDKs.
Authentication
API key in the Api-Key request header, scoped to one project; every request also sends an X-Pinecone-Api-Version header (date-based, e.g. 2025-10). The organization Admin API instead uses OAuth2 client-credentials (service accounts) via login.pinecone.io/oauth/token, passing a Bearer token to api.pinecone.io/admin (Enterprise).
Change detection
No webhooks and no native change-data-capture feed. Vectors carry no server-side update timestamp, so Stacksync detects changes by re-reading - paginating vector ids with the list operation (serverless indexes) and fetching by id, or by re-upserting from the source of truth. describe_index_stats bounds a resync with per-namespace counts.
Capabilities
read · write
Rate limits
Data-plane operations are capped per second: query, upsert, update, and delete at 100 req/s per namespace, fetch at 100 req/s per index, and list at 200 req/s per index; breaches return HTTP 429. Hard request limits also apply: an upsert is max 2 MB or 1000 records, metadata max 40 KB per record, and query top_k up to 10,000 with a 4 MB result cap.

SAP Hana

Integration surface
SQL over the SAP HANA client (JDBC/ODBC drivers); OData/REST via XS for app-layer access
Authentication
Dedicated database user credentials over an encrypted TLS connection (password sent hashed); Kerberos, SAML, JWT, and X.509 certificate authentication are also supported. SAP HANA Cloud enforces TLS and IP allowlisting.
Change detection
Polling on a last-modified timestamp column, or AFTER INSERT/UPDATE/DELETE triggers writing to a shadow table; system-versioned temporal tables and SLT/SDI provide trigger- and log-based CDC. No native webhooks.
Capabilities
read · write · CDC
Rate limits
No SaaS-style request quota; throughput is bounded by connection limits, statement memory caps, and workload-class admission control (concurrency/memory limits) that share capacity with production queries.
How it works

How to connect Pinecone to SAP Hana — three steps, no code

Configure and sync within minutes, no code. Whether you sync 50k or 100M+ records, Stacksync handles the queues, infra, and plumbing. Integrations are non-invasive and need zero setup on your systems.

  1. 01

    Connect your apps

    Authenticate Pinecone and SAP Hana with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    Pinecone connected
    SAP Hana connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Pinecone and SAP Hana objects to sync — Stacksync auto-detects both schemas, including custom fields where the platform exposes them. Sync to existing tables, or let Stacksync create new ones with ideal data types.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · Pinecone ⇄ SAP Hana
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    Pinecone SAP Hana
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Pinecone and SAP Hana integration FAQ

SECURITY

Security teams trust Stacksync

As a data company, we understand the importance of keeping your data secure. Stacksync is built with security best practices to keep your data safe at every layer, and is DPF-certified for US, EU, UK and CH data transfers.

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DPF US-EU-UK-CH
→ SECURITY WITH BENEFITS

SSO & SCIM

Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.

Alerts

Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.

Secure connection options

Securely connects to your systems with:

OAuth 2 SSH Tunnelling SSL certificates IP Whitelisting VPN gateway VPC peering and more

Related integrations

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