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Data warehouse ⇄ AI

Dremio to Pinecone integration — real-time, two-way sync

Keep Dremio and Pinecone 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 Dremio and Pinecone

Send the records Dremio holds into Pinecone for embedding, classification, and scoring, and land what Pinecone produces back in Dremio as new columns, one two-way connection instead of a batch job.

Dremio holds the raw records the business runs on; Pinecone turns those records into embeddings, scores, labels, and summaries. The two meet wherever a warehouse row needs to be enriched by a model and the result needs somewhere durable to live. Most teams stitch that meeting together with export scripts and a queue, then spend their time keeping the glue alive.

Stacksync syncs Index statistics, Indexes, Vectors (records), Namespaces in Pinecone with Physical datasets, Virtual datasets (views), Apache Iceberg tables, Spaces and folders in Dremio field by field, in real time, and in both directions. Rows added or changed in Dremio flow into Pinecone as they happen, and the Index statistics, Indexes, Vectors (records), Namespaces that Pinecone generates land back in Dremio as columns or tables, with field-level mapping and conflict rules in place of a custom pipeline.

The payoff is that model output stops living in a separate place from the data it describes. Once results sit in Dremio, they join against billing, product, and usage tables already there, so you can report on quality, cost, and coverage without moving anything by hand.

Common use cases

  • 01 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.
  • 02 Write embeddings and their metadata into a Pinecone index from a Postgres or warehouse table so a semantic-search or RAG feature always queries fresh vectors.
  • 03 Sync curated Dremio views into an operational Postgres so applications get low-latency access to lakehouse data.
  • 04 Reverse-ETL aggregates computed over lake data out to CRMs and finance tools for business users.

Common sync patterns

One place to analyze AI results

Combine Pinecone's output with the tables already in Dremio to report on model quality, cost, and coverage without exporting anything to a spreadsheet.

History that outlives a run

A continuously synced copy in Dremio preserves every generated result, so outputs stay auditable even as they are overwritten or expire inside Pinecone.

Feed live warehouse records to Pinecone

Rows added or changed in Dremio flow into Pinecone within seconds, so embeddings, classifications, and enrichments are computed on current data rather than a nightly extract.

What you can sync between Dremio and Pinecone

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.

Dremio objects Pinecone objects How this pairing syncs
Virtual datasets (views) SQL views layering semantics over physical data; the preferred sync target for curated extracts. 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. Virtual datasets (views) is specific to Dremio and Namespaces to Pinecone — each maps to any object or custom field on the other side.
Apache Iceberg tables Lakehouse tables supporting DML and snapshot metadata usable for incremental reads. 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. Apache Iceberg tables is specific to Dremio and Collections to Pinecone — each maps to any object or custom field on the other side.
Spaces and folders Namespaces that organize virtual datasets and govern access. 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. Spaces and folders is specific to Dremio and Backups to Pinecone — each maps to any object or custom field on the other side.
Reflections Materialized accelerations that make repeated extraction queries cheaper. 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. Reflections is specific to Dremio and Index statistics to Pinecone — each maps to any object or custom field on the other side.
Jobs Query execution records useful for monitoring sync workloads. 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. Jobs is specific to Dremio and Indexes to Pinecone — each maps to any object or custom field on the other side.
Sources Connected storage and database systems (S3, ADLS, relational databases) Dremio queries in place. 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. Sources is specific to Dremio and Vectors (records) to Pinecone — each maps to any object or custom field on the other side.

How changes propagate between Dremio and Pinecone

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.

Dremio Pinecone Interval-based propagation

DetectionStacksync polls Dremio for changes on an incremental schedule, reading only records changed since the previous pass. Polling via SQL.

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

Pinecone Dremio 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 Dremio as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • Dremio: Bounded by engine capacity and workload management rather than API rate limits.
  • 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.
What ships with Dremio ⇄ Pinecone

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Dremio or Pinecone 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 Dremio or Pinecone record.

Observability

Monitoring

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

Trading partners

EDI

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

How the Dremio and Pinecone connectors work

Dremio

Integration surface
Arrow Flight SQL, JDBC/ODBC, and a REST API
Authentication
Personal access tokens or username/password; OAuth-based SSO on Dremio Cloud
Change detection
Polling via SQL; Iceberg table snapshots can anchor incremental reads; no consumer-facing change feed
Capabilities
read · write
Rate limits
Bounded by engine capacity and workload management rather than API rate limits

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.
How it works

How to connect Dremio to Pinecone — 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 Dremio and Pinecone 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
    Dremio connected
    Pinecone connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Dremio and Pinecone 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 · Dremio ⇄ Pinecone
    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
    Dremio Pinecone
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Dremio and Pinecone 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.

Learn more about security
SOC 2 Type II
ISO 27001
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CCPA
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

Every pair below is a real-time, two-way sync. Search all 407 integrations available for Dremio and Pinecone.

Popular · 7 of 407
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