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Database ⇄ Data warehouse

Amazon DynamoDB to BigQuery integration — real-time, two-way sync

Keep Amazon DynamoDB and BigQuery 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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Why teams connect Amazon DynamoDB and BigQuery

Connect Amazon DynamoDB and BigQuery with one live, two-way sync: operational rows flow into the warehouse, and computed results flow back where systems can read them fast.

Operational databases and analytical warehouses want the same data at different moments. Analysts want Amazon DynamoDB's rows in BigQuery, current and joinable, without a change-data-capture pipeline to maintain. Engineers want the outputs of warehouse work, such as aggregates, features, and segments, available in Amazon DynamoDB where the services that read from it get them at normal query latency.

Stacksync covers both directions with one connection. Tables or collections in Amazon DynamoDB sync into BigQuery in real time, and result tables in BigQuery sync back into Amazon DynamoDB, with schema and type mapping between the two systems handled for you.

Common use cases

  • 01 Activate modeled BigQuery tables by syncing computed attributes back into sales and marketing tools
  • 02 Maintain a customer master table in BigQuery joined across CRM, billing, and support sources
  • 03 Backfill or migrate records into DynamoDB from another database using BatchWriteItem, then keep the two stores in continuous sync.
  • 04 Mirror a high-write DynamoDB table into a relational database so teams can join NoSQL application data against relational tables for reporting.

Common sync patterns

Offload heavy reads

Point analytical queries at the synced copy in BigQuery and keep Amazon DynamoDB focused on its operational workload.

Operational data in the warehouse, minus the pipeline

Rows from Amazon DynamoDB land in BigQuery as they change, replacing hand-built CDC and batch extract jobs.

Serve warehouse results at database speed

Aggregates or model outputs computed in BigQuery sync into Amazon DynamoDB, where whatever reads from that database gets them without querying the warehouse.

What you can sync between Amazon DynamoDB and BigQuery

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.

Amazon DynamoDB objects BigQuery objects How this pairing syncs
Tables Top-level containers, each with a partition key and optional sort key; Stacksync syncs a table as a stream of items with full read and write via PutItem, UpdateItem, and DeleteItem. Tables The syncable unit: only tables can be synced per the Stacksync docs. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Time to Live (TTL) Per-item expiry timestamps; DynamoDB deletes expired items in the background and emits a Streams REMOVE record for each deletion. Projects Connection scope: the service account grants access per project. Time to Live (TTL) is specific to Amazon DynamoDB and Projects to BigQuery — each maps to any object or custom field on the other side.
Items Individual schemaless records (attributes up to 400 KB each); read with GetItem, Query, and Scan and written with PutItem or BatchWriteItem, so write is supported here. Partitioned tables Synced like regular tables; partition columns map to target fields. Items is specific to Amazon DynamoDB and Partitioned tables to BigQuery — each maps to any object or custom field on the other side.
Global secondary indexes (GSIs) Alternate key projections that let you Query by non-key attributes without a full table Scan; read-only views maintained automatically by DynamoDB. Clustered tables Supported; clustering is transparent to the sync. Global secondary indexes (GSIs) is specific to Amazon DynamoDB and Clustered tables to BigQuery — each maps to any object or custom field on the other side.
Local secondary indexes (LSIs) Extra sort keys within the same partition key, defined at table creation; queried like the base table for alternate access patterns. Datasets Organizational container — you pick which dataset’s tables to sync. Local secondary indexes (LSIs) is specific to Amazon DynamoDB and Datasets to BigQuery — each maps to any object or custom field on the other side.

How changes propagate between Amazon DynamoDB and BigQuery

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.

Amazon DynamoDB BigQuery Sub-second propagation

DetectionChanges in Amazon DynamoDB are captured at the source via change data capture — no polling loop against its API. DynamoDB Streams emit ordered item-level change records (INSERT, MODIFY, REMOVE) with KEYS_ONLY, NEW_IMAGE, OLD_IMAGE, or NEW_AND_OLD_IMAGES views.

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

BigQuery Amazon DynamoDB Sub-second propagation

DetectionChanges in BigQuery are captured at the source via change data capture — no polling loop against its API. Real-time notification service deployed into your Google Cloud project: Eventarc ("a notification service that enables real-time updates to happen").

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

Rate-limit considerations

  • Amazon DynamoDB: Throughput is metered in read/write capacity units (provisioned or on-demand): 1 WCU = one 1 KB write per second, 1 RCU = one strongly-consistent 4 KB read per second. Exceeding capacity or the ~3,000 RCU / 1,000 WCU per-partition ceiling returns ProvisionedThroughputExceededException with throttling.
  • BigQuery: Subject to Google Cloud quotas on queries, DML, and streaming; DML is supported but the platform favors append-heavy batch and streaming loads over row-at-a-time writes.
What ships with Amazon DynamoDB ⇄ BigQuery

Connect Amazon DynamoDB and BigQuery for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Amazon DynamoDB–BigQuery connection.

Real-time

Two-way sync

Changes in Amazon DynamoDB or BigQuery instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Amazon DynamoDB or BigQuery 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 Amazon DynamoDB or BigQuery record.

Observability

Monitoring

Track your Amazon DynamoDB ⇄ BigQuery sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Amazon DynamoDB and BigQuery.

How the Amazon DynamoDB and BigQuery connectors work

Amazon DynamoDB

Integration surface
AWS SDK / low-level HTTPS JSON API at dynamodb.<region>.amazonaws.com (PutItem, GetItem, UpdateItem, DeleteItem, Query, Scan, BatchWriteItem, TransactWriteItems), plus PartiQL (ExecuteStatement) for SQL-style access and DynamoDB Streams for change capture.
Authentication
AWS Signature Version 4 (SigV4) signed requests using an IAM access key ID and secret key, or temporary STS credentials from an assumed IAM role; IAM policies scope access down to table and item level.
Change detection
DynamoDB Streams emit ordered item-level change records (INSERT, MODIFY, REMOVE) with KEYS_ONLY, NEW_IMAGE, OLD_IMAGE, or NEW_AND_OLD_IMAGES views and 24-hour retention, read via shard iterators (or Kinesis Data Streams for longer retention). No native HTTP webhooks.
Capabilities
read · write · CDC
Rate limits
Throughput is metered in read/write capacity units (provisioned or on-demand): 1 WCU = one 1 KB write per second, 1 RCU = one strongly-consistent 4 KB read per second. Exceeding capacity or the ~3,000 RCU / 1,000 WCU per-partition ceiling returns ProvisionedThroughputExceededException with throttling.

BigQuery

Integration surface
GoogleSQL via the BigQuery REST API, client libraries, JDBC/ODBC drivers, and the Storage Read/Write APIs
Authentication
Google Cloud service account: create a dedicated service account, grant roles (BigQuery Data Editor, BigQuery Job User, Cloud Functions Service Agent, Cloud Run Developer, Eventarc Event Receiver
Change detection
Real-time notification service deployed into your Google Cloud project: Eventarc ("a notification service that enables real-time updates to happen") with a Cloud Run "secure portal for real-time notification service in
Capabilities
read · write · CDC
Rate limits
Subject to Google Cloud quotas on queries, DML, and streaming; DML is supported but the platform favors append-heavy batch and streaming loads over row-at-a-time writes
BigQuery setup guide
How it works

How to connect Amazon DynamoDB to BigQuery — 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 Amazon DynamoDB and BigQuery 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
    Amazon DynamoDB connected
    BigQuery connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Amazon DynamoDB and BigQuery 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 · Amazon DynamoDB ⇄ BigQuery
    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
    Amazon DynamoDB BigQuery
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Amazon DynamoDB and BigQuery integration FAQ

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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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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 574 integrations available for Amazon DynamoDB and BigQuery.

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