Stream data from Amazon SQS to Google Bigquery
Move data from Amazon SQS to Google Bigquery in minutes using Estuary. Stream, batch, or continuously sync data with control over latency from sub-second to batch.
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- 200+Of connectors
- 5500+Active users
- <100msEnd-to-end latency
- 7+GB/secSingle dataflow
How to integrate Amazon SQS with Google Bigquery in 3 simple steps
Connect Amazon SQS as your data source
Set up a source connector for Amazon SQS in minutes. Estuary supports streaming (including CDC where available) and batch data capture through events, incremental syncs, or snapshots — without custom pipelines, agents, or manual configuration.
Configure Google Bigquery as your destination connector
Estuary supports intelligent schema handling, with schema inference and evolution tools that help align source and destination structures over time. It supports both batch and streaming data movement, reliably delivering data to Google Bigquery.
Deploy and Monitor Your End-to-End Data Pipeline
Launch your pipeline and monitor it from a single UI. Estuary guarantees exactly-once delivery, handles backfills and replays, and scales with your data — without engineering overhead.
Amazon SQS connector details
Estuary’s Amazon SQS connector continuously ingests messages from Amazon Simple Queue Service (SQS) queues into Estuary collections. It uses polling-based message retrieval, providing near real-time event capture from both standard and FIFO queues. Depending on configuration, messages can be retained or deleted after being read, giving you control over reliability and cost.
- Poll-based message capture for low-latency ingestion
- Supports both standard and FIFO SQS queues
- Optional delete-after-read mode to manage replay behavior
- Securely operates within Estuary’s managed or private cloud deployments
Google Bigquery connector details
Estuary’s Google BigQuery materialization connector writes Estuary collections into tables within a BigQuery dataset for scalable analytics and near real-time reporting. The connector stages data through Google Cloud Storage, then applies standard merges or high-speed delta updates so BigQuery stays current as upstream data changes.
- Materialize Estuary collections into BigQuery tables within a selected BigQuery dataset.
- Use a Google Cloud Storage bucket as a temporary staging area for reliable delivery into BigQuery.
- Create the staging bucket in the same region as the destination BigQuery dataset.
- Support both standard merge mode and delta update mode for performance and update flexibility.
- Use a Google Cloud service account with access to the BigQuery dataset, BigQuery jobs, BigQuery read sessions, and the staging GCS bucket.
- Enable clustering by primary keys where appropriate to improve query performance on materialized tables.
See how Cosuno uses Google Bigquery
Estuary in action
See how to build end-to-end pipelines using no-code connectors in minutes. Estuary does the rest.
Spend 2-5x less
Estuary customers not only do 4x more. They also spend 2-5x less on ETL and ELT. Estuary's unique ability to mix and match streaming and batch loading has also helped customers save as much as 40% on data warehouse compute costs.
Amazon SQS to Google Bigquery pricing estimate
Estimated monthly cost to move 800 GB from Amazon SQS to Google Bigquery is approximately $1,000.
Data moved
Choose how much data you want to move from Amazon SQS to Google Bigquery each month.
GB
Choose number of sources and destinations.
Why pay more?
Move the same data for a fraction of the cost.
What customers are saying
Estuary just works. We’ve never had an incident, and it cut our data movement costs in half.
Read the Success Story
We're a big fan of Estuary's real-time, no code model. It's magic that we're getting real time data without much effort and we don't have to spend time thinking about broken pipelines. We've also experienced fantastic support by Estuary.
Getting started with Estuary
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Make sure you read through the documentation, especially the get started section.
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Frequently Asked Questions
Is this integration suitable for production workloads?
Yes. Estuary pipelines are designed for production use, with exactly-once delivery semantics, automated backfills, and continuous operation at scale.
Can I control where my data runs and is processed?
Yes. Estuary offers multiple deployment options, including fully managed SaaS, private deployments, and bring-your-own-cloud (BYOC). This allows teams to control where their data plane runs and meet security, compliance, and networking requirements. Learn more about Estuary's security and deployment options.
Can I build this Amazon SQS to Google Bigquery integration manually?
Yes, it's possible to build a manual pipeline using custom scripts, scheduled jobs, or open-source tools. However, manual approaches typically require ongoing maintenance, custom error handling, schema management, and operational overhead. Estuary simplifies this by providing a managed pipeline with built-in reliability, scaling, and monitoring.
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DataOps made simple
Add advanced capabilities like schema inference and evolution with a few clicks. Or automate your data pipeline and integrate into your existing DataOps using Estuary's rich CLI.
