Stream data from IBM Db2 Batch to Google Bigquery
Move data from IBM Db2 Batch 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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How to integrate IBM Db2 Batch with Google Bigquery in 3 simple steps
Connect IBM Db2 Batch as your data source
Set up a source connector for IBM Db2 Batch 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.

IBM Db2 Batch connector details
This connector captures data from IBM Db2 by periodically running SQL queries and converting results into JSON documents. It supports full-refresh, cursor-based incremental, and custom-query modes for flexible data ingestion into Estuary collections.
- Supports Db2 for LUW (Linux, UNIX, Windows); other Db2 variants may work but are untested.
- Allows full-refresh or cursor-incremental capture using timestamps or auto-increment IDs.
- Polling is configurable using interval strings like 5m or scheduled times like daily at 12:34Z.
- Tables without primary keys use /_meta/row_id, enabling automatic deletion detection during full refreshes.
Tip: Use a cursor column whenever possible — it dramatically reduces data volume, avoids full table scans, and ensures efficient incremental captures.

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
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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.

IBM Db2 Batch to Google Bigquery pricing estimate
Estimated monthly cost to move 800 GB from IBM Db2 Batch to Google Bigquery is approximately $1,000.
Data moved
Choose how much data you want to move from IBM Db2 Batch to Google Bigquery each month.
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Why pay more?
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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 IBM Db2 Batch 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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