IBM Db2 to PostgreSQL Migration: 2 Reliable Methods

Introduction

Many enterprises are moving data from IBM Db2 to PostgreSQL as part of modernization initiatives. PostgreSQL offers lower licensing costs, strong ecosystem support, and flexibility across cloud and on-prem environments.

However, migrating or syncing data from Db2 to PostgreSQL is not always straightforward. Teams must decide whether they need a one-time migration or an ongoing, repeatable data pipeline.

This guide walks through two reliable methods used in production today. One focuses on a modern, managed pipeline approach, while the other covers a traditional approach commonly used by database teams.

Both options have tradeoffs, and the right choice depends on data freshness requirements, operational complexity, and long-term ownership.

Why Move from IBM Db2 to PostgreSQL

Organizations adopt PostgreSQL as part of broader modernization and cost-optimization efforts. Compared to proprietary databases, PostgreSQL offers greater flexibility and long-term control.

• Cost and Licensing Considerations:IBM Db2 licensing can become expensive as data volumes and workloads grow. PostgreSQL removes per-core and per-instance licensing, making cost more predictable.
• Ecosystem and Tooling: PostgreSQL integrates well with modern analytics tools, data platforms, and cloud services. Its open ecosystem makes it easier to adopt new technologies without vendor lock-in.
• Cloud and Platform Flexibility: PostgreSQL runs consistently across on-prem, cloud, and hybrid environments. This makes it a strong fit for teams modernizing legacy Db2 systems incrementally rather than all at once.

What Makes Db2 to PostgreSQL Hard

Moving data from IBM Db2 to PostgreSQL involves more than copying tables. Db2 environments vary widely across platforms and configurations.

1. Platform and Feature Differences: IBM Db2 runs across LUW (Linux, UNIX, and Windows), iSeries, and z/OS platforms. Each platform exposes different capabilities, which makes standardized change data capture difficult.
2. Limited CDC Options: Unlike some modern databases, Db2 does not provide a simple, universal CDC interface across all editions. This often forces teams to rely on polling or batch-based approaches.
3. Schema and Data Type Mismatches: Db2 and PostgreSQL differ in how they handle numeric precision, timestamps, and binary data. These differences must be handled carefully to avoid data loss or inconsistencies.
4. Operational Complexity: Custom scripts and manual jobs increase operational risk over time. Handling retries, failures, and incremental updates often becomes harder than expected.

If you’re also converting DB2 schema objects and SQL syntax (types, constraints, sequences, triggers, functions), the PostgreSQL wiki includes a DB2 UDB to PostgreSQL conversion reference you can use as a starting point.

Method 1: Sync IBM Db2 to PostgreSQL Using Estuary

This method is well suited for teams that need a dependable and repeatable way to move data from IBM Db2 into PostgreSQL. Estuary captures data by periodically executing queries against Db2 and materializes the results into PostgreSQL tables.

It works especially well when ongoing syncs, incremental updates, and predictable operations matter more than one-time exports.

This approach is commonly used with IBM Db2 for Linux, UNIX, and Windows (LUW), where polling-based access is the most reliable option.

When This Method Makes Sense

This approach is a good fit when:

• You need ongoing or scheduled data syncs from Db2 to PostgreSQL
• Manual export scripts are becoming hard to maintain
• You want predictable behavior without building custom orchestration
• Polling-based incremental updates are sufficient for your use case

It is especially useful when Db2 does not expose reliable CDC logs.

Prerequisites

Before creating the pipeline, make sure the following requirements are met:

• An IBM Db2 database that Estuary can reach, either directly or through an SSH tunnel
• A Db2 database user with read access to the tables or schemas you want to capture
• A PostgreSQL database accessible from Estuary
• An Estuary account (you can register for free at dashboard.estuary.dev/register)

Db2 User and Permissions

IBM Db2 commonly uses OS-level or LDAP-based user management. The database user must exist at the operating system or directory level before database privileges are granted.

Grant the user permission to connect to the database:

plaintext
GRANT CONNECT ON DATABASE TO USER flow_capture;

Grant read access to data. To allow access to all existing and future tables and views in a schema:

plaintext
GRANT SELECTIN ON SCHEMA myschema TO USER flow_capture;

If you prefer more granular control, grant SELECT on individual tables instead:

plaintext
GRANT SELECT ON myschema.mytable TO USER flow_capture;

Step 1: Create an IBM Db2 Capture

In the Estuary UI, open Sources, click + New Capture, and search for IBM Db2. Select the IBM Db2 Batch connector, which captures data by periodically executing queries against Db2 tables or views.

In Capture Details, provide:

• A unique capture Name
• The Data Plane where the capture will run

In Endpoint Config, fill in the required fields:

• Server Address: Db2 host or host:port
• User: database user (such as flow_capture)
• Password: password for the database user
• Database: logical Db2 database name

Under Advanced Options, you can define how and when data is read from Db2:

• Set a Default Polling Schedule such as 5m, 1h, or daily at 12:34Z
• Configure the Time Zone used to interpret Db2 TIMESTAMP values (defaults to UTC)
• Enable Discover Views if Db2 views should be captured
• Limit discovery using Discovery Schema Selection

If Db2 is hosted on a private network, configure Network Tunnel → SSH Forwarding. This allows Estuary to connect through a bastion host using an SSH endpoint and private key. Alternatively, you can allow-list Estuary’s IP addresses for your data plane.

Click Next to validate the connection and start the discovery process.

Estuary discovers tables and creates bindings that run either as full refreshes or incremental polls using cursor columns such as update timestamps or IDs. You can customize the query template used for each binding or use the default configuration.

Click Save and Publish.

For detailed configuration options and supported behaviors, see the IBM Db2 Batch connector documentation .

Step 2: Materialize Data into PostgreSQL

Next, go to Destinations, click + New Materialization, and select PostgreSQL. This materialization controls how captured Db2 data is mapped and written into PostgreSQL tables.

The PostgreSQL connector automatically creates destination tables based on the captured collections. Tables do not need to be created manually in advance.

In Materialization Details, set:

• A unique materialization Name
• The Data Plane

In Endpoint Config, provide:

• Address: PostgreSQL host and port
• User: PostgreSQL database user
• Database: PostgreSQL database name
• Database Schema: commonly public
• Optional Hard Delete behavior if source deletions should be applied

Select an Authentication method such as password-based authentication or a supported cloud IAM option. You can also configure SSL Mode, SSH forwarding, or an optional dbt Cloud Job Trigger if downstream transformations should run after materialization.

In Source Collections, make sure to link your IBM Db2 capture. Your capture bindings will populate the Collections table.

Click Next and Save & Publish.

At this point, the Db2 to PostgreSQL pipeline is configured and ready to run on a predictable schedule, automatically moving data based on the polling, discovery, and network settings you defined.

More details on PostgreSQL connection options, SSL modes, and table behavior are available in the PostgreSQL materialization connector documentation .

Pros and Cons

Pros

• Minimal custom code or scripting required
• Supports incremental polling using cursor columns
• Centralized configuration and monitoring
• Handles schema discovery and table mappings automatically
• Works well for repeatable and long-running pipelines

Cons

• Uses polling rather than log-based CDC
• Full-refresh tables can generate higher data volumes if not tuned
• Incremental accuracy depends on suitable cursor columns
• While it may work with Db2 platforms besides LUW (such as iSeries or z/OS), these platforms are not officially tested. If you use these versions, you can contact Estuary to discuss options.

Summary

This method prioritizes operational simplicity and predictability. For teams that need a dependable Db2 to PostgreSQL pipeline without ongoing script maintenance, it provides a balanced tradeoff between freshness, reliability, and effort.

Method 2: Export Data from IBM Db2 and Load into PostgreSQL

This method uses native Db2 utilities to export data into files and then loads those files into PostgreSQL. It is commonly used for one-time migrations or controlled batch transfers.

While familiar to many database teams, this approach requires manual scripting and ongoing maintenance if repeated regularly.

When This Method Makes Sense

This approach works best when:

• You are performing a one-time migration
• Data volumes are manageable
• A DBA team already owns export and load scripts
• Near real-time or frequent syncs are not required

It is less suitable for continuous or incremental data movement.

Step 1: Export Data from IBM Db2

IBM Db2 provides native utilities such as EXPORT and db2move to extract table data into files.

A common approach is using the EXPORT command to generate CSV or DEL files:

plaintext
EXPORT TO customers.del OF DEL
SELECT * FROM myschema.customers;

You can also export to CSV format if preferred. Each table must typically be exported separately unless wrapped in automation scripts.

Step 2: Transfer and Prepare Files

Once data is exported, files must be transferred to a system that can access PostgreSQL. This often involves SCP, SFTP, or shared storage.

Before loading, teams usually:

• Validate file encoding
• Ensure column order matches PostgreSQL tables
• Handle NULLs and date formats explicitly

Schema mismatches are a common source of load failures at this stage.

Step 3: Load Data into PostgreSQL

PostgreSQL’s COPY command is commonly used to load exported files efficiently.

Example using psql:

plaintext
COPY customers
FROM '/path/customers.del'
WITH (FORMAT csv, HEADER false);

For larger datasets, this step may require tuning PostgreSQL settings or batching loads to avoid lock contention and long transactions.

Ongoing Sync Considerations

This method does not provide built-in incremental logic. Updates and deletes must be handled manually, often by:

• Re-exporting full tables
• Tracking timestamps or IDs in custom queries
• Writing comparison or merge logic in PostgreSQL

As data volumes grow, these scripts become harder to maintain and monitor.

Pros and Cons

Pros

• Uses native Db2 and PostgreSQL tooling
• No external services required
• Suitable for controlled, one-time migrations

Cons

• Manual and script-heavy
• No native incremental or continuous sync
• Higher operational overhead over time
• Error handling and retries must be built manually

Summary

Export-and-load workflows remain a valid option for simple migrations. However, they require significant manual effort and do not scale well when ongoing synchronization or reliability becomes a priority.

Comparing the Two Methods

Both approaches can move data from IBM Db2 to PostgreSQL, but they differ significantly in how much effort they require to operate and maintain over time.

The table below highlights the practical differences teams usually care about.

Key Difference to Understand

The core difference is ownership.

Export-and-load workflows push ongoing responsibility onto scripts and operators, while managed pipelines centralize configuration and execution.

For teams planning long-term data movement, this difference often matters more than the initial setup time.

How to Choose the Right Method for Your Team

The right approach depends on how often data needs to move and how much operational effort your team can support. Both methods are valid, but they serve different needs.

Use Db2 Export and Load If

This method is usually the better choice when:

• You are performing a one-time migration
• Data volumes are relatively small
• A DBA team already manages batch jobs and scripts
• Ongoing synchronization is not required

It works best for controlled, short-lived projects.

Use Estuary If

This approach is a better fit when:

• You need ongoing or scheduled synchronization
• Incremental updates are required
• You want to reduce custom scripting and operational burden
• Reliability and repeatability matter more than ad-hoc flexibility

It is commonly chosen when Db2 remains active after PostgreSQL is introduced.

A Practical Rule of Thumb

If the migration has a clear end date, batch exports are often sufficient.
If Db2 and PostgreSQL must stay in sync for weeks or months, automated pipelines are typically easier to manage.

Final Thoughts

Moving data from IBM Db2 to PostgreSQL is a common step in database modernization, but the right approach depends on how long the systems need to coexist and how much operational effort your team can support.

Batch export and load workflows remain effective for simple, one-time migrations. They are familiar, transparent, and work well when data movement has a clear endpoint.

For teams that expect Db2 and PostgreSQL to run in parallel for weeks or months, managed pipelines like Estuary often reduce long-term maintenance by centralizing scheduling, incremental logic, and reliability in one place.

Choosing the right method early can save time, reduce risk, and make Db2 to PostgreSQL data movement easier to operate as requirements evolve.