When organizations migrate DB2 for i data to platforms like Snowflake, Redshift, or Teradata, they aren’t necessarily moving off of their IBM i (AS400) platform. Instead, they’re creating a separate destination for the data to support analytics and other modern workloads.
In this case, the RPG applications and DB2 for i database can remain in place, continuing to run core business operations like payroll, order entry, inventory, or manufacturing. Meanwhile, a copy of this same data is made available on a modern platform for analytics, BI, AI, and other workloads that aren’t ideal for a production IBM i environment. This gives organizations a way to get more value from their existing IBM i data without disrupting the systems that run the business.
So why move DB2 data, and what should you consider when choosing a destination?
Why Do Companies Migrate Data?
Organizations typically move DB2 data to make it more accessible for modern analytics and enterprise initiatives, including:
- Enterprise reporting and data warehousing
- Power BI, Tableau, and other dashboards
- AI and machine learning
- Cloud data lakes
- Cross-system reporting
- Reducing analytical workloads on production IBM i environments
The goal isn’t necessarily to replace IBM i, but to give its data a second destination built for modern workloads.
What to Consider When Moving DB2 Data
There are several ways to move DB2 data, including batch ETL exports, journal-based Change Data Capture (CDC), APIs, data virtualization, and third-party replication tools. The right approach depends on how current the data needs to be and what the target platform requires. For analytics and cloud platforms where data needs to stay current, journal-based CDC is often the preferred approach because it avoids repeated table scans and supports near real-time analytics.
Regardless of which method you use, a few considerations apply across almost every DB2 migration.
Journaling is your CDC engine. Near-real-time replication typically reads IBM i journal receivers rather than polling tables. If the files you need aren’t journaled, that’s one of the first things to address.
AWS DMS requires a different approach for IBM i. The AWS Data Migration Service (DMS) doesn’t support DB2 on AS/400 as a direct source, so Redshift architectures typically require a dedicated CDC or replication tool between IBM i and AWS.
Data type conversion requires attention. Packed decimal, zoned decimal, and EBCDIC character encoding all need to be mapped correctly to the target platform. Packed decimal conversion errors in particular can lead to incorrect values or data-quality issues, so plan time for testing and validation.
Your schema doesn’t necessarily reflect your business logic. IBM i databases are often structured around how RPG applications use the data rather than how analysts want to query it. Some remodeling may be needed once the data reaches the new platform.
Common Risks to Plan For
A few additional considerations can affect the complexity and timeline of a migration:
- Data quality: Legacy inconsistencies, duplicates, and outdated values
- Historical data volume: Large initial loads can take significantly longer than ongoing CDC
- Network throughput: Initial loads can put pressure on available bandwidth
- Security and compliance: Encryption, access controls, masking, and auditing
- CDC latency: Monitoring replication delays and failed changes
Once the DB2 data is ready to move, the next step is choosing the right destination. Each platform offers different strengths depending on your needs for analytics, AI, real-time data, or modernization.
Data Platform Comparison for DB2 Migration
When considering where to migrate your data, the right destination depends less on what’s technically possible and more on what you need the platform to do.
Platform: | Best For: |
| Snowflake | BI, analytics, and AI |
| Amazon Redshift | AWS-first organizations |
| SingleStore | Real-time operational analytics |
| Teradata | Enterprise-scale data warehousing |
| Vertica | High-volume analytical workloads |
| Greenplum | Cost-conscious analytics |
| EnterpriseDB (EDB Postgres) | Application replatforming |
Snowflake
Snowflake is one of the most common options for a DB2 data migration and a strong default for organizations with broad BI, analytics, and AI/ML needs. Its separation of storage and compute provides flexibility to support different workloads without requiring teams to compete for the same resources.
A typical architecture is CDC tool → S3 or Azure Blob → Snowpipe → Snowflake, with data type conversion handled during staging.
SingleStore
SingleStore is a strong fit when the goal is real-time access to operational data rather than a traditional data warehouse. Its distributed SQL architecture and HTAP capabilities allow organizations to support transactional and analytical workloads on the same platform.
This makes it well suited for dashboards, operational analytics, and customer-facing applications that need fast access to constantly changing data. Examples include live inventory, order status, pricing, or other operational data that would be difficult or inefficient to query directly from a production IBM i environment.
Teradata
Teradata is often a natural choice for organizations that already use it as their enterprise data warehouse and need to incorporate IBM i data alongside other systems.
This is particularly common in larger manufacturing, distribution, and financial environments where DB2 for i is one of several sources contributing to enterprise reporting and analytics. Teradata’s focus on large-scale data warehousing makes it well suited for organizations with significant volumes of data.
Vertica
Vertica is a strong fit when query performance across very large datasets is the primary concern. Its columnar, massively parallel processing (MPP) capabilities are designed for high-volume analytical workloads where queries may need efficiently handle billions of rows. It is particularly suited to environments where analytical query speed is a higher priority than supporting general-purpose transactional workloads.
Greenplum
Similarly to Vertica, Greenplum provides a Postgres-based, MPP architecture designed for large-scale analytics and data warehousing. However, its open-source foundation can make it attractive to organizations that need warehouse-scale analytical capabilities while maintaining greater control over infrastructure and licensing costs. It can be a practical option for organizations with significant reporting and analytics requirements that don’t necessarily need a premium enterprise data warehouse platform.
Amazon Redshift
Amazon Redshift is a natural fit for organizations already invested in the AWS ecosystem, particularly those using services such as S3, Glue, and QuickSight. Its integration with the broader AWS environment can significantly simplify the development of data pipelines and analytics workflows.
Because AWS DMS cannot connect directly to DB2 for i, IBM i data typically requires an intermediate CDC or replication tool. A common architecture is CDC tool → S3 → Redshift, with AWS Glue or Redshift COPY handling the downstream loading process. Once established, this approach provides a scalable way to continuously make IBM i data available within an existing AWS data environment.
EnterpriseDB (EDB Postgres)
EnterpriseDB (EDB Postgres) is the outlier on this list because its primary use case is often application replatforming rather than analytics.
EDB can be a good fit when an organization wants to move an operational application away from DB2 for i while maintaining a transactional, ACID-compliant relational database environment. Its PostgreSQL foundation and Oracle compatibility can also be useful for organizations consolidating applications across multiple database technologies.
In this scenario, the goal isn’t simply to create an analytical copy of IBM i data, but to establish a new operational home for an application and its underlying data.
Choosing the Right Path for Your IBM i Data
The right destination depends on your business goals. Snowflake and Amazon Redshift are strong choices for organizations looking to expand BI, analytics, and AI capabilities in the cloud. Teradata, Vertica, and Greenplum are well suited for large-scale analytical workloads, with different considerations around enterprise requirements, performance, and cost. SingleStore is a strong option for real-time operational analytics, while EnterpriseDB is better suited for organizations looking to replatform an operational application rather than build an analytical data layer.
Importantly, moving IBM i data to a modern platform doesn’t have to mean replacing the systems that already run your business. Backed by IBM Champions and a deep bench of 300+ IBM i professionals, Programmers.io can help you evaluate the factors that are important to you, develop a practical migration strategy, and implement the technologies needed to move forward with confidence.
Our AI-powered IBM i migration and conversion services help organizations modernize applications and infrastructure while minimizing operational disruption. We also provide cloud hosting and transformation services to help build the data, integration, and cloud architecture needed to support your next stage of modernization.
Ready to explore your options? Connect with our experts to start your roadmap.
