Integrating IBM z mainframe data with cloud analytics using Fivetran

IBM z systems process an estimated 68% of the world's production workloads, power 87 of the Fortune 100, and remain the backbone of industries like banking, insurance, healthcare, and government. The data processed by these systems – transactional records, customer histories, financial ledgers, compliance logs – is among the most valuable data an enterprise owns.

But the data is often under-utilized: it too often sits in isolation, disconnected from the cloud-based analytics, AI models, and business intelligence tools where your organization makes decisions. When 76% of IT decision-makers say that accessing mainframe data is a barrier to using it effectively, the problem isn't the data itself, it's the pipeline.

This guide explains why integrating your IBM z mainframe data with other sources matters, what makes it difficult, and how Fivetran helps you do it without disrupting the systems your business depends on.

Why mainframe data integration matters now

For decades, mainframe data served its purpose within the mainframe environment. Batch reports ran overnight. Analysts submitted JCL jobs and waited. That model worked when the mainframe was the entire analytical universe.

Today, your business runs on a combination of systems. Customer interactions happen in Salesforce. Marketing campaigns run through HubSpot. Product telemetry flows from cloud applications. Financial transactions still process on z/OS. When these datasets remain siloed, you're making decisions with partial information.

The cost of data silos

Consider a bank that processes millions of credit card transactions daily on its IBM z mainframe. The fraud detection team wants to combine those transaction patterns with behavioral signals from the bank's mobile app, social engineering indicators from its email platform, and market data from third-party APIs. Each data source tells part of the story. Together, they tell the whole story.

Or consider a healthcare payer whose claims processing runs on Db2 for z/OS. When that claims data is isolated from the member engagement data in their CRM and the provider network data in their cloud data warehouse, the organization can't perform the kind of cross-functional analysis that drives better outcomes and lower costs.

Integrating mainframe data with cloud analytics isn't about replacing the mainframe. It's about making the mainframe a first-class participant in your modern data stack.

What's driving urgency

Several forces are converging to make mainframe data integration a strategic priority rather than a nice-to-have:

‍AI and machine learning demand complete datasets. Models trained on incomplete data produce incomplete insights. Your mainframe holds decades of transactional history that can dramatically improve the accuracy of predictive models — but only if that data is accessible in the environments where models are trained and deployed.

‍Real-time expectations are rising. Customers, regulators, and internal stakeholders expect insights measured in minutes, not days. Batch-based mainframe reporting can't meet that standard on its own.

‍Talent dynamics are shifting. The pool of COBOL and mainframe-skilled professionals is shrinking. By making mainframe data available in environments where modern data teams already work — SQL-based warehouses, Python notebooks, BI dashboards — you reduce your dependency on specialized skills for analytical workloads while preserving mainframe expertise for what it does best: running core transactions.

What makes mainframe data integration difficult

If connecting mainframe data to the cloud were simple, every enterprise would already be doing it. Several characteristics of mainframe environments make integration more complex than connecting two cloud applications.

Unique data formats and structures

IBM z systems store data in formats that don't translate directly into the relational tables your cloud warehouse expects. Db2 for z/OS uses EBCDIC encoding rather than ASCII. VSAM (Virtual Storage Access Method) files organize data in key-sequenced, entry-sequenced, and relative-record datasets that have no direct analog in cloud storage. IMS (Information Management System) databases use hierarchical structures that predate the relational model entirely. CICS transaction data carries its own set of conventions.

Each of these formats requires specialized knowledge to extract, decode, and transform into a structure that downstream tools can consume.

Performance and cost sensitivity

Mainframe environments operate under strict resource governance. CPU cycles on z/OS are measured in MSUs (Million Service Units) and directly tied to licensing costs. Any integration approach that runs heavy extract jobs on the mainframe itself risks driving up MIPS consumption and triggering licensing cost increases. The right integration strategy captures data efficiently — ideally through log-based change data capture — to minimize mainframe resource usage.

Security and compliance requirements

Mainframe data is often subject to the strictest regulatory requirements in an organization. HIPAA, PCI DSS, SOX, GDPR — the compliance frameworks that govern mainframe data don't relax just because the data moves to the cloud. Integration solutions must support encryption in transit and at rest, maintain audit trails, and respect the access controls already defined in RACF or ACF2.

Organizational and cultural factors

In many organizations, the mainframe team and the cloud data team operate in separate worlds with different tools, different vocabularies, and different priorities. Successful mainframe data integration requires a solution that both sides can trust — one that doesn't require the mainframe team to cede control and doesn't require the cloud team to learn ISPF.

How Fivetran approaches mainframe data integration

Fivetran's philosophy is that data movement should be automated, reliable, and low-impact. That philosophy extends to mainframe environments. Rather than asking you to rip and replace your mainframe infrastructure, Fivetran meets your IBM z systems where they are and moves data to where your analysts and data scientists need it.

Db2 for z/OS: log-based CDC with minimal mainframe impact

Fivetran's Db2 for z/OS connector uses log-based change data capture to replicate data from your mainframe database to any supported destination — including Snowflake, BigQuery, Databricks, Azure Synapse, Amazon S3, and more.

Here's what that means in practice:

‍Log-based capture, not query-based extraction. Fivetran reads directly from the Db2 z/OS transaction logs using the IFI 306 interface via stored procedures installed on z/OS. This approach captures inserts, updates, and deletes as they happen without running SELECT queries against production tables. The result is near-real-time replication with minimal CPU overhead on the mainframe.

‍Automatic schema management. When your Db2 z/OS schema changes — new columns, altered data types, structural DDL changes — Fivetran detects and adapts automatically through its AdaptDDL capability. Your downstream pipelines don't break because someone added a column to a production table.

‍Support for complex data types. The connector handles tables with LOB columns, tables without primary keys, and truncate operations — common scenarios in mainframe Db2 environments that trip up simpler integration tools.

‍Fully managed pipeline operations. Once configured, Fivetran handles monitoring, failure recovery, and incremental updates. Your mainframe team doesn't need to babysit the pipeline, and your data team doesn't need to worry about stale data.

Db2 for i (AS/400 and IBM i): extending IBM platform coverage

For organizations running IBM i alongside or instead of z/OS, Fivetran provides a dedicated Db2 for i connector with both a High-Volume Agent and HVR deployment options. The connector uses journal-based capture (via the DISPLAY_JOURNAL table function) to efficiently replicate data from IBM i environments, supporting both SaaS and Hybrid Deployment models.

This means Fivetran covers the two most common IBM relational database environments — z/OS and IBM i — through a single, unified platform. Your team manages one integration tool, one set of monitoring dashboards, and one set of transformation workflows regardless of which IBM system the data comes from.

Hybrid Deployment for sensitive workloads

For organizations that need to keep data processing within their own network perimeter — common in mainframe-heavy industries like financial services and government — Fivetran offers a Hybrid Deployment model. Data is processed locally within your infrastructure before being loaded to your cloud destination. This satisfies the security and compliance teams who rightfully insist that mainframe data never traverse an uncontrolled network path.

700+ connectors for the "other" data

The real power of integrating mainframe data isn't the mainframe connection alone — it's what happens when mainframe data meets everything else. Fivetran's library of over 700 pre-built connectors means you can blend your Db2 z/OS transaction data with:

• SaaS application data from Salesforce, ServiceNow, Workday, SAP, and hundreds of other business applications
• Cloud database data from PostgreSQL, MySQL, MongoDB, and other modern databases
• Event and streaming data from webhooks, cloud functions, and event-driven architectures
• File-based data from SFTP, S3, GCS, and Azure Blob Storage

This is where mainframe data integration delivers outsized returns. It's not just about getting Db2 data into Snowflake. It's about joining thirty years of transaction history with last week's Salesforce pipeline, this morning's web analytics, and tomorrow's demand forecast — all in one place, all kept current automatically.

How Fivetran compares for mainframe data integration

The mainframe data integration space includes several established players. Here's how Fivetran's approach differs:

Purpose-built for the modern data stack

Many legacy mainframe integration tools were designed to move data between mainframes and other on-premises systems. Fivetran was built from the ground up for cloud destinations. Every connector is optimized for cloud data warehouses and data lakes, with native support for Snowflake, BigQuery, Databricks, Redshift, and other modern platforms. You aren't adapting a tool designed for a different era — you're using one designed for where data work happens today.

Breadth beyond the mainframe

Legacy mainframe integration vendors excel at mainframe-to-mainframe or mainframe-to-on-premises movement. But your data doesn't just live on the mainframe. With 600+ connectors spanning SaaS applications, databases, events, and files, Fivetran is the single platform that connects your mainframe data with everything else. One platform, one interface, one operational model.

Automated and fully managed

Traditional mainframe integration requires significant operational overhead: managing extract schedules, monitoring job failures, handling schema drift manually, and tuning performance. Fivetran automates all of this. Schema changes are handled automatically. Failures trigger automatic recovery. Incremental updates run continuously. Your team spends time on analysis, not plumbing.

Transparent, usage-based pricing

Mainframe integration tools have historically come with complex licensing models tied to MIPS, MSUs, or data volume in ways that are difficult to predict. Fivetran's pricing is transparent and based on monthly active rows, making it straightforward to forecast costs and scale predictably.

Getting started

Connecting your IBM z mainframe data to your cloud data warehouse with Fivetran is straightforward:

1. Set up your destination. Connect Fivetran to your cloud data warehouse or data lake — Snowflake, BigQuery, Databricks, or any of the other supported destinations.

2. Configure your Db2 for z/OS connector. Provide your connection details, install the required capture stored procedures on z/OS, and select the schemas and tables you want to replicate.

3. Start syncing. Fivetran performs an initial full sync, then switches to incremental log-based replication. Your mainframe data appears in your destination, kept current automatically.

4. Blend and analyze. With mainframe data landing alongside data from your other 600+ sources, your analysts and data scientists can build the cross-platform analyses that were previously impossible.

Frequently asked questions

Will Fivetran increase my mainframe MIPS costs?

Fivetran's log-based CDC approach is designed to minimize mainframe resource consumption. By reading from transaction logs rather than querying production tables, the connector avoids the CPU-intensive operations that drive up MIPS costs. The stored procedures installed on z/OS are lightweight and purpose-built for efficient log reading.

Does Fivetran support data sources beyond Db2 on the mainframe?

Fivetran currently offers deep support for Db2 for z/OS and Db2 for i (IBM i / AS/400). For other mainframe data stores like VSAM files, IMS databases, and CICS transaction data, Fivetran's Connector SDK enables custom connector development, and the Fivetran team continues to expand its IBM platform coverage based on customer demand. Contact Fivetran to discuss your specific mainframe data landscape.

Can I keep my data processing on-premises?

Yes. Fivetran's Hybrid Deployment model allows data processing to happen within your own network infrastructure. This is particularly relevant for mainframe environments where data sovereignty, regulatory compliance, and network security requirements are strict.

How does Fivetran handle EBCDIC and other mainframe-specific encoding?

Fivetran's Db2 for z/OS connector handles the translation between mainframe-native data formats and the formats your cloud destination expects. You don't need to build or maintain custom encoding translation logic.

What if I want to keep using my mainframe — not migrate off it?

That's exactly the point. Fivetran is not a mainframe migration tool. It's a mainframe data integration tool. The goal is to make your mainframe data accessible and useful in modern analytics environments while your mainframe continues doing what it does best: processing transactions at scale with industry-leading reliability.

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‍Your mainframe data is some of the most valuable data in your organization. Stop letting it sit in a silo. Start a free trial or request a demo to see how Fivetran brings your IBM z data into your modern data stack.

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