GCP INTEGRATION

Work Project

Type

Product Strategy & Platform Expansion Initiative

Role

Product Manager

Focus Area

Hybrid Cloud Data Intelligence, Enterprise Lineage, Multi-Cloud Expansion, Cloud Modernisation

Platform / Domain

Rocket Software DI (Data Intelligence),  Enterprise Metadata Management & Data Lineage Platform

Framework

Enterprise Discovery → Strategic Prioritization → Platform Expansion → Customer Adoption

1) Overview

Rocket Software DI had strong recognition in mainframe and distributed lineage ecosystems (z/OS, IBM i, ETL, BI, RDBMS, Hadoop, AWS integrations), but there was a growing strategic gap:

• Enterprise customers were accelerating toward hybrid and multi-cloud architectures 

• GCP adoption was increasing rapidly in regulated industries

• Existing perception positioned Rocket primarily as a:

  • Mainframe lineage vendor

  • Distributed metadata/catalog vendor

• Competitors were increasingly positioning themselves as:

  • Cloud-native governance platforms

  • AI-ready data intelligence providers

  • 
    

At the same time, enterprise customers,  particularly large banks,  were beginning to modernise around a new architecture pattern:

Event Streaming → Cloud Processing → Cloud Data Warehouse

Within GCP, three tools consistently emerged as the foundation of these modern architectures:

• Pub/Sub 

• Dataflow 

• BigQuery 

This became the basis of the Google Cloud integration initiative.

2) Problem Statement

Rocket DI’s existing cloud coverage was heavily AWS-oriented:

• S3

• Redshift

• Glue Catalog

• Glue Jobs

• Hadoop/Hive ecosystems

  
  

However, customers adopting GCP lacked lineage visibility across:

• Real-time event pipelines

• Cloud-native transformations

• Serverless analytics environments

  
  

This created three strategic risks:

Risk	Impact
Incomplete multi-cloud coverage	Reduced competitiveness in modernisation programs
Weak positioning in cloud-native analytics	Seen primarily as legacy/mainframe-focused
Missing lineage across modern event architectures	Inability to support enterprise cloud transformation initiatives

3) Discovery & Opportunity Identification

Customer Signals

During customer discovery and roadmap discussions with enterprise accounts,  especially large financial institutions,  a recurring modernisation pattern emerged:

I. Enterprise Event Hub (EEH)

Customers were shifting from:

• Kafka / MQ

• Batch ETL

• On-prem integration layers

  
  

Toward:

• Google Pub/Sub 

• Event-driven architectures

• Streaming pipelines

  
  

II. Enterprise Query Hub (EQH)

Customers were centralising analytics into:

• BigQuery 

• Serverless cloud data warehouses

• Unified analytical ecosystems

III. Processing Layer Modernisation

Traditional ETL jobs were being replaced with:

• Dataflow 

• Apache Beam pipelines

• Stream + batch unified processing

4) Strategic Insight

I identified that supporting:

• Pub/Sub 

• Dataflow 

• BigQuery 

would not just add “another cloud connector.”

It would fundamentally reposition Rocket DI into:

• A hybrid-cloud lineage platform

• A modern cloud governance platform

• A cloud-native data intelligence provider

This became the key strategic argument presented internally.

5) Why These 3 Tools Were Prioritised

I. They Represented the Core GCP Data Stack

Together, these services formed the backbone of most enterprise GCP architectures:

Pub/Sub → Dataflow → BigQuery

Meaning Rocket could capture:

• Event lineage

• Transformation lineage

• Analytical lineage

through one connected ecosystem.

II. They Solved a Critical Lineage Gap

Existing lineage coverage handled:

• Mainframe batch jobs

• ETL tools

• Warehouses

• BI reports

But modern cloud pipelines introduced:

• Real-time events

• Streaming transformations

• Serverless analytical layers

without visibility.

Supporting these tools closed that gap.

III. They Created a New Market Position

This initiative helped reposition Rocket from:

“Mainframe + Distributed Shop”

to:

“Hybrid + Multi-Cloud Data Intelligence Platform”

This was strategically important because customers increasingly evaluated vendors based on:

• Cloud readiness

• AI readiness

• Multi-cloud governance

• Real-time data observability

6) Proposed Architecture & Vision

Target Enterprise Architecture

Mainframe / APIs / Applications       

↓    

Pub/Sub(Event Streaming Layer)

        ↓    

Dataflow(Transformation + Processing)

        ↓    

BigQuery(Enterprise Query Hub)

        ↓ 

BI / ML / Regulatory Systems

7) Real Enterprise Lineage Example

Financial Services Example

COBOL (z/OS DB2)

→ JCL Extract / CDC

→ Pub/Sub

→ Dataflow

→ BigQuery

→ Looker Dashboard

→ Vertex AI Fraud Model

→ Axiom Regulatory Reporting

This showed how Rocket could stitch lineage across:

• Legacy systems

• Distributed systems

• Cloud-native processing

• AI workflows

• Regulatory outputs

8) Business Impact

Outcomes

I. Revenue

• Contributed to:

  • $320K in newly contracted revenue 

  • Early adoption from 3 enterprise customers

II. Strategic Positioning

• Expanded Rocket DI’s identity from:

  • Legacy-focused

  • Mainframe-centric

to:

• Cloud modernisation partner

• Multi-cloud governance platform

III. Product Direction

• Established Google Cloud integrations as a core strategic roadmap pillar

• Influenced prioritisation away from smaller tactical features toward platform expansion

9) Key Contributions

Product Strategy

• Identified cloud modernisation trend and customer demand signals

• Defined the strategic importance of GCP integrations

Roadmap Prioritisation

• Built the business case for prioritising GCP integrations over existing backlog features

• Aligned roadmap decisions with:

  • Market positioning

  • Revenue opportunity

  • Competitive differentiation

Technical Architecture

• Defined realistic enterprise lineage flows across:

  • Mainframe

  • Distributed systems

  • GCP-native architectures

Customer Alignment

• Worked directly against enterprise modernisation requirements

• Positioned roadmap around real customer architecture patterns

10) Final Takeaway

This initiative was not simply about adding support for three cloud tools.

It was about transforming Rocket DI from:

a legacy lineage platform

into:

a modern hybrid + multi-cloud data intelligence platform

with visibility across:

• Mainframes

• Distributed ecosystems

• Real-time cloud pipelines

• AI/ML workflows

• Regulatory reporting architectures.