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Ab Initio to Modern Cloud ETL Platforms using PDI ModernizeAgent
Migrate 90–95% faster with PDI ModernizeAgent.
PDI ModernizeAgent is an AI Agent that autonomously modernizes your data stack—converting Ab Initio ETL workflows into cloud-native pipelines with built-in governance, lineage, FinOps optimization, and quality guardrails.
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Key Features
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Ab Initio ETL migration tool with AI-driven conversion + explainable transformations
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Automated ETL conversion with pre/post validation and audit-ready report
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Ab Initio migration factory support (repeatable waves, parallel validation, rollback)
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Multi-target output: Ab Initio→Informatica IDMC, Ab Initio→Databricks (PySpark), Ab Initio→AWS Glue, Ab Initio→Azure Data Factory, Ab Initio→Snowflake ELT
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Built-in governance/lineage + FinOps optimization + quality guardrails.
Benefits
- Accelerate Ab Initio to cloud migration: cut migration time by 90- 95 % with AI automation
- Reduce risk: “pre/post migration checks” and audit reports for accuracy
- Lower effort: 95% reduction in manual ETL rework through automation
- Modernize faster with enterprise controls: track progress, parallel validation, rollback support
- Platform flexibility: standardize to the target that fits your operating model (IDMC vs PySpark vs Glue/ADF vs Snowflake ELT)
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Sample Use Case 1: Move to Informatica IDMC
Scenario: Consolidate integration + governance into a SaaS-first enterprise data management platform. (Informatica Knowledge)
Business Challenge:
Ab Initio on-prem teams hit pressure from cost/hardware cycles and ecosystem constraints while the business demands faster delivery and standardized cloud governance. (Gartner) Enterprises choosing IDMC often want an end-to-end platform approach (integration, catalog/governance/quality, etc.) and modernization programs explicitly positioned for legacy DI tools including Ab Initio → IDMC. (Informatica)
Solution with PDI ModernizeAgent:
- Discover Ab Initio graphs and dependencies, identify reusable patterns, and plan migration waves.
- Convert Ab Initio workflows into IDMC mappings/tasks and deployment-ready artifacts.
- Execute hybrid cutovers using Secure Agent / runtime environments and validate results at scale.
Benefits:
- Serverless runtime autoscaling reduces ops burden.
- Hybrid connectivity via Secure Agent model supports phased modernization.
- Consolidation into a cloud-native data management platform (IDMC positioning).
- Faster, lower-risk waves with automated validation and governance artifacts.
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Sample Use Case 2: Move to Databricks (PySpark)
Scenario: Replatform Ab Initio workloads into Python/Spark pipelines on a lakehouse for analytics + AI. (Databricks)
Business Challenge:
Teams want to escape a closed ecosystem and reduce reliance on specialized tooling by adopting widely available Python/Spark engineering skills, while also enabling stronger governance/lineage for modern analytics and AI programs. (Gartner)
Solution with PDI ModernizeAgent:
- Convert Ab Initio patterns into PySpark/SQL transformations, modern pipeline structure, and orchestrated runs.
- Map security + governance controls to Unity Catalog-aligned permissions/lineage patterns. (Databricks)
Benefits:
- Unified governance posture (access controls + lineage/auditing claims and lineage capture in Unity Catalog docs). (Databricks)
- Modern pipeline operations with job-based execution and reproducible deployments (lakehouse operating model).
- Better alignment to data engineering talent and open ecosystem.
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Sample Use Case 3: Move to Azure-native data integration (Azure Data Factory + Azure-SSIS IR, etc.)
Scenario: Use a managed cloud service for hybrid ETL/ELT and orchestration in a Microsoft-first estate.
Business Challenge:
Organizations want a managed platform for hybrid data integration and, in many estates, also need a runway for SSIS coexistence/lift-shift alongside broader modernization.
Solution with PDI ModernizeAgent:
- Convert Ab Initio pipelines into ADF pipelines/data flows and target-native compute integrations. (Microsoft Learn)
- If SSIS exists, support a phased plan where SSIS can run on Azure-SSIS IR while Ab Initio workloads modernize. (Microsoft Learn)
Benefits:
- ADF is a managed cloud service for complex hybrid ETL/ELT and data integration. (Microsoft Learn)
- Running SSIS in Azure can reduce operational costs and the burden of managing infrastructure (Microsoft positioning). (Microsoft Learn)
- Faster cloud alignment with Azure security/ops standards and centralized orchestration.
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Sample Use Case 4: Move to AWS-native data integration (AWS Glue + Step Functions, etc.)
Scenario: Standardize on serverless ETL + serverless orchestration inside AWS. (Amazon Web Services, Inc.)
Business Challenge:
Enterprises migrating data platforms to AWS want to eliminate “run/patch/upgrade” ETL infrastructure and adopt native services for faster integration and event-driven architectures.
Solution with PDI ModernizeAgent:
- Convert Ab Initio workflows into AWS Glue job patterns and orchestrate multi-step flows with Step Functions. (Amazon Web Services, Inc.)
- Automate validation and operationalization (logging, retries, alerts) as part of the migration factory.
Benefits:
- AWS Glue is serverless for data integration/ETL modernization. (Amazon Web Services, Inc.)
- Step Functions orchestrates AWS services into serverless workflows, speeding iteration. (Amazon Web Services, Inc.)
- Reduced platform lock-in to a proprietary ETL runtime; easier integration across AWS services.
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Sample Use Case 5: Move to Snowflake-native ELT + continuous pipelines (Snowpipe + Dynamic Tables + Streams/Tasks)
Scenario: Make Snowflake the center of gravity; do ingestion + transformation natively with continuous refresh.
Business Challenge:
Enterprises want to reduce pipeline sprawl and scheduling complexity by shifting transformations into the warehouse and adopting continuous ingestion/incremental processing patterns.
Solution with PDI ModernizeAgent:
- Convert Ab Initio loads into Snowflake ingestion patterns using Snowpipe auto-ingest. (Snowflake Documentation)
- Convert transformation logic into Snowflake ELT using Dynamic Tables (auto-refresh by query and target freshness) or Streams/Tasks for CDC-style pipelines. (Snowflake Documentation)
Benefits:
- Continuous, serverless ingestion patterns via Snowpipe auto-ingest. (Snowflake Documentation)
- Dynamic Tables simplify transformation/pipeline management with auto-refresh. (Snowflake Documentation)
- Streams + Tasks support continuous pipelines and CDC deltas. (Snowflake Documentation)
- Fewer external schedulers and less operational overhead for incremental refresh patterns. (Snowflake Documentation)
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Feedback From Customers
Revathi Bhimavarapu
Director of Business Intelligence & Analytics – STERIS Corporation
“Our data integration relied on Informatica PowerCenter for a long time. While it served us well, it was time to embrace a modern solution. Pacific Data Integrators' (PDI) expertise in Informatica Intelligent Data Management Cloud (IDMC) migration made the transition remarkably smooth. We successfully transferred data integration workloads, achieving a performance boost with the Informatica cloud-based platform (IDMC). This not only empowers our team but also positions us for future scalability.”
