B’etl Analyzer
Repository inventory, job discovery, component analysis, dependency mapping, complexity classification, unsupported-pattern identification, sizing and wave planning.
B’ETL™ MIGRATION CENTER
Modernize Legacy ETL with an Assessment-Led Migration Factory
Move from aging, expensive or difficult-to-maintain integration platforms to a modern Qlik and Talend architecture without treating migration as a blind code-conversion exercise.
Artha’s B’etl™-enabled migration factory combines automated analysis and conversion with architecture rationalization, reconciliation, testing and controlled production transition.
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Legacy Estate
Informatica · Legacy Talend · Hand-coded ETL
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B’etl Analyzer
Inventory · Dependencies · Complexity
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B’etl Converter
Mapping translation · Reusable patterns
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Validated Target
Reconciliation · Performance · Cutover
AI OVERVIEW
Modernize Legacy ETL with an Assessment-Led Migration Factory
Artha Solutions provides legacy ETL migration services for Informatica PowerCenter, IBM DataStage, Microsoft SSIS, Pentaho, iWay, legacy Talend and custom frameworks. B’etl™ supports inventory, dependency analysis, complexity classification, conversion and wave planning with human-led architecture and validation.
Artha SolutionsQlikQlik Talend CloudTalendTalend Data FabricTalend Data CatalogApache IcebergQlik Open LakehouseQlik AnswersQlik PredictQlik AutomateDatabricksSnowflakeB’etl
The Risk Is Not Moving Code. It Is Losing Business Logic.
Years of integration development create embedded dependencies, undocumented rules, duplicate jobs, operational workarounds and tightly coupled schedules. The rule that excludes a particular customer type from a revenue figure is usually a condition inside a transformation, added years ago for a reason nobody recorded. Convert the job faithfully and the rule survives; redesign it without knowing the rule exists and a number changes quietly at month end.
- Inventory the current estate
- Identify dependencies
- Rationalize redundant workloads
- Standardize reusable logic
- Select the right target pattern
- Automate suitable conversion
- Reconcile outputs
- Protect production continuity
This is why the assessment comes before the target decision. A code-conversion exercise that starts without an inventory is buying the same estate again in newer syntax, including the jobs that should have been retired.
One Migration Methodology Across Multiple Legacy Platforms
Informatica PowerCenter
Typical drivers
Licensing, aging estates and cloud modernization.
Complexity areas
Mappings, workflows, reusable objects, parameter files and custom transformations.
What B’etl™ analyzes
Repositories, lineage, dependencies, component compatibility and wave sizing.
Target options
Qlik Talend Cloud, modern Talend, cloud ELT or hybrid integration.
Validation
Mapping outputs, schedules, restart behavior and performance.
IBM DataStage
Typical drivers
Platform consolidation, skills risk and infrastructure modernization.
Complexity areas
Parallel jobs, sequences, stages, shared containers and runtime behavior.
What B’etl™ analyzes
Job graph, dependencies, stages, parameters and unsupported patterns.
Target options
Qlik and Talend patterns selected by workload.
Validation
Row-level reconciliation, sequencing and throughput.
Microsoft SSIS
Typical drivers
SQL Server modernization and movement toward cloud data platforms.
Complexity areas
Packages, control flow, script tasks, configurations and scheduling.
What B’etl™ analyzes
Package inventory, tasks, connections, expressions and dependencies.
Target options
Qlik Talend Cloud, cloud ELT, APIs or retained SQL patterns.
Validation
Package behavior, data types, errors and SQL-side logic.
Pentaho Data Integration
Typical drivers
Support posture, standardization and platform consolidation.
Complexity areas
Transformations, jobs, plugins, variables and custom steps.
What B’etl™ analyzes
Repositories, steps, hops, scheduling and plugin use.
Target options
Modern integration, data products or lakehouse pipelines.
Validation
Transformation results, orchestration and operational controls.
iWay and custom ETL
Typical drivers
Key-person risk, limited observability and architecture simplification.
Complexity areas
Custom adapters, scripts, undocumented logic and external schedulers.
What B’etl™ analyzes
Code and configuration inventory, interfaces, dependencies and runtime evidence.
Target options
API-led, event-driven, batch, CDC or hybrid patterns.
Validation
Business-rule review, integration testing and controlled cutover.
Legacy Talend
Typical drivers
Version risk, cloud transition and operating-cost reduction.
Complexity areas
Custom components, joblets, contexts, ESB, MDM and deployment practices.
What B’etl™ analyzes
Jobs, components, dependencies, runtime behavior and reuse.
Target options
Upgrade, standardize, Qlik Talend Cloud, retain or retire.
Validation
Regression, performance, deployment and recovery behavior.
Automation Where It Accelerates. Human Control Where It Matters.
B’etl Converter
Supported mapping translation, reusable conversion patterns, target-code generation, configuration mapping, logging patterns and conversion reporting.
Human-led assurance
Architecture rationalization, business-rule review, manual remediation, reconciliation, performance and security testing, cutover and production transition.
A Controlled Migration from Discovery to Production
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01
Discover
Inventory every job, its schedule and what it touches, taken from the repository and from runtime evidence rather than from documentation. Jobs that have not executed in a year surface here, and they are often a quarter of the estate.
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02
Assess
Score each workload on complexity, business criticality and target fit, and flag the near-duplicates. This is the stage that decides what is not worth migrating, which is the cheapest saving available in the whole programme.
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03
Design
Set the target patterns, naming and environment standards, then sequence the waves so the first one is genuinely low-risk and the dependencies within each wave are self-contained. Acceptance criteria are agreed before conversion starts, not negotiated during validation.
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04
Convert
Automate the patterns that translate reliably and route the rest to remediation, tracked as a named exception rather than absorbed silently. Custom components and script tasks are almost always in that second group.
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05
Validate
Row-count and value reconciliation between old and new for the same input, plus exception paths, performance under production volume, restart behaviour and security. Matching row counts on a happy-path test is the most common false confidence in ETL migration.
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06
Transition
Release by wave with a rehearsed cutover, parallel running where criticality justifies the cost, and a rollback that has been tested rather than described. Post-release verification is on the business output, not on job status.
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07
Optimize
Decommission the legacy jobs the new ones replaced, so licence and infrastructure savings are actually realised, and hand over runbooks to a team that has already worked an incident in the new platform.
Target Options Follow the Workload
Qlik Talend Cloud
The default for pipelines that need managed integration, transformation, quality and governance in one place, and for teams who would rather not maintain integration servers themselves.
Modern Talend architecture
Where data residency, network isolation or an existing operations capability makes client-managed the right call. Standardised patterns, current version, no runtime you cannot place where compliance requires.
Cloud-native ELT
When the warehouse or lakehouse is already the compute you are paying for. Pushing transformation down to it avoids moving large volumes twice, and suits set-based logic more than row-by-row rules.
Open lakehouse
High-volume analytical and historical data where storage cost dominates and several engines need to read the same tables. Apache Iceberg pipelines, described in more detail on the Open Lakehouse page.
API-led integration
Operational exchange between applications, where the requirement is a request answered in real time rather than a batch delivered on schedule. Frequently what a legacy ETL job was doing badly all along.
Hybrid integration
The honest answer for most large estates. Latency, regulation, licence economics and available skills rarely point at one destination, so the target is a deliberate combination with a documented rule for which workload goes where.
Migration Deliverables
The migration factory creates evidence and production assets, not only converted code.
- Current-state inventory and dependency map
- Complexity heatmap and rationalization recommendations
- Target-state architecture and migration backlog
- Wave plan and remediated workloads
- Reconciliation and performance evidence
- Cutover plan, knowledge transfer and support transition
CUSTOMER EVIDENCE
Implementation Experience Grounded in Enterprise Outcomes
Selected from Artha’s existing published case-study system. Customer anonymization is preserved.
Real-Time Logistics Analytics and ETL Modernization
Challenge: Inventory and logistics events took more than 24 hours to reach executive reporting, limiting hot-order visibility and increasing operational intervention.
Artha solution: Artha implemented Qlik Replicate change data capture, Qlik Compose modeling and a governed cloud analytics pipeline.
Qlik Cloud, Qlik Replicate, Qlik Compose, Snowflake, Microsoft Azure, Azure, Qlik
<2 minPublished logistics-data latency after modernization
Read the case study
SAP S/4HANA ERP Cloud Migration with AWS and Talend
Challenge: A manufacturing and construction enterprise needed to integrate SAP S/4HANA with decades of ERP history and a wider operational application landscape.
Artha solution: Artha used Talend, AWS and Snowflake to create a scalable integration and historical-data foundation for the modernization program.
Talend, AWS, Snowflake, Salesforce, SAP, MuleSoft
99%Published ERP integration accuracy
Read the case study
Talend Data Integration Platform Optimization for Large Enterprises
Challenge: Under-resourced Talend environments, hard-coded settings and limited monitoring constrained development, testing and cloud readiness.
Artha solution: Artha improved runtime configuration, reusable engineering patterns, environment controls and operational monitoring.
Talend
25–30%Published improvement in job execution and testing speed
Read the case study
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FREQUENTLY ASKED QUESTIONS
Questions Buyers and Architects Ask
Concise answers based on current Qlik product information and Artha’s consulting approach.
What is B’etl™?
B’etl™ is Artha’s migration accelerator for analyzing legacy ETL estates and automating suitable conversion work. It supports inventory, dependency mapping, complexity classification, sizing, target-code generation and reporting while architects retain control of rationalization, exceptions and validation.
Which ETL platforms can Artha assess?
Artha can assess Informatica PowerCenter, IBM DataStage, Microsoft SSIS, Pentaho Data Integration, iWay, legacy Talend environments and custom ETL frameworks. Exact analysis depth depends on repository access, exports, custom components and runtime evidence.
Can Informatica workloads be migrated to Qlik Talend Cloud?
Yes, suitable Informatica workloads can be modernized toward Qlik Talend Cloud. Artha first analyzes mappings, workflows, dependencies, transformations and operating behavior, then selects conversion, redesign, retention or retirement by workload.
Is ETL migration fully automated?
No. Automation can accelerate inventory, classification and supported conversion patterns, but architecture decisions, business-rule interpretation, unsupported components, reconciliation, security and production cutover require human control.
How does Artha preserve business logic?
Artha builds dependency and rule inventories, reviews critical transformations with business and technical owners, reconciles source and target outputs and maintains traceable acceptance evidence across migration waves.
How are converted jobs validated?
Validation includes unit, integration, row-count and value reconciliation, exception-path, performance, security, scheduling, restart and production-readiness tests according to workload criticality.
Can redundant jobs be removed during migration?
Yes, when evidence and accountable owners confirm redundancy. Rationalization is a deliberate governance decision; jobs are not removed solely because automated analysis finds similarity.
How is production disruption reduced?
Artha uses wave planning, environment controls, rehearsal, parallel validation where appropriate, cutover checklists, rollback provisions, monitoring and post-release verification.
Can migration support an Open Lakehouse target?
Yes. Suitable analytical workloads may target Qlik Open Lakehouse and Apache Iceberg. Workload fit depends on freshness, transformations, query patterns, AWS architecture, governance and operating responsibilities.
What is included in the initial migration assessment?
The assessment normally includes repository inventory, dependency mapping, complexity and risk classification, rationalization opportunities, target options, migration sizing, wave recommendations and a scoped validation approach.
NEXT STEP
Know What You Have Before You Decide How to Migrate It
Begin with evidence about workload size, dependencies, business criticality and target fit.
