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DS Srishti

Building governed medallion lakehouses where the data model is derived from evidence — not drawn in workshops.

ERP • WAREHOUSE
01. Sources / Legacy
DS SRISHTIEvidence Engine
02. Derived Modeling
MEDALLION
03. Governed Products

Brownfield vs. Greenfield Modernization

DS Srishti adapts seamlessly to your architectural starting point while delivering the exact same governed medallion lakehouse destination.

BROWNFIELD

ALL 7 STAGES

Starting point: a legacy warehouse and the ETL estate around it.

01 AnalyseLegacy estate inventory and complexity scoring
02 LineageReconstructed across warehouse, ETL and BI
03 RationaliseRetire what is unused before you pay to convert it
04 ModelDerived from legacy evidence and real usage
05 ConvertLegacy code converted, validated, reconciled
06-07 Semantic & ConsumeGoverned layer and self-service

GREENFIELD

5 STAGES

Starting point: source systems directly — ERP, CRM, operational, APIs, streams.

01 ProfileSource systems, schemas, keys, relationships
02 LineageSource-to-target dependency map established
— RationaliseNot applicable — nothing legacy to retire
04 ModelDerived from source metadata, not workshops
05 GenerateNet-new pipelines generated and validated
06-07 Semantic & ConsumeGoverned layer and self-service
Execution Blueprint

Seven-Stage Operating Model

Nothing advances on assumption. Every stage produces an inspectable artifact that can be reviewed before the next begins.

1
ANALYSE

Estate Inventory & Scoring

Inventory tables, views, procedures, ETL jobs, and reports. Score complexity objectively and forecast automation rate.

DS PROCESS ANALYZER
2
LINEAGE

End-to-End Lineage

Reconstruct column-level lineage across legacy data warehouse, ETL scripts, and BI report universes.

DS DATAMAPS
3
RATIONALISE

Legacy Object Retirement

Identify and retire unused tables, redundant ETL jobs, and duplicate reports before paying to convert them.

DS DATAMAPS
4
MODEL

Medallion Model Derivation

Derive medallion lakehouse model (Bronze/Silver/Gold) from evidence and real query usage — not workshops.

DS DATAMAPS – AGENTIC
5
CONVERT

Automated Conversion

Perform agentic process conversion for ETL jobs, SQL, stored procedures with syntax validation and reconciliation.

DS MIGRATE + DATACITIZEN
6
SEMANTIC

Certified Metric Catalog

Publish certified metric definitions, KPI business logic, and semantic layer schemas for single source of truth.

DS INSIGHTCITIZEN
7
CONSUME

Data Product Self-Service

Enable governed self-service, real-time analytics, and data product API publishing for AI & BI consumers.

FULL PLATFORM

Legacy-to-Medallion Transformation

Each legacy layer is re-expressed agentically as the medallion layer that now performs its job.

LEGACY LAYERWHAT SRISHTI DOESTARGET MEDALLION LAYER
Source SystemsConnect directly; extraction logic regenerated rather than simply repointed.SOURCES
Staging / Landing AreaConvert into an immutable, partitioned, replayable landing layer. Nothing overwritten in place.BRONZE
ODS / Integration LayerLift cleansing, deduplication, and conformance logic out of legacy ETL and express as governed transformations.BRONZE → SILVER
EDW Core / 3NFRe-derive the normalised core as conformed domain entities, business keys, and SCD strategy.SILVER
Data Marts / Star SchemasRebuild marts as domain data products attached with contract, owner, quality SLA, and lineage.GOLD
BI Semantic / UniversesConvert universe and report logic into a single certified semantic and KPI layer.SEMANTIC

Target Medallion Lakehouse

Sources → Bronze → Silver → Gold → Semantic / Consumption. Open table formats throughout; every layer governed.

BRONZEEngineering Only

BRONZE

raw · immutable · replayable

  • Data landed exactly as received from source systems
  • Partitioned by source system and load date
  • Full replay possible without re-extraction from sources
  • Schema drift logged automatically; changes never silently dropped
Quality GatedOpen Table Format
SILVEREngineering + Data Analysts

SILVER

cleansed · conformed · governed

"Silver is where the enterprise model actually lives."

  • Deduplicated and standardized domain entities
  • Conformed business keys across enterprise domains
  • Slowly Changing Dimensions (SCD Type 1/2) handling
  • Automated survivorship and conflict resolution rules
Quality GatedOpen Table Format
GOLDBusiness · AI Agents · BI & Analytics

GOLD

data products · business-ready

"Gold outputs are treated as managed, versioned data products."

  • Domain-specific marts and aggregated analytical models
  • Published semantic contracts and versioned interfaces
  • Enforced quality SLAs (Freshness, Completeness, Accuracy)
  • Registered in catalog with business glossary and lineage
Quality GatedOpen Table Format
Gold Layer Standard

Governed Data Products

"A table is something you query. A data product is something you can depend on."

Named Owner

Accountable steward and domain owner for every product

Schema Contract

Published, versioned interface; breaking changes visible before consumer impact

Quality SLA

Automated freshness, completeness, and accuracy checks on every load

Full Lineage

Source table → field → transformation → product tracing for engineers & auditors

One Definition

Single certified KPI meaning across dashboards, ML models, and AI agents

Discoverability

Catalog registration with glossary terms for self-service discovery without requests

Comprehensive Platform Coverage

DS Srishti supports virtually every major legacy data warehouse, ETL engine, modern cloud platform, and reporting tool.

LEGACY WAREHOUSES
Teradata
BTEQ, TPT, procedures, macros
Netezza
nzsql, NZPLSQL procedures
Oracle Exadata
PL/SQL, packages, MVs
Oracle
PL/SQL, jobs, partitioning
SQL Server
T-SQL, agent jobs
Greenplum · Vertica
SQL, UDFs
IBM Db2 · Informix
SQL PL, stored procedures
ETL & INTEGRATION
Informatica PowerCenter
mappings, workflows, mapplets
IBM DataStage
parallel & sequence jobs
Microsoft SSIS
packages, script tasks
Talend
jobs, joblets, routines
Ab Initio
graphs, plans
Oracle ODI · OWB
interfaces, packages
Pentaho · Matillion
transformations, orchestration
MODERN TARGETS
Databricks
Delta, Unity Catalog, DLT, PySpark
Snowflake
Snowflake SQL, dynamic tables
dbt
models, tests, docs, lineage
Microsoft Fabric
Lakehouse, notebooks, OneLake
Google BigQuery
BigQuery SQL, Dataform
Open lakehouse
Apache Iceberg on object store
Informatica IDMC
CDI, CDQ, CDGC

The reporting layer is converted too — OBIEE, Cognos, BusinessObjects, SSRS and MicroStrategy logic migrated alongside the data, so the whole estate lands together.

95%+
Automation Rate

Deterministic, syntax-validated generation of targets

155K+
Objects Modernized

Successfully delivered across global enterprise estates

500K+
Scripts Converted

Across legacy warehouse, ETL, and procedure environments

Evidence-Gated Engagement Model

You see proof before committing to the next phase. Nothing is taken on trust.

01
2–3 weeks

ASSESSMENT

Full estate inventory, column-level lineage, usage profiling, and objective complexity scoring.

DELIVERABLEAssessment Report (belongs to customer regardless of next step)
02
4–6 weeks

MODEL & PILOT

Derive medallion model, review with customer architects, convert a representative slice end-to-end, and reconcile results.

DELIVERABLETarget Model & Reconciled Pilot Pipeline
03
Scale-dependent

INDUSTRIALIZE

Full conversion in waves organized by domain, validation on every load, reconciliation, and publishing Gold data products.

DELIVERABLEDomain Data Products & Pipelines
04
2–4 weeks

TRANSITION

Runbooks, enablement, train-the-trainer, platform operations handover, and hypercare through stabilization.

DELIVERABLEOperational Handover & Hypercare

Frequently Asked Questions

DS Srishti builds a governed medallion lakehouse with data products at the top. A migration tool asks: 'How do we move an existing estate?' Srishti asks: 'What should the target model be?' It derives that target model agentically from legacy metadata, usage evidence, and source systems, then generates and validates the pipelines needed to deliver it.
Yes. Brownfield uses all seven stages including estate rationalisation. Greenfield starts by profiling source systems (ERP, CRM, APIs, streams) directly and skips legacy rationalisation (since there is nothing legacy to retire). The stages involving Model, Generate, Semantic, and Consumption remain common to both approaches.
Agentic data modeling derives conceptual, logical, and physical models directly from real metadata and usage evidence rather than months of manual design workshops. The system harvests structure, keys, cardinality, relationships, and query behaviour to infer domain entities and business keys, then generates optimized models for Bronze, Silver, and Gold.
Rationalisation cross-references lineage with actual usage to identify unused tables, unused jobs, unused reports, duplicates, and redundant objects. Performing rationalisation before conversion ensures organizations do not spend time and money migrating legacy clutter nobody actually uses.
A Gold domain model in DS Srishti is published with a named owner, versioned schema contract, automated quality SLA (freshness, completeness, accuracy), end-to-end lineage, certified KPI definitions, and catalog registration so business consumers can discover and depend on it without submitting tickets.

Proof of Impact

Real results with DS Srishti

Explore how enterprises have leveraged DS Srishti to accelerate their data modernization journey.

SAP HANA to Snowflake Migration: Achieving 93% Automation in Medical Technology
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SAP HANASnowflake

SAP HANA to Snowflake Migration: Achieving 93% Automation in Medical Technology

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Scripts
93%
Automation
DataStage to Databricks Automotive Migration: Achieving 75% Automation with DataSwitch
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DataStageDatabricks

DataStage to Databricks Automotive Migration: Achieving 75% Automation with DataSwitch

1500+
Assets
75%
Automation
Accelerating Media Insights: Reducing Data Processing Times by 85% with Redshift to Databricks
Media & Entertainment
RedshiftDatabricks

Accelerating Media Insights: Reducing Data Processing Times by 85% with Redshift to Databricks

100+
Assets
80%
Automation
Snowflake to Azure Synapse Migration: Automating 280+ Assets with DataSwitch
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SnowflakeAzure Synapse

Snowflake to Azure Synapse Migration: Automating 280+ Assets with DataSwitch

280+
Assets
85%
Automation
Automating Teradata BTEQ to PySpark: A Shipping Giant’s Databricks Migration Case Study
Shipping & Logistics
Teradata BTEQPySpark

Automating Teradata BTEQ to PySpark: A Shipping Giant’s Databricks Migration Case Study

400+
Assets
80%
Automation
Informatica to SnapLogic Migration: Achieving 80% Automation with DataSwitch
E-commerce
InformaticaSnapLogic

Informatica to SnapLogic Migration: Achieving 80% Automation with DataSwitch

80%
Automation
Significant
Cost Savings

See DS Srishti In Action

Start with an Assessment. Two to three weeks, fixed fee, and the report is yours regardless of what you decide next. It tells you what your estate actually contains, what it would cost to modernize, and what you can retire.