ETL Tools That Validate Before They Load, Not After
Most pipelines move data fast — and move the bad data with it. deKorvai extracts, transforms, and loads across SAP, Oracle, Snowflake, and any database, with profiling and validation built into the pipeline, so only clean, trusted data reaches the target.
every run monitored · every record audit-logged, source to target
ETL (Extract, Transform, Load) moves data from source systems into a target — extracting records, transforming them to fit the target model, and loading them. deKorvai adds profiling and validation inside the pipeline, so data quality issues are caught before load rather than discovered after.
It supports full and incremental extraction, reusable and DIY transformations, validation before load, and overwrite or append options — across SAP and non-SAP systems, orchestrated and monitored from one place.
Moving data fast is easy. Moving trusted data is not
Traditional pipelines extract and load efficiently, then leave you to discover the duplicates, gaps, and mapping errors downstream — in reports, broken integrations, or at go-live.
Quality is checked after the load
Most pipelines load first and validate later, so bad records reach the target and have to be found and unwound instead of stopped up front.
ETL, profiling, and validation live in separate tools
Stitching an extractor to a profiler to a validator means hand-offs, glue code, and no single view of a run from source to target.
Transformations are hand-coded and hard to reuse
Mapping logic written for one load rarely carries to the next, so every pipeline reinvents the same field, code, and structure mappings.
No audit trail from source to target
When a number looks wrong at the target, there's no record of what was extracted, transformed, or rejected — so reconciliation becomes guesswork.
A complete extract, transform & load engine
Everything a trusted pipeline needs — on one layer, not four stitched-together tools.
Rule-based extraction
Extract from SAP ECC, Oracle, SQL Server, and any relational source using conditions, filters, and selective rules.
Full & incremental loads
Choose full or delta extraction; incremental loads keep large datasets in sync without reprocessing everything.
Reusable & DIY transformations
Ready-to-use and do-it-yourself transformation functions — field, code, and structure mapping, all reusable across pipelines.
Validation before load
Pre-load validation checks records against target rules; invalid records are routed for correction instead of loaded blindly.
Overwrite & append options
Control how data lands in the target — overwrite existing records or append new ones, per load.
Built-in profiling
Profiling runs inside the pipeline, detecting duplicates, missing fields, and anomalies before transformation.
Workflow orchestration
Orchestrate multi-step pipelines with dependency management and a smart decision engine — not a pile of cron jobs.
Wide connectivity
Broad extraction and loading options across databases, files, SaaS, and ERP — SAP and non-SAP alike.
End-to-end monitoring & audit
Execution tracking, change logs, and a full audit trail for every record — from source to target.
Extract → Profile → Transform → Validate → Load
One repeatable workflow that replaces four disconnected tools.
Extract
Rule-based full & incremental extraction from SAP ECC, Oracle, SQL Server, any RDBMS.
Profile
Detect duplicates, gaps, and anomalies before transformation begins.
Transform
Reusable & DIY functions; field, code & structure mapping.
Validate
Pre-load checks against target rules — only clean data proceeds.
Load
Overwrite or append into target staging, monitored, with a full audit trail.
ETL vs ELT — and when each fits
| ETL — transform, then load | ELT — load, then transform | |
|---|---|---|
| Best for | Migrations, regulated data, quality gates before load | |
| Data quality | Enforced before the target is touched | |
| Typical target | Operational systems, ERP, migration targets | |
| deKorvai | Native — validate-before-load is the default |
Trusted pipelines, run and evidenced
Four patterns — each extracted, validated, and audited end to end.
Connect
Point at a source and define extraction rules and filters.
Choose mode
Full for a first load, incremental for changes since last run.
Extract
Pull only the records the rules select — no full reloads needed.
Evidence
Log what was extracted, when, and against which rules.
What a trusted pipeline is worth
Clean data at the target, first time
Validation before load means the target isn't polluted with records you'll have to find and unwind later.
Faster, more efficient loads
Reusable transformations and incremental extraction cut the manual effort and reprocessing that slow traditional pipelines.
One platform, less sprawl
Extraction, transformation, profiling, validation, and orchestration on one layer removes the cost of stitching separate tools.
Reconciliation you can defend
A full source-to-target audit trail means every number at the target can be traced back to what was extracted and transformed.
Repeatable, not reinvented
Reusable rules and templates make the next pipeline faster than the last, instead of starting from scratch each time.
Works across your whole estate
SAP and non-SAP, on-prem and cloud, databases and files — one engine instead of a different tool per source.
Where teams run deKorvai pipelines
Data migration & loading
Extract, transform, validate, and load legacy data into a target system — including programs like an SAP S/4HANA move — with clean records at cutover.
System consolidation
Extract from multiple systems and load into one target, with transformation and validation resolving conflicts along the way.
Incremental data sync
Keep a target in step with a source using delta extraction, moving only what changed since the last run.
Loading to cloud & warehouses
Move data into cloud platforms and warehouses, with the option to validate before load or transform in place.
Cross-system data movement
Move data between SAP, non-SAP, files, and SaaS through one engine instead of a tool per connection.
Feeding non-production
Extract and load data into non-production environments as part of the platform's test-data workflows.
From load-then-fix to validate-then-load
Traditional ETL
- →Load data, then discover the problems
- →Separate tools for extract, profile, and validate
- →Hand-coded transformations, rebuilt each time
- →Errors surface downstream, at reports or go-live
- →No trail from source to target
- →Full reloads even for small changes
With deKorvai
- ✓Validate before load — bad records never land
- ✓Extract, profile, transform, validate on one layer
- ✓Reusable transformation functions
- ✓Issues caught in the pipeline, before the target
- ✓Full source-to-target audit trail
- ✓Incremental loads move only what changed
The ETL engine inside deKorvai
The ETL Engine is one component of the deKorvai platform, working alongside the DQ Engine, Scrambler, and agentic AI (Agent Master) — connecting to your systems through standard protocols.
Documented integrations
SAP · SAP HANA · Oracle · Microsoft · Snowflake · AWS · Google Cloud · Salesforce · PostgreSQL. Explore Data Quality, Data Masking, and Agentic AI.
50,000+ records extracted, validated & loaded — 95% first-pass
A full extract-profile-transform-validate-load pipeline into target staging tables, with pre-load validation catching errors before they reached the target. (Delivered on an SAP migration, using the same engine that runs on any system.)
Extract from and load to any system
SAP and non-SAP, on-prem and cloud — connected through one engine. See all integrations →
ETL, answered
Part of one unified platform
ETL is one of four capabilities in deKorvai — explore the rest.
Stop unwinding bad data after the load
See how deKorvai extracts, transforms, validates, and loads — and what validate-before-load changes for your migrations and integrations.
Book a DemoA working session on your data pipelines