DATA WAREHOUSE MODERNIZATION
Move the warehouse. Modernize the workloads around it.
Datachecks helps retire legacy analytical warehouses by migrating the data, SQL workloads, ETL, utilities, orchestration and downstream dependencies required to run them on Snowflake or Databricks.
LEGACY WAREHOUSE ESTATE
Teradata / Netezza / Redshift / Synapse
SQL Workloads
ETL
Load Utilities
Schedulers
Historical Data
Reporting
Dependencies
DATACHECKS
SNOWFLAKE / DATABRICKS
WHEN THIS SERVICE FITS
When the warehouse has become the constraint.
01
Legacy platform cost
The warehouse is expensive to operate, scale or maintain.
02
Modernization pressure
Teams need cloud-native analytics, elasticity or broader data-platform capabilities.
03
Migration complexity
SQL, ETL, utilities and dependencies make the program much larger than table movement.
04
End-of-life or consolidation
The organization needs to retire, consolidate or replace an aging analytical platform.
WHAT ACTUALLY GETS MODERNIZED
The warehouse is more than tables.
THE WAREHOUSE ESTATE
12 AREAS MODERNIZED
01
Tables and views
02
Historical data
03
SQL workloads
04
Stored procedures and macros
05
Load / unload utilities
06
ETL and ELT
07
Schedulers and orchestration
08
Data-quality logic
09
Reporting and BI dependencies
10
Security and access patterns
11
Validation and reconciliation
12
Cutover and retirement
A warehouse migration is complete only when the legacy analytical estate can be safely retired.
PRIMARY MODERNIZATION SCENARIOS
Common warehouse modernization paths.
Teradata → Snowflake
Modernize Teradata SQL, BTEQ, macros, load utilities, ETL and reporting workloads into Snowflake.
Explore Teradata → Snowflake
Teradata → Databricks
Move Teradata warehouse workloads and surrounding analytics processes into a Databricks-native architecture.
Explore Teradata → Databricks
Netezza → Snowflake
Retire Netezza analytical workloads and modernize SQL, ETL and historical data into Snowflake.
Discuss This Migration
Synapse → Databricks
Move Synapse analytical workloads, pipelines and dependent reporting into Databricks.
Discuss This Migration
Also supporting Redshift, Hadoop analytics and other warehouse modernization scenarios
LEGACY WORKLOAD → TARGET PATTERN
Modernize the workload—not just the syntax.
LEGACY WAREHOUSE
MODERN TARGET
Tables / Views
Target tables / views
SQL Workloads
Target-native SQL
Procedures / Macros
SQL / Python / target-native logic
Load Utilities
Native ingestion
ETL
Native pipelines / dbt / modern ELT
Schedulers
Target-native or external orchestration
Historical Workloads
Modern storage / compute pattern
BI Dependencies
Target analytics architecture
CONVERT
MODERNIZE
RETIRE
Not every legacy workload should be reproduced one-to-one. Datachecks identifies what should be converted, redesigned or retired.
HOW DATACHECKS DELIVERS
From warehouse discovery to production cutover.
01
Assess
02
Discover
03
Map
04
Translate
05
Migrate
06
Validate
07
Reconcile
08
Cutover
AI Agents
Warehouse discovery, translation and reconciliation at estate scale.
Native Migration Tools
Snowflake and Databricks capabilities used where they are strongest.
Migration Experts
Architecture, exceptions and every sign-off.
Migration Evidence
Object-level proof connected across the lifecycle.
Automation at scale. Human judgment where it matters.
NATIVE TOOLS + DATACHECKS
Use platform-native migration capabilities where they are strongest.
Datachecks works with Snowflake and Databricks native migration capabilities and adds whole-estate discovery, semantic decisions, exception handling, business validation and migration evidence around them.
NATIVE MIGRATION TOOLS
Conversion
Data movement
Platform execution
+
DATACHECKS
Dependencies
Semantic mapping
Exceptions
Business equivalence
Migration Evidence
+
MIGRATION EXPERTS
Architecture
Review
Critical decisions
PRODUCTION-READY WAREHOUSE
We do not rebuild what the destination platform already does well.
DIFFICULT WAREHOUSE MIGRATION PATTERNS
The difficult work is usually in the workloads around the warehouse.
Complex stored procedures and macros
BTEQ / proprietary utilities
ETL redesign
Distribution / partitioning assumptions
Historical workload migration
Scheduler dependencies
Source-specific performance patterns
BI and reporting dependencies
Data reconciliation
Business-rule equivalence
Repeatable work is automated. Complex patterns become explicit exceptions with owners and evidence.
MIGRATION EVIDENCE
Prove the modernized warehouse is equivalent.
Datachecks connects source workloads, mappings, translated artifacts, exceptions, validation results and reconciliation evidence across the migration lifecycle.
Explore Migration Evidence
WORKLOAD PASSPORT
SALES_DAILY_AGG
SOURCE
Teradata
TARGET
Snowflake
Mapping
APPROVED
Translation
COMPLETE
Exceptions
2 / 2 RESOLVED
Validation
48 / 48 PASSED
Reconciliation
MATCHED
STATUS
READY
WHAT THE CUSTOMER RECEIVES
What the modernization delivers.
Warehouse Inventory
Data, workloads and dependencies in scope.
Workload Classification
Convert / modernize / redesign / retire.
Target Mapping
Approved source-to-target architecture.
Modernized Artifacts
Target-native SQL, pipelines and implementation outputs.
Validation & Reconciliation Evidence
Proof of technical and business equivalence.
Cutover Readiness Pack
Evidence and unresolved blockers for production transition.
PART OF DATA ESTATE MIGRATION
Data Warehouse Modernization is one type of Data Estate Migration.
When the analytical warehouse is the center of gravity, this service focuses the broader Datachecks migration model on warehouse workloads, ETL, reporting dependencies and legacy retirement.
Planning to retire a legacy warehouse?
Start with the source platform, workload estate and target destination. Datachecks can assess the complexity and take the migration through production readiness.