Skip to content
totusphere

ETL & Data Integration

Integration that keeps up with your business.

Legacy ETL tools hold years of business logic, but they are expensive to license, hard to change and slow to scale. We integrate your applications and modernize ETL into code-first ELT on your cloud platform, carrying the logic across safely and proving every number matches.

Discuss your projectAll services

Sound familiar?

The problems we solve

  • Costly legacy licenses

    Per-core ETL licenses and dedicated servers that grow faster than the value they deliver.

  • Logic locked in tools

    Thousands of mappings and stored procedures with little documentation and few people who understand them.

  • Long change cycles

    Simple changes take weeks because of tightly coupled jobs and manual deployment.

  • Point-to-point sprawl

    Applications wired directly to each other, so one change breaks three integrations.

Outcomes

What you get

  • Legacy ETL licenses and servers retired on a planned schedule
  • Business logic documented, tested and version-controlled
  • Changes released in days, through CI/CD
  • One governed integration layer instead of point-to-point links

Approach

How we deliver it

  1. 1

    Inventory and analyze

    Automated scanning of existing jobs, mappings and dependencies to size and sequence the work.

  2. 2

    Convert with accelerators

    Pattern-based conversion of mappings into dbt, Spark or SQL, with engineers reviewing every workflow.

  3. 3

    Reconcile in parallel

    Old and new pipelines run side by side with automated row- and aggregate-level comparison.

  4. 4

    Cut over and decommission

    Workflow-by-workflow switch with rollback plans, then retire the legacy jobs.

Deliverables

What we hand over

Working software, tests and documentation in your repositories and cloud accounts.

Technologies

  • Informatica
  • SSIS
  • IBM DataStage
  • Talend
  • dbt
  • Azure Data Factory
  • AWS Glue
  • Apache Kafka
  • Fivetran
  • Airbyte
ETL estate assessment
Complexity scoring, dependency graph and a sequenced modernization plan with effort estimates.
Converted pipelines
Code-first ELT in dbt, Spark or SQL with tests and documentation for every model.
Integration layer
APIs, event streams and managed connectors replacing point-to-point integrations.
Reconciliation framework
Automated data comparison reports signed off by business owners before cutover.
CI/CD pipeline
Automated testing and deployment so changes ship safely and often.

Use cases

Where this makes a difference

  • Informatica PowerCenter to dbt + Snowflake

    Hundreds of mappings re-implemented as tested SQL models on a cloud warehouse.

  • SSIS to Azure Data Factory and Fabric

    On-premises SQL Server packages moved to managed, monitored cloud pipelines.

  • Application integration hub

    ERP, CRM and e-commerce systems connected through events and APIs instead of nightly file drops.

ETL modernization & big data migration

Leave legacy platforms without the big-bang risk.

Hadoop clusters, appliance warehouses and legacy ETL tools hold years of business logic. We move them to a modern lakehouse in planned waves, reconciling every number before anything is switched off.

From

  • Cloudera / Hadoop
  • Teradata
  • Netezza
  • Oracle Exadata
  • Informatica
  • SSIS
  • IBM DataStage
  • Talend

To

  • Databricks
  • Snowflake
  • Microsoft Fabric
  • Google BigQuery
Big data migrationETL modernization
  1. 01AssessInventory data, jobs and dependencies; build the business case.
  2. 02Plan wavesGroup workloads into low-risk waves with clear success criteria.
  3. 03ConvertMove data, convert ETL and SQL, map security and access.
  4. 04Validate in parallelRun old and new side by side; reconcile every number.
  5. 05Cut over and retireSwitch consumers with rollback plans, then decommission.

FAQ

Common questions

ETL or ELT?

On modern cloud platforms we usually recommend ELT: load first, transform inside the platform with tested SQL or Spark. Some cases, such as masking sensitive data before it lands, still call for transformation in flight.

Can conversion be automated?

Partly. Accelerators handle repeatable patterns quickly, but engineers review and test every workflow. Fully automatic conversion tends to copy legacy problems into the new platform.

How do we know the numbers still match?

Old and new pipelines run in parallel and an automated reconciliation compares outputs at row and aggregate level. Business owners sign off before each cutover.

Related services

All services →

Tell us where your data is today.

A 45-minute conversation with a senior engineer. You leave with an honest view of what to build first, whether or not we work together.

Book a consultationExplore services