Transformations / data warehouse migration
Get the Answers Before the Migration Finishes
One ontology over your old warehouse and your new one. The business gets answers across both today — and the move happens table by table, only where it pays.
Databricks lakehouseThe Migration, Before and After
- Years of program before the first new answer
- Business logic rewritten by hand in a new dialect
- Two warehouses in parallel, reconciled in spreadsheets
- Integrators paid for every month the project runs
- Answers across old and new systems from the start
- SQL translated between dialects, checked by tests
- Parity monitored continuously as tables move
- The order of the move set by cost and usage
Move What Pays, in the Order It Pays
Every table is scored by what it costs to run and who reads it. The plan falls out of the usage — most of the estate turns out not to be worth moving at all.
- Query cost and readers measured per table
- Lineage shows what breaks if a table moves
- Unread tables retired, not migrated
Every Object Proven Before Anything Repoints
Views, procedures and ABAP-backed reports are translated between dialects and run on both engines. When a total differs, the cause is found and fixed before a single dashboard moves.
- Teradata, Oracle and ABAP into Spark, Snowflake or Postgres SQL
- Parity tests on every translated object
- Metrics repoint behind the ontology — consumers never notice
- client
- profile
- software services · san francisco, ca
- estate
- erp · warehouses · bi · fp&a models
- status
- live · nothing migrated
Offered a year-long migration. Live in under a week.
We looked at a number of different solutions. We found TextQL to be the best.
Answers First, Move Later
The order is the point: the business is answered in the first step, and every step after it is sized by what it saves.
- 01 / connect Answer Across Both Old and new engines read in place under one ontology. No rows copied, answers from week one. nothing moves
- 02 / rank Let Usage Decide Cost, readers and lineage set the order of the move — and what never moves at all. the plan
- 03 / move Table by Table Each object translated, proven on both engines, then repointed behind the ontology. no big bang
Map Your Migration Before You Fund It
Connect the warehouses you already run. We rank the estate by cost, usage and lineage, and show your team what is worth moving — and what the business can have answered today.
- 01A 30-minute call to list the engines and the move on the table
- 02We connect in place inside your cloud — nothing is copied
- 03Your team gets a ranked plan and live answers across both
Moves Teams Are Making
- 01 Teradata to Databricks or Snowflake
Answer across both while BTEQ, views and procedures are translated and proven, table by table.
- 02 Oracle retirement
PL/SQL and finance marts translated with parity tests, so the ledger reconciles on both sides before the switch.
- 03 SAP HANA and BW reporting offload
ABAP and CDS-view logic translated into the lakehouse, so reporting stops depending on the ERP’s own database.
- 04 Estates after an acquisition
Two companies, two warehouses, one set of definitions — answered together long before anyone consolidates.
- 05 Hadoop decommissioning
Find what is still read, move that, and switch the cluster off with the lineage to prove nothing broke.
- 06 Cost-driven workload moves
Move only the workloads whose cost justifies it, ranked by what each query actually spends.
It changes what they are needed for. The ontology gives the business answers across both systems from the start, and translation with parity tests handles much of the mechanical work — so the program is sized by what is worth moving.