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PEOPLE / SYSTEMS / OUTCOMES

Build around the people who know the work.

All technical guides

FOCUSED ENGINEERING GUIDE

Move legacy SQL workloads to a modern stack without rewriting everything by hand.

Inventory, transform and validate stored procedures, scripts and embedded business logic in controlled workload groups.

Review a legacy SQL workload
FORData platform leader, CTO, modernization owner, analytics engineering lead
TRIGGERDatabase cost, platform retirement or a cloud data program requires legacy workloads to move

WHAT THIS ENGAGEMENT COVERS

Legacy SQL Modernization Services

Legacy SQL estates often contain years of business logic spread across stored procedures, scripts, schedulers and reporting jobs. A line-by-line rewrite is slow, while automated syntax conversion alone cannot prove that the new workload behaves like the old one.

Ariftly modernizes SQL in controlled workload groups. We map dependencies, classify repeatable patterns, accelerate suitable conversions and isolate the code that needs engineering judgment. Each wave is checked against representative source outputs so progress is measured in verified behavior, not converted files.

THE PROBLEM

The difficult part is rarely the happy path.

We start with the dependencies, exceptions and verification burden that make this work risky.

  1. 01

    Thousands of stored procedures and scripts

  2. 02

    Undocumented dependencies between workloads

  3. 03

    Dialect-specific behavior and unsupported constructs

  4. 04

    Business logic embedded deep inside SQL

  5. 05

    Manual conversion with weak regression evidence

  6. 06

    Uncertainty about equivalent output

EXPECTED INPUTS

What we work from.

  • SQL repository and stored procedures
  • DDL, schemas and dependency metadata
  • Representative data or safe fixtures
  • Current reference outputs
  • Target platform and engineering conventions

DELIVERABLES

What you receive.

  • Dependency and workload inventory
  • Pattern classification and migration backlog
  • Converted SQL or dbt models
  • Transformation rules and documented exceptions
  • Automated validation harness
  • Source-versus-target reconciliation

DELIVERY PATH

A controlled path
to a verified result.

  1. 01

    Inventory

  2. 02

    Classify

  3. 03

    Transform

  4. 04

    Compare

  5. 05

    Deploy

AI + DETERMINISTIC ENGINEERING

Move faster.
Verify the result.

01 / ACCELERATE

AI-assisted engineering

AI accelerates dependency analysis, pattern classification and first-pass conversion across repetitive workload families.

02 / VERIFY

Deterministic controls

Parsing, tests, row and aggregate comparisons, invariant checks and engineering review verify behavior before release.

01

Accelerate repetitive work

AI assists analysis, classification, mapping and transformation where it can reduce manual effort.

02

Return inspectable artifacts

Your team receives code, mappings, tests, evidence and documented exceptions rather than a black box.

03

Fit the existing environment

We work with the systems, controls and deployment boundaries that already run the business.

04

Keep critical decisions human

Material changes and unresolved exceptions remain subject to accountable engineering review.

REFERENCE SCENARIO

Reference scenario: one legacy SQL workload group

Illustrative classification for planning a bounded modernization wave. Counts are omitted because no customer result is being claimed.

ARIFTLY / REVIEW PACKAGE
01 Dependency graph and workload groups
02 Direct-conversion candidates
03 Transformation-required queue
04 Engineering-review exceptions
05 Output-equivalence report

ENGAGEMENT MODEL

Start bounded.
Build from evidence.

  1. 01

    Assessment

    One bounded workload, system or partner flow.

  2. 02

    Production work

    Implement, integrate and validate the agreed scope.

  3. 03

    Ongoing operation

    Monitor, support or extend where the operating need justifies it.

  4. 04

    Reusable automation

    Turn proven repeated patterns into reusable engineering capability.

QUESTIONS

Do you support PL/SQL and stored procedures?

Yes. Support depends on the constructs and target platform, which we classify during the initial workload assessment.

How do you prove output equivalence?

We compare representative source and target outputs using agreed row, aggregate and business-invariant checks, then document unresolved differences.

Do you need production data?

Not necessarily. Sanitized representative datasets or generated fixtures can support assessment and much of the validation design.

Can your team work in our environment?

Yes. Delivery can be adapted to your repository, cloud boundary, CI process and security requirements.

Do you convert everything automatically?

No. Repeatable patterns can be accelerated, while unsupported constructs and material business logic are isolated for engineering review.

DISCUSS THE REAL PROJECT

Bring the system.
We’ll map the path.

Share a representative workload, specification or architecture. Sensitive production access is not needed for the first conversation.

Review a legacy SQL workload