Technical Infrastructure

JarvisX Capabilities

Discover our supported stacks, dialect conversion coverage, and the 13-layer quality validation system across all 7 platform modules.

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JarvisX means Trust & Value

We do not just wrap AI APIs and call it code conversion. Every artifact passes through a 13-layer quality engine — from deterministic rewrites and dialect-specific cards to syntax validation and auto-repair. With 121 pair-specific conversion cards, 23 deterministic rewrite rules, and sqlglot-powered syntax validation across 15 dialects, JarvisX delivers conversion quality that is genuinely above market: deterministic transpile first, AI prompting only where needed, with per-artifact confidence scores so review tracks risk.

Modules

Conversion Modules

Powering enterprise databases, pipelines, risk auditing, and synthetic data generation.

query

JarvisQuery

SQL Conversion

61 pair-specific cards with function-level mappings. Converts complex SQL statements, procedural blocks, and analytical functions with deep semantic awareness.

Sources

TeradataOracleMySQLPostgreSQLSQL ServerHiveRedshiftSnowflake+ 5 more

Targets

BigQuerySnowflakeDatabricksRedshiftPostgreSQLHiveOracleSparkSQL ServerMySQLDuckDB

Any source × any target works. Pairs with dedicated cards get function-level guidance (DATEDIFF, QUALIFY, NVL, etc.).

flow

JarvisFlow

Workflow / ETL Conversion

42 pair-specific cards with component-level mappings. Translates visual ETL pipelines and schedules into modern code-defined orchestrators.

Sources

TalendInformaticaSSISDataStageADFOozieControl-MAutosys+ 8 more

Targets

Databricks NotebookDatabricks JobsDatabricks DLTAirflowDagsterPrefectdbtADFSSISInformaticaTalendDataStageStep FunctionsBigQuerySnowflakeRedshiftJSONXMLYAML

Talend → Databricks has 30+ component mappings (tMap, tDBInput, tRunJob, tJava, etc.).

code

JarvisCode

Script / Language Conversion

Cross-language conversion with framework awareness. Safely migrates custom business logic scripts, command-line utilities, and backend routines.

Sources

PythonJavaScalaJavaScriptTypeScriptC#GoPL/SQL+ 6 more

Targets

PythonJavaScalaJavaScriptTypeScriptGoC#KotlinRustRubyPHPDatabricks (PySpark)BigQuery SQLSnowflake SQLPostgreSQL

Highlighted pairs: Python → PySpark, Java → Python, Scala → Python, BTEQ → Python, Shell → Python.

schema

JarvisSchema

DDL Rewrite

Schema conversion with type mapping and constraint preservation. Automatically transforms physical model properties and constraints.

Sources

TeradataOracleMySQLPostgreSQLSQL ServerHiveRedshiftBigQuery+ 3 more

Targets

Same as sources + dialect specific optimizations

Handles: CREATE TABLE, INDEX, VIEW, constraints (PK/FK/CHECK), partitioning, identity columns, and computed columns.

risk

JarvisRisk

Migration Risk Assessment

Source-first risk analysis with optional gap analysis. Identifies migration blockers and calculates complexity scores before writing code.

Sources

Complexity scoringVendor lock-in detectionPerformance risksData precisionDate/time risksSecurity flagsTransaction modelSchema compatibility+ 1 more

30+ risk patterns detected. 13 hard-feature detectors. Per-artifact confidence scoring with manual review flags.

graph

JarvisGraph

Lineage & Impact

Visual lineage with risk overlay. Maps dependencies across tables, schemas, jobs, and individual queries.

Sources

Cross-system dependenciesUpstream / DownstreamInteractive visualizationRisk-flagged columns

Generates interactive node graphs. Allows SVG / JSON / ZIP export for reporting and analysis.

JarvisData

Synthetic Test Data Generation

AI-powered sample data generation directly from your DDL definitions. Supports BigQuery, Snowflake, Databricks, and PostgreSQL targets. Configures basic, realistic, and AI-enhanced distributions (1K, 10K, or 100K rows per table) for offline workflow verification.

BigQuerySnowflakeDatabricksPostgreSQL

Validation Pipeline

13-Layer Quality Engine

Every conversion runs through these deterministic and adaptive checks to guarantee code safety.

sqlglot deterministic transpilation

15 dialects supported natively

Pre-LLM deterministic rewrites

23 strict transformation rules

Complexity-adaptive prompting

Saves LLM tokens & ensures high fidelity

Feature detection + micro-cards

Context isolation per code segment

121 pair-specific conversion cards

Deep function-level translation recipes

RAG retrieval (HyDE + RRF reranking)

Matches semantic search patterns

LLM conversion (Advanced AI Agent)

Validates structure and flow

Post-LLM deterministic rewrites

Applies post-processing schema mappings

Syntax validation (sqlglot)

Validates AST parser compliance

Category-specific validation

Validates query structures and data models

Auto-repair & reconversion

Capped LLM fixes for failed, empty or placeholder outputs

Live execution validation (DuckDB)

Runs the converted SQL on sample data & diffs rows vs source — self-repairs on failure

Semantic scoring + confidence flags

Sampled logical-parity scoring; surfaces high-risk scripts for human audit

Syntax Matrix

Featured Conversion Pairs

Deepest functional mapping support. Other pairs execute via foundational rules + intelligent translation models.

JarvisQuery

61 pairs

Teradata → BigQuery ★

Teradata → Snowflake ★

Teradata → Redshift

Teradata → Spark

Teradata → PostgreSQL

Oracle → BigQuery ★

Oracle → Snowflake

Oracle → PostgreSQL

Oracle → Redshift

Oracle → Spark

MySQL → BigQuery

MySQL → Snowflake

MySQL → Redshift

MySQL → PostgreSQL

MySQL → Spark

MySQL → DuckDB

PostgreSQL → BigQuery

PostgreSQL → Snowflake

PostgreSQL → Redshift

SQL Server → BigQuery

SQL Server → Snowflake

SQL Server → PostgreSQL

Hive → BigQuery

Hive → Spark

Redshift → BigQuery

Redshift → Snowflake

Snowflake → BigQuery

BigQuery → Snowflake

Spark → BigQuery

Druid → BigQuery

+ 31 more pairs supported

JarvisFlow

42 pairs

Execution-proven, not just name-faithful

We compile your source's logic to SQL, translate the converted DAG back to SQL, and diff both on a synthetic fixture — credential-free. Try the Live Flow Validator →

Talend → Databricks Notebook ★

Talend → Airflow ★

Talend → Databricks Jobs

Talend → dbt

Talend → ADF

Informatica → Databricks ★

Informatica → Airflow

SSIS → Databricks Notebook ★

SSIS → Airflow

DataStage → Databricks

Airflow → Databricks Jobs

Airflow → Dagster

Airflow → Prefect

ADF → Airflow

ADF → Databricks DLT

Oozie → Airflow

Control-M → Airflow

Autosys → Airflow

DataStage → Databricks DLT

dbt → BigQuery

dbt → Snowflake

+ 9 more pairs supported

JarvisCode

80 pairs

High Confidence Pairs:

Python → PySpark ★

Java → Python ★

Scala → Python ★

BTEQ → Python ★

Shell → Python ★

C# → Python

JavaScript → TypeScript

TypeScript → JavaScript

Scope:

10 Source Languages

8 Target Languages

80 total possible combinations supported via compiler rules + micro-context LLM translation

121

Pair-Specific Cards

11+

SQL Dialects

ETL/Workflow Tools

Languages

Quality Layers

Rewrite Rules