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Publicly listed on BSE · ISO 27001 & SOC 2 Type II certified
Publicly listed on BSE · ISO 27001 & SOC 2 Type II certified
Alloy replaces repeated low-level pipeline setup with a reusable,
configurable framework for batch data engineering.
Alloy gives Databricks teams a reusable starting point for batch-oriented source onboarding. It standardizes common ingestion, technical lineage, data-quality, and Medallion-layer mechanics while keeping source-specific transformations and business rules visible to developers.
Custom pipeline development creates compounding problems, not just the first time, but every time a new source arrives.
Instead of onboarding data, your team is rewriting ingestion logic, rebuilding validations, retesting transformations, and maintaining yet another pipeline.
Records disappear without explanation, schema changes go unnoticed, and executives discover the issue before engineering does.
Alloy replaces one-off pipeline development with a pre-built, configurable framework. It uses the same quality logic, audit trail, and governance layer - applied to every source you onboard.
New source requires repeated ingestion and layer setupNew source starts from reusable components, configuration, and targeted customization
Validation and rejection logic varies across pipelinesConfigurable Silver rules route invalid records with documented reasons
Technical metadata depends on individual implementationStandard technical lineage columns are applied across supported onboarding flows
Our engineers work in an AI-assisted development environment that helps them move faster, make fewer mistakes, and deliver more value per sprint.
Ingestion via PySpark batch reads with configurable source detection and validation.
Validation via configurable quality rules with quarantine tables and rejection tracking.
Secures rows and columns automatically while mapping end-to-end data lineage.
Manages task dependencies, retries, and schedules natively using the standard Jobs API.
Guarantees ACID transactions, point-in-time time travel, and high-performance querying across layers.
Captures execution history, validation results, row counts, rejected records, and runtime events by default.
Alloy is designed for scheduled or manually initiated batch processing on Databricks. It supports repeatable ingestion, technical lineage, repeat-safe Bronze loading, Silver transformation and quality routing, and client-specific Gold aggregation.
Scheduled data ingestion
Compliance and audit requirements
Analytics and reporting pipelinesMulti-source enterprise data onboardingEvery component is pre-built and configurable. Your team defines the rules, and Alloy applies them consistently while tracking quality, audit, and pipeline execution across every data source.
Instead of spending weeks rebuilding ingestion logic, configure new sources using a standardized framework.
Every transformation is traceable from executive KPI back to raw source records.
Built by Databricks engineers with experience designing enterprise-grade data platforms for regulated and data-intensive organizations.
Validate Alloy inside your own Databricks environment before rolling it out across additional pipelines.
Week 1
Week 1Built on Databricks engineering best practices, Alloy helps organizations create consistent, governed, and traceable batch ETL pipelines without repeatedly building custom frameworks from scratch.
Tracks every transformation from raw ingestion to business-ready datasets, giving businesses complete visibility into how every report, KPI, and metric is produced.
Automatically captures execution history, validation results, row counts, and runtime events to simplify compliance, troubleshooting, and operational monitoring.
Leverages Unity Catalog integration, standardized quality rules, and controlled data access to maintain consistent governance across every pipeline.
Built on the Databricks Medallion Architecture with reusable, configurable components that let you onboard new data sources without repeatedly building and maintaining custom ETL pipelines.
Tracks every transformation from raw ingestion to business-ready datasets, giving businesses complete visibility into how every report, KPI, and metric is produced.
Automatically captures execution history, validation results, row counts, and runtime events to simplify compliance, troubleshooting, and operational monitoring.
Logistics
Healthcare
Retail and E-commerce
Financial Services
In their words
They're very willing to assemble the team that we ask for if we have certain preferences.
Beneficial Ownership Registry
I was impressed at the speed, cost, and talent that they have at Capital Numbers.
CEO, engageSimply
Without built-in lineage, compliance reviews and root-cause investigations become manual and time-consuming.
Reruns can duplicate previously published recordsDeterministic record hashing supports repeat-safe Bronze reruns
Common Spark mechanics are rebuilt repeatedlyReusable functions standardize common ingestion, transformation, quality, and aggregation mechanics
Bronze-to-Gold transformations
Bad records are quarantined before they reach downstream systems, helping your team spend less time investigating production issues and more time delivering new data capabilities.
Seamlessly connect to cloud storage (S3, ADLS, GCS), APIs, and internal data catalogs without custom integration logic
Automated audit logs track execution history, row counts, and runtime errors, while built-in monitoring instantly flags anomalies to eliminate operational overhead.
Once proven, the exact same foundation seamlessly handles your next several data sources through configuration and light customization instead of repeated ground-up development.
Raw data captured
Automatically validated
Trusted business metrics
Tracks every transformation from raw ingestion to business-ready datasets, giving businesses complete visibility into how every report, KPI, and metric is produced.
Automatically captures execution history, validation results, row counts, and runtime events to simplify compliance, troubleshooting, and operational monitoring.
Leverages Unity Catalog integration, standardized quality rules, and controlled data access to maintain consistent governance across every pipeline.
Built on the Databricks Medallion Architecture with reusable, configurable components that let you onboard new data sources without repeatedly building and maintaining custom ETL pipelines.
Manufacturing
Media & Entertainment
A three-layer data pipeline design pattern native to Databricks. Raw data lands in Bronze, gets cleaned and validated in Silver, and is shaped into reporting-ready outputs in Gold. Each layer adds a defined level of structure and quality, so only trusted data reaches downstream consumers.
A custom pipeline is written from scratch for each data source, meaning new code, new testing, new support burden every time. The accelerator is a pre-built framework you configure per source. The ingestion logic, quality engine, audit logging, and monitoring are already built. You define the rules; the framework runs them.
Alloy supports AWS, Azure, and Google cloud storage files (CSV, JSON, Parquet), REST APIs, JDBC databases, and Databricks Unity Catalog tables. Format detection and schema handling are configurable per source.
Records that fail data quality checks are routed to a dedicated rejected records table, and not discarded. Each rejected record carries a reason code and batch reference. A data quality summary gives a run-level view of all pass and fail counts, making it easy to investigate, remediate, and reprocess.
Yes. The framework is built natively on Databricks Delta Lake for storage and Unity Catalog for governance, including catalogues, schemas, permissions, and lineage. It integrates directly into your existing Unity Catalog environment.
The accelerator is configurable by design. Source paths, target tables, quality rules, schema definitions, alerting thresholds, and schedules are all set through pipeline configuration, and not hardcoded. The framework handles common logic; your team defines the rules specific to each source.
They invest in the success of their clients which makes them flexible in accomodating the needs of growing companies.
Managing Partner, Consensus Interactive
They're very willing to assemble the team that we ask for if we have certain preferences.
Beneficial Ownership Registry
I was impressed at the speed, cost, and talent that they have at Capital Numbers.
CEO, engageSimply
They invest in the success of their clients which makes them flexible in accomodating the needs of growing companies.
Managing Partner, Consensus Interactive
They're very willing to assemble the team that we ask for if we have certain preferences.
Beneficial Ownership Registry
I was impressed at the speed, cost, and talent that they have at Capital Numbers.
CEO, engageSimply
They invest in the success of their clients which makes them flexible in accomodating the needs of growing companies.
Managing Partner, Consensus Interactive
They're very willing to assemble the team that we ask for if we have certain preferences.
Beneficial Ownership Registry
I was impressed at the speed, cost, and talent that they have at Capital Numbers.
CEO, engageSimply
They invest in the success of their clients which makes them flexible in accomodating the needs of growing companies.
Managing Partner, Consensus Interactive
Trusted By Global Brands, Growth Companies, And Enterprise Teams
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