Datai Analytics
Products

Accelerators that compress weeks into days.

Battle-tested frameworks and tooling we plug into client engagements — six categories of accelerators built for the platforms our team works in every day.

— Accelerator

Databricks Accelerators

Pre-built foundations for Databricks lakehouse implementations — ingestion, transformation, governance, and observability scaffolded to opinionated standards. Skip the boilerplate; start delivering business logic in week one.

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  • Auto Loader and ingestion frameworks for common source patterns
  • Medallion architecture templates (bronze / silver / gold)
  • Unity Catalog setup and access governance scaffolding
  • Delta Live Tables reference pipelines
  • Performance tuning playbooks (Z-order, liquid clustering, photon)
— Accelerator

Code Engines

Code-generation engines that turn metadata and configuration into production-ready Python, SQL, and dbt code. Reduce the surface area engineers maintain by hand without sacrificing review-ability.

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  • Configuration-driven pipeline generators
  • Schema-to-DDL and schema-to-dbt-model emitters
  • Templated transformation libraries with built-in quality checks
  • Source-system adapter generators (REST APIs, JDBC, file feeds)
  • Self-serve analyst tooling that emits reviewable SQL
— Accelerator

DevOps Accelerators

The CI/CD rails that let data teams ship like software teams. Versioned, tested, observed — and ready to run on day one.

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  • GitHub Actions and Azure DevOps pipeline templates
  • Terraform modules for Databricks, Snowflake, AWS, Azure, and GCP
  • Databricks Asset Bundle scaffolding
  • Automated testing harnesses for SQL, Python, and dbt projects
  • Pre-commit hooks and code-quality gates
— Accelerator

Deployment Accelerators

Environment provisioning, schema migration, and release management tooling — the unglamorous infrastructure that makes safe, repeatable deploys boring.

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  • Multi-environment promotion (dev → staging → prod)
  • Schema change management (Flyway, schemachange, Liquibase)
  • Blue/green deploy templates for Databricks workflows
  • Snowflake account-level deployment automation
  • Rollback-safe release patterns for production data pipelines
— Accelerator

AI / ML Accelerators

The reusable scaffolding that turns ML prototypes into production systems — feature stores, registries, monitoring, and starter kits for the use cases we see most.

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  • MLflow tracking and model registry boilerplate
  • Feature engineering and feature-store templates
  • Model monitoring, drift detection, and retraining workflows
  • RAG and LLM starter kits using Databricks Mosaic AI
  • Forecasting and classification reference pipelines
— Accelerator

Reporting Accelerators

Pre-built semantic layers, dashboard templates, and BI scaffolding for the tools that actually deliver the answer to the business.

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  • Power BI dataset and report templates
  • Tableau workbook scaffolding for common analytics patterns
  • Databricks SQL dashboard libraries
  • Semantic-layer definitions with dbt and Cube
  • Self-serve analyst enablement materials
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