Introducing Databricks GenAI Partner Accelerators for Data Engineering & Migration

Enterprises face increasing pressure to modernize their data stacks. Teams need to move away from legacy ETL systems and complex on-premises platforms and shift toward simpler, scalable architectures. Many organizations still rely on manual code conversion, fragmented data pipelines, and time-consuming validation steps. These slow migration timelines make it harder to adopt AI.

Partner-built GenAI accelerators now help remove this overhead. Databricks partners use Agent Bricks to build AI agents that generate SQL and Python code, validate pipeline logic, and suggest improvements. These agents read existing workloads and produce schema mappings, migration scripts, and optimized pipelines that run on the Databricks Data Intelligence Platform. This gives engineers a faster path to parity and lets teams focus on architecture instead of repetitive operational work.

This blog highlights two categories of partner solutions.

1. GenAI Accelerators for Data Engineering: These accelerators automate common data engineering tasks. Partners built systems that read source data, create pipeline scaffolding, generate transformation logic, and validate data quality. Some support natural language prompts so analysts and engineers can describe tasks in plain language. The goal is simple. Reduce the time needed to build and maintain pipelines and improve consistency across teams.
2. GenAI Accelerators for Data and Platform Migration: These solutions support customers moving from legacy ETL and data warehouse tools. Accelerators parse existing jobs, identify dependencies, convert code to Databricks, and validate outputs. They help teams migrate faster, reduce hand-built conversions, and maintain accuracy during large transitions.

More than twenty partners deliver these solutions. Current partners include Blend360, Blueprint, Celebal Technologies, Cognizant, Elastacloud, Entrada, EXL, EY, Hexaware, Indicium, Infogain, Infosys, Insight, Koantek, LTIMindtree, Persistent Systems, Shorthills, Slalom, TCS, Tiger Analytics, Wipro, Xebia, zeb, and Zensar.

Partner accelerators give teams a practical way to modernize at scale. They also help organizations start using GenAI in parts of the data lifecycle that benefit most from automation. With Databricks and our partner ecosystem, enterprises gain a unified platform and a growing set of AI-driven tools that shorten delivery time and improve engineering outcomes.

GenAI Accelerators for Data Engineering

Databricks GenAI Partner Accelerators for Data Engineering empower organizations to modernize and scale their data operations with speed and intelligence. Leading Databricks partners have built these accelerators using Databricks AI and Agent Bricks that combine advanced generative AI capabilities with proven data engineering frameworks to automate complex tasks such as data ingestion, transformation, and pipeline optimization. These accelerators support natural language interfaces, further simplifying the work for personas such as data analysts, data engineers and data scientists. By leveraging AI-driven insights and prebuilt solution templates, enterprises can reduce development cycles, ensure data quality, and accelerate time-to-value across their modern data stacks. These accelerators represent a new era of intelligent data engineering where automation meets innovation- enabling teams to focus on outcomes instead of operations. The following partner offerings help with data engineering tasks such as building and modifying data pipelines, data modeling, performing data transformations and validating data quality:

• Blend360 Trellis IQ
• Blueprint Lakehouse Optimizer
• Celebal Technologies Eagle Eye - Data Observability Accelerator
• Elastacloud Chat QnA
• EXL EXLdata.ai
• EY Data Fusion
• Infosys DE.AI
• LTIMindtree SSIS to PySpark Migration
• Persistent Systems iAURA Data Observability
• Persistent Systems iAURA Data Modeler & Mapper
• Slalom LakeSpeak - An MCP Client with Genie Support
• TCS Agentic Ops
• Tiger Analytics Intelligent Data Express (IDX)
• Tiger Analytics iDEA (Intelligent Data Engineering Agent)
• Tiger Analytics Automated Data Quality (ADQ)
• zeb Agentic MDM for FSI
• zeb Retail Agentic Data Activation

Blend360 Trellis IQ

Trellis IQ is Blend360's scalable agentic AI solution for high-volume data management, built on Databricks. It deploys intelligent agents that coordinate data wrangling, harmonisation, and stewardship tasks, integrating seamlessly with existing systems. The platform transforms unstructured transaction data into analysis-ready datasets by treating inconsistent product names, multilingual entries, and schema variations as natural language problems. Leveraging LLMs for contextual understanding, it operates 102x faster than manual processes at 550 records per minute with >90% accuracy. For one global CPG manufacturer, Trellis IQ cleared a 7-year harmonisation backlog in 7 days while reducing OpEx costs.

Blueprint Lakehouse Optimizer

The Lakehouse Optimizer by Blueprint is an Augmented FinOps platform that transforms how enterprises manage cost, performance, and governance across their lakehouse. Built on the Databricks ecosystem, including Unity Catalog, Delta Live Tables, and Workflows, it simplifies spend analysis, job optimization, and forecasting. With intelligent recommendations, unhealthy spend detection, automated alerts, and executive insights, LHO turns complex telemetry into clear actions. Organizational mapping and AI-driven optimization help teams cut total cost of ownership by 30%, boost performance, and reinvest savings into high-impact initiatives while maintaining governance, compliance, and scalable operations.

Read this blog to learn how the Lakehouse Optimizer helps you maximize your Databricks investment by aligning cost, performance, and governance into a unified optimization framework.

Celebal Technologies Eagle Eye - Data Observability Accelerator

Eagle Eye by Celebal Technologies is a Databricks Brickbuilder Accelerator that delivers AI-powered data observability, anomaly detection, and lineage tracking within the Lakehouse architecture. It continuously monitors pipelines, validates quality, and detects hidden drifts using ML and LLM capabilities—going beyond static rules to flag inconsistencies before they impact analytics or AI outcomes. Integrated with Unity Catalog, Eagle Eye provides interactive lineage views and actionable alerts that ensure data transparency, compliance, and accountability across industries from banking to retail, transforming observability into intelligence and enabling enterprises to make confident decisions with clean, trusted, and auditable data at scale.

Read this blog to learn how Eagle Eye ensures your data is always reliable, timely, and actionable.

Elastacloud Chat QnA

Elastacloud's Chat QnA Accelerator enables teams to query distributed enterprise data through natural language conversations. Built on Databricks AI, it connects to databases, data lakes, and SaaS tools without requiring data migration. The solution features an assumptions engine that automatically maps schemas, relationships, and business rules, eliminating technical barriers for non-technical users. It generates visualizations, maintains full governance through Unity Catalog, and ensures all responses are explainable and auditable. Users receive context-aware answers with citations while respecting existing permissions. Typical deployment takes three to six weeks, democratizing data access and reducing analyst workload while maintaining enterprise security and compliance standards.

Read this blog to learn how Chat QnA lets your team chat directly with your data, no matter where it lives.

EXL EXLdata.ai

EXLdata.ai is an agentic AI-native data solution designed to tackle the #1 barrier to AI adoption: fragmented, unstructured, and non-AI-ready data. This Databricks powered solution embeds intelligence into every stage of the data lifecycle—modernization, governance, and management, and provides an open architecture for seamless integration into all the hyperscalers and Databricks. The outcomes driven are to transform data into trusted, AI-ready inputs that fuel smarter, faster business decisions. The EXL.data.ai solution also converts fragmented enterprise data into governed, AI-ready assets, speeding up time-to-insight and enabling confident decision-making across operations, finance, and customer engagement.

Read this press release to learn how EXLdata.ai is helping to solve enterprises’ biggest challenge in making data ready for AI.

EY Data Fusion

EY’s Data Fusion is a cloud-native, AI-driven data management solution built on Databricks’ Data Intelligence Platform to meet the complex data and analytics demands of financial institutions. It simplifies data processing and delivers trusted, AI-ready data through an intuitive interface. By leveraging Unity Catalog, scalable compute, and integrated ML and GenAI capabilities, Data Fusion seamlessly handles large-scale data and AI workloads while ensuring robust performance, governance, and compliance. Advanced AI features—such as automated data quality checks, PII/PCI detection, natural language-based data mapping and exploration — boost efficiency and enhance data trust across the enterprise.

Watch this video to discover how EY Data Fusion enables efficient data management and delivers trusted, AI-ready data for financial institutions.

Infosys DE.AI

Infosys DE.AI is an AI-powered Pro Code DevX accelerator designed for the Databricks ecosystem. Operating as an intelligent pair programmer, it streamlines data engineering workflows by assisting with data migration, ELT/ETL development, code optimization, and DevOps integration. Embedded in the data engineering lifecycle, DE.AI uses custom MCP connectors to generate, refactor, and optimize PySpark, SQL, and DLT code with context-aware suggestions. Its Auto Migrator enables autonomous migration journeys, converting legacy systems like Informatica XML to Databricks through intuitive prompts and slash commands. Seamlessly integrated with Databricks Asset Bundles, Unity Catalog, and Delta Live Tables, DE.AI ensures governed, scalable enterprise deployments.

Read this POV to learn how agentic AI transforms the lifecycle by offloading the majority of efforts to intelligent agents.

LTIMindtree SSIS to PySpark Migration

LTIMindtree's SSIS-to-PySpark Migration Solution automates the transformation of legacy SSIS packages into scalable PySpark pipelines on Databricks. Using a multi-agent architecture orchestrated through LangGraph, it handles analysis, logic conversion, and documentation while preserving business intent. The modular design offers flexibility for integration with alternative orchestration frameworks, enabling seamless adaptation. By converting thousands of complex, undocumented packages into a repeatable process, LTIM accelerates modernization, reduces risk, and ensures precision and traceability throughout migration.

Read this blog to learn how this accelerator leverages AI agents orchestrated through frameworks like LangGraph to build an intelligent, modular migration utility.

Persistent Systems iAURA Data Observability

Data quality issues, inconsistent reconciliations, and stale or delayed data continue to undermine trust in analytics. iAURA Data Observability, built natively on the Databricks Lakehouse, provides continuous, intelligent monitoring of data quality, reconciliation, and freshness across pipelines. It automatically detects schema drift, anomalies, and inconsistencies before they affect downstream insights. With adaptive learning, it refines quality thresholds without manual intervention and shifts teams from reactive troubleshooting to proactive, insight-driven operations. Automated reconciliation and unified data health dashboards enable faster issue resolution and reduced reliance on manual checks. Organizations adopting iAURA have seen 30–40% fewer data quality incidents and significantly improved confidence in analytics and AI outcomes.

Read this blog to learn how this accelerator continuously monitors data quality, reconciliation, and freshness.

Persistent Systems iAURA Data Modeler & Mapper

Organizations modernizing on the Databricks Lakehouse often struggle with inconsistent data definitions, slow model development, and limited agility as business needs evolve. iAURA Data Modeler & Mapper addresses these challenges through Agentic AI–driven automation. It connects to source systems or ingests schema files, automatically identifying entities, attributes, relationships, and metadata—reducing manual discovery and mapping effort by 40–50%. iAURA then proposes optimized data warehouse schema designs, including fact/dimension structures and source-to-target mapping with transformation logic. It further accelerates KPI standardization by 35–45% and generates complete documentation and ER diagrams. The result is a faster, consistent, and business-aligned modeling experience on Databricks.

Read this blog to learn how this accelerator automates schema mapping and transformation logic.

Slalom LakeSpeak - An MCP Client with Genie Support

Slalom's LakeSpeak is a production-ready accelerator that brings graph intelligence to the Databricks Lakehouse with native Genie (MCP) support. It enables AI agents to reason across relationships, metrics, and real-time data using natural language — delivering intelligence that is accurate, contextual, and explainable across the enterprise.

Read this blog to learn how to activate Databricks intelligence in every workflow.

TCS Agentic Ops

TCS Agentic Ops automates incident identification and resolution to improve operational efficiency. This scalable AI solution extracts information from incidents and logs, classifies issues, provides tailored recommendations, and autonomously implements fixes. Built on Databricks, the agents are easily configurable and adaptable to evolving data landscapes. Organizations can reduce operational overhead by 30-40% while gaining greater visibility and control over incident management, enabling faster, more agile responses to critical issues.

Tiger Analytics Intelligent Data Express (IDX)

Tiger Analytics developed Intelligent Data Express (IDX), an AI-powered, metadata-driven accelerator for Databricks Lakehouse modernization (from ingestion, transformation, DQ to insight generation). This multi-layered solution combines a Databricks Lakehouse foundation with reusable micro-services and dual user experiences (web UI + conversational agent). IDX enables governed self-service and AI automation across the data lifecycle, accelerating Lakehouse and data-product delivery by 40%+. Its core innovation is deep Generative AI embedding. An agentic AI layer (built on Agent Bricks) powers automated source analysis, knowledge extraction, AI-driven data-quality inference, and automatic pipeline metadata generation from natural language. IDX also transpiles legacy SQL/ETL to optimized PySpark/Spark-SQL and offers a conversational Data Engineering co-pilot, transforming modernization into an intelligent, continuous capability.

Read this blog to learn more about how IDX makes data platforms faster to build, easier to manage, and ready for intelligence.

Tiger Analytics iDEA (Intelligent Data Engineering Agent)

iDEA (Intelligent Data Engineering Agent) by Tiger Analytics is an AI-powered accelerator that transforms how enterprises engineer, manage, and consume data on the Databricks Lakehouse. Built for both Data Engineers and Business Users, iDEA provides a unified conversational interface that bridges technical precision with business agility. iDEA automates every stage of the data product journey from ingestion, transformation, quality validation, and governance to discovery, analytics, and visualization. By understanding natural language intent, iDEA intelligently orchestrates workflows, enforces compliance, and delivers actionable insights instantly, empowering organizations to automate the complete Data Product Lifecycle end-to-end.

Read this blog to learn how this accelerator brings agentic intelligence to the Databricks Lakehouse, transforming how teams build, manage, and trust data.

Tiger Analytics Automated Data Quality (ADQ)

Tiger Analytic’s Augmented Data Quality (ADQ), powered by Generative AI, transforms manual, reactive data quality processes into proactive trust. Its advanced "Agentic Architecture" automatically profiles data, enriches metadata, and recommends complex DQ rules in minutes. ADQ moves beyond simple checks to perform advanced anomaly detection, identifying business-centric microsegments and flagging outliers with natural language explanations. This framework saves > ~60% in manual effort, building a new foundation of data trust and reimagining data governance.

Read this blog to learn how this framework uses Generative AI to detect anomalies, recommend dynamic rules, and build a foundation of data trust.

zeb Agentic MDM for FSI

zeb’s Agentic MDM for Financial Services accelerator automates and unifies fragmented data reconciliation using agentic AI on the Databricks Lakehouse. It consolidates funds, securities, counterparties, and client data into a single source of truth while ensuring compliance with Basel III, Dodd-Frank, and GDPR. With Unity Catalog for security and governance, it reduces manual reconciliation efforts by up to 90% and enables AI-ready data for risk management and innovation.

Read this blog to learn how this accelerator brings agent-driven insight, automated entity unification, and centrally governed mastering into one streamlined framework.

zeb Retail Agentic Data Activation

zeb's Retail Agentic Data Activation accelerator, powered by the Databricks Data Intelligence Platform, helps retailers and consumer goods companies standardize vendor and supplier data into consistent schemas. Using agentic AI for intelligent content interpretation and zero-code pipeline generation, it accelerates onboarding and ensures data quality. Built with Unity Catalog for governance, the solution delivers up to 90% faster onboarding and enhanced data accuracy across millions of SKUs.

Read this blog to learn how this accelerator transforms diverse vendor and manufacturer feeds into a unified and reliable product data foundation.

GenAI Accelerators for Data and Platform Migration

Data migration is the process of moving data between different systems, storage formats, or cloud environments. In an AI-first world, this is no longer just an IT chore—it's a foundational business imperative. Artificial intelligence and machine learning models are fundamentally dependent on vast quantities of high-quality, accessible data for training and generating accurate insights. Legacy systems often keep valuable data locked in inefficient silos, making it unusable for modern analytics. Migrating this data to modern, scalable cloud platforms is the critical first step to unlocking its potential, ensuring it is clean, consolidated, and ready to fuel powerful AI-driven innovation.

However, legacy data and platform migrations are notoriously complex, slow, and expensive. We are thrilled to introduce a new era of efficiency with GenAI Accelerators for Data and Platform Migration from migration partners. Our leading consulting and system integrator partners are leveraging generative AI to automate complex tasks like SQL and ETL code conversion, and deploying agentic AI features to intelligently orchestrate workflows and validate data autonomously. This groundbreaking approach is already delivering incredible results, enabling organizations to accelerate their migration timelines by up to 70% and reduce manual effort by more than 50%. Say goodbye to migration bottlenecks and hello to a smarter, faster, and more cost-effective path to modernization.

Say goodbye to migration bottlenecks and hello to a smarter, faster, and more cost-effective path to modernization with migration accelerators listed below:

• Cognizant Cloud Data Migration Factory
• Entrada SASquatch
• EXL Code Harbor^TM: SAS to Databricks Migration Accelerator
• Hexaware Amaze Migration Accelerator
• Indicium AI Migration Agents (Prompt2Pipeline)
• Infogain iRAPID SAS to Databricks Migration Accelerator
• Insight Agentic Data Architect & Modeler
• Koantek X2D Migration Accelerator
• LatentView MigrateMate
• LTIMindtree Scintilla.ai (SAS to Python/PySpark Migration)
• Persistent Systems iAURA Agentic ETL & DWH Migration
• Shorthills AI KodeBricks
• Wipro Legacy Modernization Tool
• Xebia Agentic Data Pipeline Migrator
• Zensar Technologies ZenseAI.Data

Cognizant Cloud Data Migration Factory

Cognizant's Cloud Data Migration Factory streamlines migration of legacy systems to the Databricks Data Intelligence Platform using advanced data engineering, NLP, and pre-trained LLMs. This generative AI-powered co-pilot enhances code quality, accelerates decision-making, and automates the modernization process through a proven end-to-end methodology. The solution reduces migration costs by 40–60%, strengthens security with Unity Catalog integration, and accelerates AI-driven analytics for smarter insights. Organizations achieve faster project completion, improved accuracy, and significant productivity gains while transitioning vast application and database portfolios to cloud platforms aligned with their business objectives.

Check out this additional landing page to learn more about how Cognizant revolutionizes the journey from insights to analytics across the data value chain.

Entrada SASquatch

Facing high SAS costs, limited scalability, and complex compliance needs, organizations struggle with modern AI adoption. Manual migration to Databricks is risky, often failing due to proprietary code and hidden dependencies. Entrada’s SAS-to-Databricks Accelerator delivers a smart, rapid, and cost-effective solution. It automates code translation, dependency mapping, and data optimization, achieving migrations up to 80% faster. The accelerator modernizes workloads on Databricks’ open platform, reducing Total Cost of Ownership and eliminating licensing fees. It ensures 100% compliance via Unity Catalog and features self-healing mechanisms, empowering organizations to unlock advanced analytics and AI at scale.

Read this blog to learn how SASquatch delivers a smart, rapid, and cost-effective path forward.

EXL Code Harbor^TM: SAS to Databricks Migration Accelerator

EXL's Code Harbor^TM is a GenAI solution that automates the migration of legacy codebases to modern open-source languages and cloud platforms like Databricks. Specializing in SAS-to-Databricks transformation, it also supports BTEQ, HQL, PL/SQL, SQL Server, R, and ETL platforms, including Informatica, Alteryx, and DataStage. Designed for insurance, banking, and healthcare sectors, Code Harbor combines EXL's domain expertise with AI capabilities while supporting on-premises, cloud, and hybrid environments. A global insurance provider achieved 50% faster SAS migration to Databricks using Code Harbor, with minimal manual intervention, enhanced compliance through comprehensive metadata documentation, and seamless governance framework integration.

Read this press release to learn how this accelerator helps enterprises streamline their transition from SAS to Databricks to support enhanced cloud modernization initiatives.

Hexaware Amaze Migration Accelerator

Accelerate your journey from legacy SAS to modern PySpark with AMAZE, Hexaware’s Migration Accelerator, powered by the Databricks Data Intelligence Platform. This AI-driven solution automates the end-to-end conversion of SAS workloads into optimized, cloud-native PySpark notebooks—reducing migration timelines by up to 80%. With GenAI and LLM-powered automation, AMAZE delivers up to 5X faster conversion speed, 70% out-of-the-box accuracy, and significantly lower total cost of ownership. Enterprises benefit from modularized, maintainable code and scalable analytics capabilities. By modernizing on Databricks, organizations unlock a unified data foundation, simplify operations, and accelerate their AI and analytics transformation with a cloud-native approach built for scale.

Read this flyer to learn how to make the switch to Python for better AI readiness.

Indicium AI Migration Agents (Prompt2Pipeline)

Migrating to Databricks no longer needs to be a complex or time-consuming process. Indicium’s AI Migration Agents (Prompt2Pipeline) combine generative AI and Agentic automation to interpret legacy code and business logic, transforming them into Databricks-native, high-performance pipelines — up to 7 times faster. The solution accelerates modernization across industries, improving governance, performance, and cost efficiency, while enabling enterprises to move seamlessly from data debt to data intelligence on the Databricks Data Intelligence Platform.

Read this blog to learn more about how Prompt2Pipeline is accelerating modernization with AI migration agents.

Infogain iRAPID SAS to Databricks Migration Accelerator

Say goodbye to expensive, siloed SAS environments. Infogain is partnering with Databricks, through their Brickbuilder Accelerator iRAPID: SAS to Databricks - PySpark Migration suite, to revolutionize data modernization, converting complex SAS procedures, EGP files, and macros into scalable PySpark code using GenAI automation. Their proven framework delivered stunning results: migrations that once took months now completed 50% faster with 95% accuracy.

Read this blog to learn how Infogain helps you unlock real-time analytics, handle everything from inventory analysis to automated validation, and scale through a cloud-native, open platform.

Insight Agentic Data Architect & Modeler

Insight Agentic Data Architect & Modeler (ADAM) is a modular solution leveraging AI and LLMs on Databricks to simplify data integration, modeling, and governance. Automated agents handle schema discovery, metadata enrichment, and data model creation, reducing manual work and accelerating results. The framework includes business user workflows for metadata review and approval, ensuring data quality and compliance. It supports secure operations including HIPAA/PHI requirements with flexible deployment options. Built on Databricks Agent Bricks, Insight Adam enables rapid data platform modernization, faster analytics, and greater agility while maintaining strong governance, enterprise catalog integration, and continuous pipeline improvement.

Koantek X2D Migration Accelerator

X2D is Koantek’s AI-driven migration accelerator, transforming legacy data ecosystems into the Databricks Lakehouse in weeks, not years. Using agentic AI and intelligent routing, X2D delivers 80% automated code conversion, reducing migration timelines by 60%. The platform combines AI-powered transpilation with Databricks Lakebridge integration, supporting 30+ data platforms, orchestration tools, and BI sources. SOC2/GDPR compliant, X2D’s enterprise-grade features include intelligent wave planning for business continuity, parallel validation ensuring zero data loss, and Unity Catalog-native governance. Koantek has migrated petabyte-scale environments using X2D in under 12 weeks for Fortune 500 enterprises, delivering immediate ROI through reduced operational costs and accelerated time-to-insight.

Read this blog to learn how to migrate legacy EDW/ETL to the Databricks Lakehouse 3x faster with ~60% lower cost and near-zero risk.

LatentView MigrateMate

LatentView MigrateMate is an automated, platform-agnostic data migration solution that makes it simple to move your most valuable data from on-premise systems to the cloud. Purpose-built for modernization, MigrateMate integrates seamlessly with Databricks to deliver a smooth, end-to-end migration into a secure and scalable lakehouse foundation ready for analytics, AI, and governance. By combining automation, data deduplication, and intelligent optimization, MigrateMate helps organizations cut migration costs by 30% to 40% while maintaining data quality and integrity. Its Databricks-enabled workflows bridge system compatibility gaps and accelerate time to insight, turning complex migrations into a fast, reliable, and value-driven transformation journey.

Read this blog to learn how MigrateMate incorporates GenAI for discovery, conversion, and automated validation.

LTIMindtree Scintilla.ai (SAS to Python/PySpark Migration)

Modern businesses need to migrate from expensive, inflexible SAS systems to scalable cloud platforms like Databricks, but manual code conversion is slow and risky. LTIMindtree's Scintilla.ai offers an intelligent, automated solution using a multi-agent system that analyzes SAS code, converts it to optimized PySpark, and validates results for accuracy. This preserves business logic while reducing manual effort by 80%. The platform integrates seamlessly with Databricks and Unity Catalog, enabling organizations to retire costly SAS licenses and embrace cloud agility confidently, transforming complex migrations into controlled, efficient transitions.

Read this blog to learn more and visit this page for additional details about the accelerator.

Persistent Systems iAURA Agentic ETL & DWH Migration

Modernizing legacy ETL and data warehouses is complex due to tightly coupled pipelines, undocumented logic, and large-scale data validation needs. iAURA Agentic ETL and DWH Migration, built natively on the Databricks Lakehouse, streamlines this process using GenAI and agentic automation. It supports migrations from platforms like Oracle, Teradata, Informatica, DataStage, SAS, Snowflake, and more. iAURA automatically parses legacy ETL code, extracts business rules, maps dependencies, and generates Databricks-native pipelines in PySpark, SQL, or Delta Live Tables. Automated data reconciliation ensures accuracy and parity. Enterprises achieve 30–50% faster migration, lower costs, and a smoother, more reliable modernization journey to Databricks.

Read this blog to learn how iAURA help enterprises modernize with intelligence, automation, and speed on the Databricks Data Intelligence Platform.

Shorthills AI KodeBricks

KodeBricks by Shorthills AI is a Generative AI accelerator built on Databricks that automates data pipeline creation and migrates data and ETL scripts to Databricks. Using Vibe Coding, it allows developers to provide instructions in plain, conversational English, which KodeBricks then converts into production-ready pipelines. By automating tasks like cluster setup, pipeline generation, and governance, KodeBricks cuts down time spent on manual configurations, freeing up to 50% time spent on these essential non-coding tasks. It writes high-quality and efficient Spark code from intent and Databricks SQL and creates structured notebooks staying within the developer’s IDE. This results in faster, error-proof delivery, improved productivity, and higher efficiency across your organization.

Read this blog to learn how Shorthills AI's KodeBricks helps you build faster, smarter, and more efficient pipelines.

Wipro Legacy Modernization Tool

Wipro's Legacy Modernization Tool, powered by Azure Databricks, reimagines enterprise transitions from legacy systems like SAS to modern open-source ecosystems. The platform automates SAS-to-Python and SAS-to-PySpark conversion with high accuracy, delivering detailed code insights, syntax validation, lineage tracking, and production-ready outputs with minimal manual effort. AI-powered agents automatically detect and correct logical errors in SAS data steps, macros, procedures, and functions. Built on Azure Databricks, it provides scalable compute for analysis, debugging, conversion, and documentation. The solution accelerates modernization, reduces migration complexity, and preserves existing SAS asset value while enabling faster innovation.

Xebia Agentic Data Pipeline Migrator

Xebia’s Agentic Data Pipeline Migrator accelerates migrations to Databricks by automating SQL and ETL modernization using a multi-agent framework powered by Databricks-native LLMs. The migrator analyzes source workloads from Snowflake, Redshift, BigQuery, Postgres, MySQL, and SQL Server, then translates, validates, and rebuilds them as optimized Databricks pipelines. Teams receive a fully auditable report that preserves logic, lineage, and performance. What once required weeks of manual recoding now completes in hours, reducing risk and providing organizations with a fast and reliable path into Databricks.

Read this case study to learn more on how Xebia helped Modernize a Global E-Commerce Data Pipeline with Agentic AI on Databricks.

Zensar Technologies ZenseAI.Data

Modern enterprises need to simplify data ecosystems and unlock value trapped in legacy ETL and EDW platforms. ZenseAI.Data, Zensar’s next-gen accelerator, automates migrations to the Databricks Data Intelligence Platform—reducing timelines by 30–40%. It delivers structured, transparent modernization with automated lineage, code translation, and validation, ensuring compliance and predictability. Beyond migration, ZenseAI.Data enables unified, governed data foundations for AI-ready architectures, real-time insights, and industry-specific outcomes. Together with Databricks, it lays the groundwork for agentic AI, empowering enterprises to monetize data, drive automation, and scale innovation.

Read this blog to learn how Zensar streamlines and automates migrations from legacy systems to Databricks.

Streamline Data Engineering and Migration

The era of GenAI and Agentic AI is here, and partner solutions and accelerators for data engineering and migration built on the Databricks Data Intelligence Platform are key to removing the undifferentiated heavy lifting required by data professionals. By leveraging these purpose-built accelerators, companies can empower their data engineers to be more productive and focus their efforts on high-value data engineering tasks. Whether you're looking to improve efficiency of your data engineering team or speed up migration efforts to Databricks, our partners are ready to help you accelerate your data, analytics and AI journey.

Stay tuned for the next blog in the series, where we will share GenAI partner solutions aligned to industry-specific outcomes. The first blog in the series introduced cross-industry accelerators for Agentic AI, GenAI and LLMOps.

Get started with Brickbuilder Solutions

At Databricks, we continually collaborate with system integrators and consulting partners to enable more use cases across data, analytics, and AI. Want to get started? In addition to Agentic AI Systems, Cross-Industry GenAI Use Cases, Cross-Industry GenAI Frameworks, and LLMOps Accelerators, check out our full set of partner solutions and accelerators on the Databricks Brickbuilder page.

Create a Brickbuilder for the Databricks Data Intelligence Platform

Brickbuilders are a key component of the Databricks Partner Program and recognize partners who have demonstrated a unique ability to offer differentiated data, analytics, and AI solutions and accelerators in combination with their development and deployment expertise.

Partners who are interested in learning more about how to create a Brickbuilder Solution or Accelerator are encouraged to email us at [email protected].