Accelerating Data Pipelines on Databricks using Generative AI

How Shorthills AI's KodeBricks helps you build faster, smarter, and more efficient pipelines

Data teams often find themselves spending weeks on tasks like learning infrastructure, understanding clusters and optimal cluster sizes, and handling repetitive code. These activities, though essential, take time away from building scalable solutions. As a result, the true value of Databricks remains untapped. Even with AI-assisted code generation, engineers still face the challenge of juggling multiple tools, leading to context switching that slows down the overall process.

Enter KodeBricks- Shorthills AI’s Generative AI accelerator which utilizes Vibe Coding, to automate data pipeline creation, streamlining workflows, and reducing manual coding tasks. KodeBricks allows developers to do more, letting them instruct in plain, conversational English of what is required. As a “vibe-coding” tool, it turns those instructions into production-ready pipelines, cutting friction and speeding up Databricks deployment.

By automating the data workflow setup, ensuring built-in governance, and consistency across projects, KodeBricks eliminates the roadblocks that slow down teams, helping them deliver results to customers faster—results that are not only faster and error-proof but also save vast amounts of time that can be redirected to other high-value tasks, improving the overall efficiency of the organization.

About KodeBricks: The AI Accelerator for Databricks for generation of ETL pipelines and workflows

KodeBricks is a Generative AI accelerator that converts natural language into production-ready data pipelines and partners with your team to manage the Databricks workflow- from cluster setup to delivery.

Built natively for the Databricks Lakehouse, it works seamlessly with Unity Catalog and Delta Lake, ensuring every pipeline adheres to governance and best practices by default.

KodeBricks moves beyond simple code generation to enable end-to-end workflow automation, empowering both engineers and analysts to build, deploy, and manage pipelines directly from their preferred IDE. By reducing development friction, it accelerates project delivery on Databricks and also cutting down time non-coding tasks that sometimes takes up to 50% of teams’ bandwidth.

Why Should You Switch to KodeBricks?

1. Eliminate Productivity Bottlenecks

   Context switching between IDEs, tools, and documentation is a productivity killer. KodeBricks integrates directly into your preferred IDE, freeing developers from managing infrastructure.

   
   

2. AI-Powered Code & SQL Generation

   KodeBricks leverages cutting-edge LLMs (like GPT 5, Gemini 2.5 Pro) via a deeply customized IDE integration. It writes high quality and efficient Spark code from intent and Databricks SQL and creates structured notebooks.

   
   

3. Automate ETL Pipeline Creation & Reduce Repetitive Tasks

   KodeBricks automates the creation of ETL pipelines, including tasks like cluster setup, notebook scaffolding, and I/O routines. Engineers can save up to 50% time spent on manual configurations.

   
   

4. Widening pool for Data Engineering

   Even analysts and new team members, with no coding experience, can contribute to pipeline creation. With KodeBricks, all they need to do is describe their desired outcome in natural language, and the tool will generate a production-ready pipeline. This advantage is particularly valuable as it frees up teams and managers from the burden of training new team members.

   
   

5. Streamline Governance & Ensure Compliance

   Governance and compliance are crucial but often time consuming. KodeBricks automatically enforces governance by leveraging Unity Catalog, ensuring data lineage, access control, and policy compliance for every pipeline.

Why Databricks + KodeBricks

KodeBricks complements Databricks by removing the operational friction between intent and execution. Together, they redefine what productivity, governance, and innovation look like in the modern enterprise.

• For leaders: It means higher ROI due to time savings and increased project throughput, and therefore accelerated decision-making, along with higher and easier platform adoption.

  
  

• For architects: It ensures design consistency and built-in compliance across the enterprise. It also means lower efforts in repeated checks and fewer possibilities of mistakes due to the accuracy that automation of ETL pipelines brings along. It also saves time spent on finding the right clusters, setting them up and sizing them, so one gets the right compute faster with less trial and error.

  
  

• For engineers: It replaces repetitive tasks with automation. It frees them from non-core coding tasks, which eat up a significant amount of time, especially for new teams, leading to faster onboarding. It translate development intent directly into efficient and high-quality Spark code, reducing boilerplate and boosting efficiency by up to 50%.

  
  

• For product managers: It provides the ability to deliver solutions faster, with reduced friction between product design and development. The time savings and streamlined workflows also allow product managers to focus on strategic initiatives, reducing project delays and enhancing the product development lifecycle.

  
  

• For Databricks: KodeBricks amplifies Databricks’ value proposition by making the platform more accessible, improving adoption, and enabling users to leverage its full capabilities faster. The automated pipeline generation ensures higher quality, better governance, and seamless integration, leading to enhanced customer satisfaction and increased usage.

This partnership represents the next evolution of the Lakehouse: where Generative AI and Data Engineering converge to turn every idea into a production-ready solution.

Industries Transformed with KodeBricks: Real-World Impact

Automotive M&A Advisory

A leading US automotive advisory firm that provides M&A and investment insights for the U.S. car dealership market struggled to leverage its raw data, coming from over 18,000 dealerships spanning decades. Each record had roughly 150 fields drawn from multiple, disparate   datasets and other open source APIs. This had issues of inconsistent formats, missing common identifiers that prevented easy merging, and large gaps. These problems slowed extraction of actionable insights: full data refreshes took more than a week and blocked timely, strategic decisions such as dealership valuations.

With KodeBricks, the firm automated its data ingestion pipelines, reducing the time to process raw files from weeks to days. The solution deployed machine learning models to generate predictive analytics, such as revenue forecasts, and built a visualization platform for dealership data. KodeBricks enabled a seamless integration of data, generating reports automatically.

Healthcare & Life Sciences Analytics

A healthcare analytics firm dealing with massive, fragmented datasets—such as EHRs, claims, and patient records—struggled to build a unified, actionable view of patient data. The data, scattered across siloed systems and in inconsistent formats, hindered critical analytics for risk assessments, cost estimation, and proactive care.

By migrating multi-terabytes of historical data to a modern Databricks Lakehouse and utilizing KodeBricks to automate ETL pipelines, the firm data sources into a single, reliable dataset. KodeBricks automated data ingestion, cleansing, and transformation, enabling a single source of truth for analytics, while ensuring compliance with industry standard stringent healthcare regulations.

How It Works: The KodeBricks + Databricks Synergy

KodeBricks operates as an intelligent layer within the Databricks Lakehouse, automating every stage of the data lifecycle:

1. Ingest: KodeBricks connects to diverse sources and generates optimized ingestion pipelines using Databricks tools like Auto Loader and Delta Live Tables.

   
   

2. Transform: It automatically builds the Medallion architecture—Bronze for raw data, Silver for cleansed data, and Gold for business-ready aggregations—ensuring reusability and standardization.

   
   

3. Govern: Every dataset, job, and transformation is registered with Unity Catalog, enforcing access control, lineage, and policy compliance.

   
   

4. Orchestrate: KodeBricks creates and manages Databricks Workflows, adding job dependencies, alerts, retries, and monitoring automatically.

   
   

5. Visualize and Share: Once processed, data is instantly accessible via Databricks SQL or shared securely with partners using Delta Sharing—ready for visualization in Power BI, Tableau, or Looker.

In essence, KodeBricks turns human intent into governed, executable Lakehouse workflows—without a single manual setup step.

KodeBricks UI

1. Generating and deploying a notebook from a prompt

   

   

2. Defining an Entire ETL Workflow in Plain English

   

   

   
   

3. Instantly Querying Data Without Writing SQL

   

   

About Shorthills AI

Shorthills AI is a leading Generative AI and Data Engineering company helping high-growth enterprises scale smarter and innovate faster. With a large team of data and AI experts, we deliver full-stack technology implementations and Generative AI–powered automation, enabling enterprises to realize value 30–40% faster.

Founded in 2018, Shorthills AI has partnered with global technology leaders including Databricks, Microsoft, and Meta, and is recognized by NASSCOM for excellence in data innovation.

With deep expertise across Auto, Financial Services, Healthcare, Real Estate Lending, and Digital Native Businesses, Shorthills AI helps organizations accelerate topline growth, optimize costs, enhance customer satisfaction, and minimize risk through intelligent, connected solutions.