Unlock the Full Potential of Databricks
Optimize operations and maximize business value with real-time data, advanced analytics, and next-generation automation.
We can turn this vision into reality!
The most common issue with Databricks is the underutilization of its potential. Data is nothing but a money pit if it cannot be transformed into business-supporting value. That’s why data platforms can’t remain at a “data swamp” level — evolution is needed!
Ikidata is a deep Databricks expert, bringing intelligence and automation to reality.​ Every success story starts with a clear understanding of the bigger picture, so let’s break it down together and uncover the value of each piece.
Infrastructure - The Foundation
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Just like building a house, the importance of a strong foundation cannot be overstated. The same principle applies to developing the Databricks Data Intelligence Platform. For setting up infrastructure, we strongly recommend using Terraform, which comes with excellent support and documentation. The most critical components, such as provisioning a new workspace, are best handled through Terraform.
At a more specific level—like setting up clusters, creating workflows, or serving endpoints—you should choose the approach that best fits your needs. The possible options on top of Terraform include:
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Databricks Asset Bundles – a user-friendly option
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Databricks CLI – offers more flexibility
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Custom automation via REST API – for full control and customization
Choosing the right strategy ensures scalability, efficiency, and seamless integration into your organization’s workflow.
Data Ingestion - The Raw Material
Databricks supports both ETL and streaming data pipelines, offering industry-leading frameworks and tools. For streaming data, we highly recommend using Delta Live Tables (DLT), which enables a seamless end-to-end process with scalable and reliable data pipelines.
When it comes to traditional batch data pipelines, there are numerous options available. With Lakehouse Federation, you can establish direct API connections, build custom Python scripts to fetch incremental data from REST APIs, or process incoming raw files dynamically. The flexibility of Databricks ensures that regardless of the ETL challenge, there is always a solution. To date, we have yet to encounter an ETL requirement that Databricks couldn’t handle.
Data processing - Medallion Architecture
In data processing, the Medallion architecture is highly recommended. The concept is simple: raw data is first stored as a bronze Delta table in its original, untouched form. This ensures that if any issues arise later in the pipeline, data products can be easily rebuilt using the bronze data as a reliable source of truth.
Next, the data is cleaned and structured into a silver Delta table, making it business-ready. This step typically involves renaming columns, changing data types, and applying minor transformations. Since silver tables are directly accessible to business users, data quality measures should be implemented at this stage.
The final stage consists of gold tables or views, which represent fully refined data products. These may include aggregated insights, multiple silver tables combined, machine learning model predictions, or any other form of curated data.
For orchestrating the entire data processing workflow, Databricks Workflows is the recommended solution.
Data Governance – Unity Catalog to rule them all
Unity Catalog provides a unified solution for data and AI governance, operating at the account level. With Unity Catalog, organizations can establish centralized governance and monitoring, enabling effortless visibility and control over their entire data ecosystem.
It encompasses data integrations with external systems, data management, AI model governance, audit trails, cost monitoring and more. Practically everything needed for comprehensive data oversight. Best of all, Databricks offers system tables that can be leveraged for automated monitoring and reporting. Since you own your data, there's no need to transfer it to external software providers, ensuring both security and compliance.
Value creation in Databricks - Self-help layer
The biggest challenge with Databricks is often its underutilization. Data, on its own, is just a financial burden unless it is transformed into real business value. That’s why it’s crucial to empower business users to make the most of Databricks.
Databricks provides powerful AI-assisted tools for everyday operations. With Genie, you can work like a data engineer/analyst without writing a single line of code, seamlessly turning ideas into business insights instead of getting stuck in internal ticketing processes. Additionally, Databricks Assistant offers exceptional support for writing Python and SQL code, significantly boosting efficiency.
Creating secure user interfaces has never been easier with Databricks Apps, while AI Playground allows instant access to LLM models and AI agents at the click of a button. All in all, a truly enjoyable experience!
Reporting & Analytical needs - Visualize Business Insight
With dashboards, you can effortlessly transform data into clear and actionable visual insights. There are numerous options available, but the best starting point is Databricks’ own rapidly evolving AI/BI Dashboard. This ensures that data remains securely in one place without the need to transfer it to external software providers, while also enabling built-in monitoring and cost optimization.
Additionally, Genie is natively integrated, making AI/BI dashboards even more efficient and user-friendly. Of course, Databricks also offers seamless integrations with the most popular third-party dashboard solutions. Beyond native integrations, secure data sharing is made easy with Delta Sharing capabilities, ensuring that insights can be accessed and utilized safely across your organization.
Machine Learning & Deep Learning – From Data Points to Intelligence
Developing Machine Learning (ML) and Deep Learning (DL) solutions in Databricks is a truly enjoyable experience. ML clusters automate the setup process, providing a pre-configured infrastructure with the most commonly used ML and DL libraries — so you can focus on building models rather than managing environments.
With AutoML, you can automate the initial steps of the model creation process and then continue refining it with the auto-generated code. MLflow makes it effortless to implement MLOps, ensuring proper governance from proof of concept (PoC) to full-scale production deployment.
And when it’s time to move into production, Model Serving allows you to deploy and utilize your models instantly — at the click of a button.
Mosaic AI – GenAI Usage
In Databricks, Generative AI (GenAI) development is powered by Mosaic AI, providing everything needed for a robust LLMOps framework. You can leverage external model providers such as Azure OpenAI or Amazon Bedrock, utilize Databricks-hosted models (e.g., Llama 3.3), or even train your own LLMs.
With Mosaic AI Gateway, governance is seamlessly maintained, eliminating concerns over unexpected costs or unauthorized usage. Through Model Serving, you can effortlessly host models or GenAI agents, making them easily accessible for external systems.
A comprehensive and powerful ecosystem — built for seamless AI innovation!
GenAI Agents - The Future
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At last, we’ve arrived at the core of the discussion. GenAI has rapidly become a game-changer, giving companies a competitive advantage through advanced automation and cutting-edge solutions. To drive the next wave of business value, organizations must embrace robust automation powered by GenAI agents. The companies that master this transformation will be the true winners of the future.
As a pioneer in GenAI Agent automation, Ikidata provides deep expertise in this rapidly evolving field — covering technical, architectural, and business perspectives.
