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Continue reading →: Git matters more than ever in the age of AI coding
Before AI, a developer might write 100 lines of code in a day and know almost every line and its logic, because they typed it themselves. Git usage was relatively straightforward: add, commit, and push. But the moment a developer or team adopts an AI coding agent; the pace changes…
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Continue reading →: Testing setup in databricks ETL
A cleanly running Databricks job for three months just failed, and it’s time to dig into the results of each step to understand where it went wrong. Someone changed how duplicate orders get deduplicated — and nobody noticed, because nothing was watching. That’s the scenario a proper test suite exists…
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Continue reading →: Logging in Databricks
“Why sudden drop in the record count, while the incoming stream and data size is unaffected?” is the most common question in the data engineering world. Now, we need to backtrack and understand the run details along with the processed data: was there any bulk data received with nulls in…
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Continue reading →: Databricks Lakehouse Federation Intro
While data has become a valuable asset for every organization and plays a key role in the decision-making process, the value of analytical workloads has increased significantly as well. However, many organizations still have legacy data that has not yet been migrated and continues to reside in relational databases such…
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Continue reading →: Databricks Lakeflow Connect Introduction
While developing any data pipeline, it starts with extracting data from a source. Especially when preparing data for a BI dashboard or AI model, both need data from different sources. The source is less likely to be cloud storage and more likely to be enterprise SaaS applications like Salesforce, SAP,…
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Continue reading →: Databricks AI Functions Intro
Whether the goal is to generate business insights, build AI-powered applications, or train machine learning models, organizing data into a structured format—rows and columns—is essential. Structured data offers several advantages: It is easier to analyse. It improves clarity and readability. It can be queried efficiently using SQL, the lingua franca…
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Continue reading →: Databricks Column Level Security
Data governance is a paramount concern for every data engineer to ensure that data maintains high integrity. One of the key aspects of achieving this is granting access only to the right people. In Databricks, access control is implemented across multiple layers: the account level, the workspace level, and the…
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Continue reading →: Databricks Row Level Security
Row-Level Security (RLS) is one of those database concepts that sounds intimidating—until someone explains it the right way. Think of it like a lunchbox rule at school. Everyone can open the fridge, but you can only take your lunchbox—not your friend’s, not your teacher’s. A database works the same way.…
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Continue reading →: Understanding Databricks UDFs
In any data platform, functions play a major role in keeping code modular, reusable, and clean. The Databricks ecosystem also provides functions, it is important to design functions correctly without compromising Spark’s capabilities. Like other systems, Databricks offers a rich set of built-in functions. However, this blog focuses on User-Defined…
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Continue reading →: Introduction and Getting Started with DBT on Databricks
As organizations continue to adopt modern data platforms, the need for reliable, scalable, and maintainable data transformation processes has become increasingly important. While Lakehouse platforms excel at storing and processing large volumes of data, transformation logic is often developed and maintained entirely by data engineers. In many organizations, even a…