Overview In recent months, there's been a surge in frameworks promoting "agentic" architectures for solving information retrieval and decision-making tasks. These include MCP, A2A, AutoGen, LangGraph, and OpenAI’s agents-python-sdk. While these model...
If you’ve ever worked with a data lake, you know how quickly it can turn into a “data swamp.”Messy, Unstructured, Hard to trust, Harder to scale. That’s where Medallion Architecture comes in — and when combined with Databricks, it becomes an absolu...
Introduction Modern data platforms demand real-time capabilities — from ingestion to transformation to serving data for BI and ML use cases. Azure Databricks offers three powerful tools to help with this: Auto Loader: For scalable, file-based ingest...
In today's rapidly evolving digital landscape, data security and compliance have become paramount. Delta Live Tables (DLT) is a powerful framework primarily used for data ingestion and transformation within the Databricks ecosystem. This article expl...
As a Senior Machine Learning Engineer working on a customer churn prediction model at a bustling tech company, I recently encountered a puzzling situation that turned into an insightful journey. Our team was tasked with maintaining a production machi...
Key Concepts ETL (Extract, Transform, Load) It is a process used in data warehousing to: Extract data from various sources. Transform it into a format suitable for analysis. Load it into a data warehouse for storage and querying. Data Warehouse ...
If you're a data engineer working with Databricks, you know that choosing the right type of table is crucial for your workflows. Databricks offers a variety of table types, each designed for specific use cases. Here’s a breakdown of the 9 most import...
Setting up clusters in Databricks can feel like trying to build a house without knowing how many rooms you need. Too few resources, and your jobs crash. Too many, and you burn cash. Let’s break this down step by step—with real examples—so you can mat...
databricks tutorial with code examples example 1: creating an etl pipeline in databricks from pyspark.sql import sparkSession spark = sparkSession.builder.appName("etl_pipeline").getOrCreate() df = spark.read.csv("s3://data-source/customers.csv", h...
Imagine your company is collecting huge amounts of data—customer transactions, logs, real-time event streams, and even those unnecessary cat videos stored in some forgotten corner of the cloud. You've been asked to organize and make sense of this cha...
