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Category: Databricks

  • Integrating with Azure Data Lakehouse: Real-Time and Batch

    Integrating with Azure Data Lakehouse: Real-Time and Batch Integrating with Azure Data Lakehouse: Real-Time and Batch Azure provides a comprehensive set of services to build a data lakehouse, primarily leveraging Azure Data Lake Storage Gen2 (ADLS Gen2) as the foundation, along with services for real-time and batch data integration and processing. Real-Time (Streaming) Integration Real-time Read more

  • Comparing BI Offerings: AWS, Azure, and GCP

    Comparing BI Offerings: AWS, Azure, and GCP Comparing Business Intelligence (BI) Offerings: AWS, Azure, and GCP Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) are the leading cloud providers, each offering a comprehensive suite of services for Business Intelligence (BI) and data analytics. While there’s feature overlap, they also have distinct strengths. Read more

  • Real-Time Ingestion of Salesforce Data into Azure Data Lake

    Real-Time Ingestion of Salesforce Data into Azure Data Lake Real-Time Ingestion of Salesforce Data into Azure Data Lake Ingesting data from Salesforce into Azure in real-time for a data lake typically involves leveraging event-driven architectures and Azure’s data streaming and integration services. Here are the primary methods: 1. Salesforce Platform Events or Change Data Capture Read more

  • Stream Data Processing in Azure

    Stream Data Processing in Azure Stream Data Processing in Azure Microsoft Azure offers a variety of services for building real-time data streaming and processing solutions. Core Azure Services for Stream Data Processing: 1. Azure Event Hubs A highly scalable publish-subscribe service that can ingest millions of events per second with low latency. It serves as Read more

  • C3.ai and Competition

    C3.ai and Competition (2025) In April 2025, C3.ai (AI) operates in the enterprise AI software market, providing a suite of applications and a platform for digital transformation. Their offerings cater to various industries, including manufacturing, financial services, government, utilities, oil and gas, and defense. C3.ai’s Key Areas: Enterprise AI Applications: Over 130 pre-built AI applications Read more

  • Top 20 Databricks Interview Questions

    Preparing for a Databricks interview? This article compiles 20 key questions covering various aspects of the platform, designed to help you showcase your knowledge and skills. 1. What is Databricks? Answer: Databricks is a unified analytics platform built on top of Apache Spark. It provides a collaborative environment for data engineering, data science, and machine Read more

  • Databricks Optimization Techniques for Enhanced Performance

    Let’s dive into some key Databricks optimization techniques to enhance the performance and efficiency of your data processing workloads. These techniques span various aspects of the Databricks platform and Apache Spark. 1. Data Partitioning Concept: Dividing your data into smaller, more manageable chunks based on the values of one or more columns. This allows Spark Read more

  • Databricks Workflow Sample: Simple ETL Pipeline

    Let’s walk through a sample Databricks Workflow using the Workflows UI. This example will demonstrate a simple ETL (Extract, Transform, Load) pipeline: Scenario: Extract: Read raw customer data from a CSV file in cloud storage (e.g., S3, ADLS Gen2). Transform: Clean and transform the data using a Databricks notebook (e.g., filter out invalid records, standardize Read more

  • Medallion Architecture

    The Medallion Architecture is a data lakehouse architecture pattern popularized by Databricks. It’s designed to progressively refine data through a series of layers, ensuring data quality and suitability for various downstream consumption needs. The name “Medallion” refers to the distinct quality levels achieved at each layer, similar to how medals signify different levels of achievement. Read more

  • Databricks scalability

    Databricks is designed with scalability as a core tenet, allowing users to handle massive amounts of data and complex analytical workloads. Its scalability stems from several key architectural components and features: 1. Apache Spark as the Underlying Engine: 2. Decoupled Storage and Compute: 3. Elastic Compute Clusters: 4. Auto Scaling: 5. Serverless Options: 6. Optimized Read more