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Category: data lakehouse

  • Processing Data Lakehouse Data for Machine Learning

    Processing Data Lakehouse Data for Machine Learning Processing Data Lakehouse Data for Machine Learning Leveraging the vast amounts of data stored in a data lakehouse for Machine Learning (ML) requires a structured approach to ensure data quality, relevance, and efficient processing. Here are the key steps involved: 1. Data Discovery and Selection Details: The initial… Read more

  • Processing Data Lakehouse Data for Agentic AI

    Processing Data Lakehouse Data for Agentic AI Processing Data Lakehouse Data for Agentic AI Agentic AI, characterized by its autonomy, goal-directed behavior, and ability to interact with its environment, relies heavily on data for learning, reasoning, and decision-making. Processing data from a data lakehouse for such AI agents requires careful consideration of data quality, relevance,… Read more

  • Building an Azure Data Lakehouse from Ground Zero

    Building an Azure Data Lakehouse from Ground Zero Building an Azure Data Lakehouse from Ground Zero: Detailed Steps Building a data lakehouse on Azure involves leveraging Azure Data Lake Storage Gen2 (ADLS Gen2) as the storage foundation, along with services like Azure Synapse Analytics, Azure Databricks, and Azure Data Factory for data processing and querying.… Read more

  • Building a GCP Data Lakehouse from Ground Zero

    Building a GCP Data Lakehouse from Ground Zero Building a GCP Data Lakehouse from Ground Zero: Detailed Steps Building a data lakehouse on Google Cloud Platform (GCP) involves leveraging services like Google Cloud Storage (GCS), BigQuery, Dataproc, and potentially Looker. Here are the detailed steps to build one from the ground up: Step 1: Set… Read more

  • Building an AWS Data Lakehouse from Ground Zero

    Building an AWS Data Lakehouse from Ground Zero Building an AWS Data Lakehouse from Ground Zero: Detailed Steps Building a data lakehouse on AWS involves setting up a scalable storage layer, a robust metadata catalog, powerful ETL/ELT capabilities, and flexible query engines. Here are the detailed steps to build one from the ground up: Step… Read more

  • 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

  • Integrating with AWS Data Lakehouse: Real-Time and Batch mode

    Integrating with AWS Data Lakehouse: Real-Time and Batch Integrating with AWS Data Lakehouse: Real-Time and Batch AWS offers a suite of services to build a data lakehouse, enabling both real-time and batch data integration. The core of the data lakehouse is typically Amazon S3, with services like AWS Glue, Amazon Athena, and Amazon Redshift providing… 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 High level Concepts

    Databricks High-Level Concepts: A Detailed Overview Databricks High-Level Concepts: A Detailed Overview Databricks is a unified analytics platform built on top of Apache Spark, designed to simplify big data processing and machine learning. It provides a collaborative environment for data scientists, data engineers, and business analysts. Here’s a detailed overview of its key high-level concepts:… Read more

  • Simplistic implementation of Medallion Architecture (With Code)

    Here we demonstrate a simplistic implementation of Medallion Architecture. Medallion Architecture provides a structured and robust approach to building a data lakehouse. By progressively refining data through the Bronze, Silver, and Gold layers, organizations can ensure data quality, improve governance, and ultimately derive more valuable insights for their business Python Explanation of the Sample Code… 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

  • Data Lake vs. Data Lakehouse: Understanding Modern Data Architectures

    Organizations today grapple with ever-increasing volumes and varieties of data. To effectively store, manage, and analyze this data, different architectural approaches have emerged. Two prominent concepts in this landscape are the data lake and the data lakehouse. While both aim to provide a centralized data repository, they differ significantly in their design principles and capabilities.… Read more