🎥 Azure Databricks Series: Mastering External Tables – A Step-by-Step Guide 📊✨ скачать в хорошем качестве

🎥 Azure Databricks Series: Mastering External Tables – A Step-by-Step Guide 📊✨

You can find the scripts used in this video in below blog, https://jbswiki.com/2024/09/22/%f0%9f... [UNSUPPORTED_FEATURE.TABLE_OPERATION] The feature is not supported: Table `spark_catalog`.`default`.`hdfcbank` does not support DELETE. Please check the current catalog and namespace to make sure the qualified table name is expected, and also check the catalog implementation which is configured by "spark.sql.catalog". SQLSTATE: 0A000 External Tables in Azure Databricks allow you to define tables that map to data stored outside of the Databricks File System (DBFS). This means you can query and manipulate your external data directly from Databricks without the need to import it into DBFS. 🗄️ Think of it as creating a virtual layer over your existing data storage. It’s like having a window into your data lake, blob storage, or any external data source, enabling you to interact with the data as if it were part of your Databricks workspace. 💡 Why Use External Tables? There are several reasons why External Tables are a valuable tool in your Databricks toolkit: Cost Efficiency 💸: Since the data is not duplicated in Databricks, you save on storage costs. Data Consistency 📅: You are always querying the most up-to-date data from the source. Scalability 🚀: Handle large datasets without worrying about storage limitations in Databricks. Seamless Integration 🔗: Easily integrate with other Azure services like Azure Data Lake Storage (ADLS), Azure SQL, and more. Using External Tables, you can achieve a balance between performance and cost while maintaining data integrity across different environments. ⚖️ 💡 Section 2: Benefits of Using External Tables External Tables provide a range of benefits that make them a must-have for any data professional. Let’s break down these benefits in more detail: 1. Seamless Data Access 🔄 External Tables allow you to query data from various sources without moving or duplicating it. This is especially useful when working with data stored in multiple locations, such as: Azure Data Lake Storage 🏞️ Azure SQL Database 🗄️ Blob Storage 🌐 On-Premises Databases 🏢 With External Tables, you can connect to these sources directly and start analyzing data immediately, without any complex data import processes. 🚀 2. Cost Efficiency 💰 Data duplication can be costly, especially when working with large datasets. External Tables help you avoid this by keeping the data in its original location and creating a virtual table that points to it. This way, you only pay for the storage and compute resources you actually use. 💸 3. Scalability 🏗️ Whether you’re working with gigabytes or petabytes of data, External Tables allow you to scale your data analytics without hitting storage limits. Since the data remains in its original storage, you can leverage the scalability of Azure services like ADLS or Blob Storage to handle even the largest datasets. 📈 4. Data Consistency and Integrity 🔒 By querying the data directly from its source, you ensure that you’re always working with the most recent and accurate data. This eliminates the risk of data discrepancies that can arise when moving data between different systems. ✅ 5. Improved Data Governance and Security 🛡️ With External Tables, you can enforce data access policies at the storage level, ensuring that only authorized users can access sensitive data. This adds an extra layer of security to your data workflows. 🔐 🌐 Section 3: Real-World Use Cases for External Tables External Tables can be a game-changer in various scenarios. Let’s explore some real-world use cases where External Tables shine: 1. Data Lake Analytics 🏞️ Imagine you have a massive data lake with terabytes or even petabytes of raw data. Importing this data into Databricks for analysis would be costly and time-consuming. Instead, you can use External Tables to query and analyze the data directly from the data lake. This allows you to perform complex analytics without moving a single byte of data! 💡 2. Data Integration Projects 🔗 If you’re integrating data from multiple sources, such as an on-premises database, an Azure SQL Database, and a data lake, External Tables allow you to create a unified view of your data. You can query data from all these sources within a single Databricks notebook, making it easy to perform cross-source analytics. 🔄 3. ETL Pipelines 🛠️ Building ETL pipelines often involves moving data between different storage systems. With External Tables, you can simplify your ETL workflows by querying the source data directly, transforming it in Databricks, and then writing the transformed data back to its destination. This reduces the need for data duplication and simplifies the pipeline architecture. 📊 4. Data Exploration and Ad-Hoc Analysis 🔍 External Tables are perfect for data exploration and ad-hoc analysis.