Vijay Gadhave’s Post

What is the INSERT BY NAME feature in Databricks and How does it make data insertion simpler? -The INSERT BY NAME feature in Databricks SQL makes it easier to insert data into tables, especially when you're dealing with tables that have many columns. -Normally, when you insert data into a table, you have to make sure the columns in your SELECT statement match the order of columns in the table. This can be tricky and time-consuming, especially if the order changes frequently. -INSERT BY NAME simplifies this by automatically matching the columns in your SELECT statement to the columns in the table based on their names. You don't need to worry about the order of the columns- Databricks handles that for you. Benefits in Daily Use You don't need to manually line up columns, reducing the chances of making mistakes. -Your SQL code is cleaner and easier to understand. -You spend less time worrying about column order, which speeds up your work. Real Life Example Imagine you have a table called SalesData with a lot of columns. If the order of columns in your source data changes often, using INSERT BY NAME saves you from having to adjust your SQL every time, making your job easier and less prone to errors. #Databricks #sql

What are Session Variables in Databricks and how to use them to improve your SQL workflows? SQL Session Variables are a powerful addition to Databricks. They can greatly enhance the efficiency and readability of your SQL workflows. Unlike parameter markers, session variables allow you to keep scalar values within your SQL session. This means you don't have to pass data back and forth through dataframes. It significantly simplifies your queries and helps maintain state between different SQL operations. Key Features: 1️⃣ Declare Variables: You can declare a session variable with a specific type and an initial default value. 2️⃣ Set Variables: Assign values to variables based on the result of SQL expressions or queries. 3️⃣ Reference Variables: Use these variables in any query or DML statement within the session. This approach helps to break up complex queries into manageable parts and efficiently pass state from one query to another. #dataengineering #databricks #WhatsTheData

Views are one of the most important concepts in SQL. Databricks has different categories of views and all have their different use-cases. -> View is a read-only object composed from one or more tables and views in a metastore. -> View can be created from tables and other views in multiple schemas and catalogs. -> View can significantly improve query performance, simplify complex data models, and enhance data security in your Databricks environment. The types of views are listed below : 1. Standard Views 2. Temporary Views 3. Global Temporary Views 4. Materialized Views 5. Dynamic Views #sql #views

🌟 Big News: Brewit (YC W23) now supports Databricks SQL warehouse! You can connect to your Databricks SQL warehouse within a few clicks and start using natural language to query your database and visualize data. Getting Started: 1. Go to “Data Sources” in your dashboard and create a new data source. 2. Select “Databricks” and fill in the database credentials. 3. Test your connection and start to chat with your data! Thanks Leah M. and Nina Williams for facilitating this process! Stayed tuned for our next announcement with Databricks 👀

🚀 Optimize Your Spark SQL Queries 🚀 Maximize the performance of your Spark SQL queries with these optimization tips: 1. Broadcast Joins 📡: i. Use broadcast() to optimize joins with small tables. ii. Reduces shuffle time by broadcasting small tables to all executors. 2. Predicate Pushdown 🔍: i. Enable predicate pushdown to reduce the amount of data read. ii. Improves performance by filtering data early in the query plan. 3. Caching Tables 💾: i. Cache frequently accessed tables to avoid repeated computation. ii. Use cacheTable() or persist() to store tables in memory. 4. Columnar Storage 📊: i. Use Parquet or ORC formats for better compression and faster reads. ii. Leverage columnar storage benefits by specifying spark.sql.parquet.enableVectorizedReader=true. Implement these strategies to make your Spark SQL queries run faster and more efficiently! 🌟 For more such content on big data, follow me and stay tuned! 🌐🔔 #ApacheSpark #SparkSQL #BigData #PerformanceOptimization

How to Evaluate your Repeat/Loyal Customer Rate with BigQuery #BigQuery #MarketingAnalytics #SQL

🚀 Boost Query Performance with Materialized Views in Databricks SQL Materialized views (MVs) in Databricks SQL allow you to speed up query performance by precomputing and storing the results of complex queries. Instead of recalculating the data for each query execution, materialized views serve up pre-aggregated or pre-joined data, reducing query time dramatically! Here’s how materialized views can help: 1. 💡 Optimized Performance: With MVs, you can cache the results of frequently executed queries, reducing resource usage and time. 2. ⏱️ Faster Query Execution: Ideal for dashboards and reports that need real-time performance with minimal lag. 3. 🔄 Automatic Refresh: Ensure your materialized views stay up-to-date with the underlying data through scheduled refresh options. 🔗 Check out Databricks documentation on how to create and use Materialized Views effectively: [Databricks SQL Materialized Views Documentation](https://lnkd.in/dSeXyhij) #Databricks #DataEngineering #SQL #MaterializedViews #BigData #DataOptimization #LinkedInTech #DataAnalytics

Get started with Data Analysis on Databricks Now, I am able to: Describe how Databricks SQL is used by data practitioners Create and manage SQL Warehouses Configure users and data access privileges Create and configure Databricks SQL alert destinations Create and run queries in Databricks SQL Create visualizations and dashboards Create alerts

🔧 Working with Delta Lake Tables: A Quick Guide 📊 Here’s how you can effectively set up, manage, and interact with Delta Lake tables: 🛠️ Setting Up: Start by setting your active catalog in Databricks to hive_metastore. 🆕 Creating Tables: Create a Delta Lake table using standard SQL commands, specifying Delta Lake as the storage format with USING DELTA. 🔍 Catalog Explorer: Use the Catalog Explorer in Databricks to view and interact with your table. Navigate through the database to find and inspect the product_info table. 📈 Inserting Data: Add data to the table with the INSERT INTO command. Each insert creates new data files in the table directory. Use SELECT * FROM product_info to view the inserted data. 📂 Exploring Table Directory: Check table metadata and file structure to see details about your table and view contents of the table directory, including data files and the _delta_log directory. 🔄 Updating Tables: Update data in the table using the UPDATE command. Delta Lake manages updates by creating new files while preserving old ones for historical reference. Verify file consistency with DESCRIBE DETAIL. 🔑 Key Points: Delta Lake tables are managed using standard SQL with Delta-specific options. The Catalog Explorer helps you view and interact with table structures and data. Data insertions and updates are tracked in the table directory and transaction log. Explore metadata and file structures using SQL commands and Databricks file system commands. #DeltaLake #DataManagement #Databricks #SQL #BigData #DataEngineering #DataUpdates #DataStorage

I have to store data from a temp view in databricks using spark SQL to a dataframe in comma seperat Check it out: https://lnkd.in/gb_r_3xM Join the conversation! #apachesparksql #databricks