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5 Tips to Optimize Your Azure Databricks Workflow. 💪Spill Your Databricks Secrets!(I'll share mine too ) 🚀Hi #dataanalytics #dataengineering #AzureDatabricks fam, let's get those Spark clusters humming! Shall we? Here's how to level up your Azure Databricks game (Without the Headaches)😉: 1. CLI Power User: The Databricks CLI is your command-center from the comfort of your own terminal.😎 Upload data, tinker with notebooks, launch jobs... all without leaving your favorite coding environment.💻 2. Delta Lake Love: If you're not using Delta Lake, are you even Databricking? ACID transactions, schema enforcement – it's your data quality guardian.🛡️ It's your secret weapon for handling massive datasets and keeping things squeaky clean ✅. 3. Partition for Performance: Big datasets got you down? Partitioning your data strategically is like adding an express lane to your queries.🚗 Slice up those big datasets for faster queries. ⚡️ 4. Policy Patrol: Cluster policies are your friends. Set some rules, prevent rogue configurations, and keep your workspace running smoothly.📜 Avoid headaches.😌 5. Azure Monitor is Watching: No more wondering "what's going on in there?" 👀 Keep an eye on those clusters! Azure Monitor aids in tracking performance and spotting issues before they become full-blown problems.💡 Remember, Databricks is always evolving! Stay curious, stay ahead of the curve.🌟  Thursday challenge: 💬 What are YOUR favorite Databricks tips and tricks? Share below! 👇 Let's build a knowledge hub together. 🚀 Don’t forget to like, comment, share, repost, follow, and connect!🙌  #AzureTipsAndTricks #AzureDatabricks #DataEngineering #ThursdayShare #Spark #BigData #DataScience #SmartdataanalyticInc #smartdatalearning

Follow GritSetGrow for more Databricks learnings. #gritsetgrow #dataengineering

🚀 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨 𝐒𝐞𝐫𝐢𝐞𝐬 22: 𝐇𝐨𝐰 𝐭𝐨 𝐑𝐞𝐚𝐝 𝐋𝐚𝐫𝐠𝐞 𝐂𝐒𝐕 𝐅𝐢𝐥𝐞𝐬 𝐟𝐫𝐨𝐦 𝐀𝐳𝐮𝐫𝐞 𝐁𝐥𝐨𝐛 𝐒𝐭𝐨𝐫𝐚𝐠𝐞 𝐢𝐧𝐭𝐨 𝐚 𝐒𝐩𝐚𝐫𝐤 𝐃𝐚𝐭𝐚𝐅𝐫𝐚𝐦𝐞! Handling massive CSV files can be tricky, but with the power of Spark in Databricks, it becomes a breeze! Whether you're dealing with tons of data or just getting started with Azure, here's how you can efficiently load large CSV files from Azure Blob Storage and work with them in a Spark DataFrame. 💡 𝐊𝐞𝐲 𝐒𝐭𝐞𝐩𝐬: 1. 𝐒𝐞𝐭 𝐔𝐩 𝐀𝐜𝐜𝐞𝐬𝐬 – Make sure your Azure Blob Storage credentials are ready! 2. 𝐌𝐨𝐮𝐧𝐭 𝐭𝐡𝐞 𝐁𝐥𝐨𝐛 𝐒𝐭𝐨𝐫𝐚𝐠𝐞 (𝐨𝐩𝐭𝐢𝐨𝐧𝐚𝐥) – This step simplifies file access. 3. 𝐑𝐞𝐚𝐝 𝐭𝐡𝐞 𝐂𝐒𝐕 𝐅𝐢𝐥𝐞 – You can read it directly from Blob Storage or from a mounted path. Bonus: You can add extra customizations like defining your own delimiters, handling null values, or setting escape characters. 🔎 𝐒𝐮𝐦𝐦𝐚𝐫𝐲: Spark’s distributed computing capabilities make it incredibly efficient to work with large datasets, directly pulling in data from Azure Blob Storage. Have you tried this before, or is this something you plan to explore in your next data project? Let me know in the comments! 💬 Follow Shivakiran Kotur for more such scenario and more insight on DataEngineer! Do check out topmate for Dp 203 dumps and notes and connect with me for free! #Azure #Databricks #Spark #BigData #DataEngineering #DataScience

⏰𝗗𝗮𝘆 𝟭𝟐⏰ 🌐 𝐌𝐨𝐮𝐧𝐭 𝐀𝐃𝐋𝐒 𝐆𝐞𝐧𝟐 𝐨𝐫 𝐁𝐥𝐨𝐛 𝐒𝐭𝐨𝐫𝐚𝐠𝐞 𝐢𝐧 𝐀𝐳𝐮𝐫𝐞 𝐔𝐬𝐢𝐧𝐠 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 𝐀𝐜𝐜𝐞𝐬𝐬 𝐊𝐞𝐲 𝐚𝐧𝐝 𝐒𝐀𝐒 𝐭𝐨𝐤𝐞𝐧 🌐 In Azure Databricks, "mounting" refers to the process of making external storage accessible within the Databricks environment, allowing users to easily read and write data stored in services like Azure Blob Storage or Azure Data Lake Storage (ADLS). Key Points About Mounting 1.Mount Points: When you mount a storage location, it creates a mount point in the Databricks File System (DBFS) that you can reference like a local file system path. 2.Benefits:  Simplifies access to external data.   Allows for easier data management and integration within Databricks notebooks and jobs.  Enables better organization of data resources. 3. Common Storage Options:    Azure Blob Storage   Azure Data Lake Storage (Gen1 and Gen2) 🌟 𝔻𝕠 𝕤𝕙𝕒𝕣𝕖 & 𝕗𝕠𝕝𝕝𝕠𝕨 𝕗𝕠𝕣 𝕞𝕠𝕣𝕖 𝕔𝕠𝕟𝕥𝕖𝕟𝕥! Vinoth P ❤️ 🌟 #Databricks #spark #ApacheSpark #BigData #DataEngineering #DatabricksLakehouse #DatabricksSQL #DatabricksML #DatabricksCommunity #DatabricksForDataScience #DatabricksTraining #SparkStreaming #SparkSQL #SparkML #SparkArchitecture #DataAnalysis #DataVisualization #CloudComputing #AI  #DataPlatform #mount #SAS #accessKey 

Databricks Day 19: #dataengineeringin30days Register for a free webinar to get started with upskilling in Databricks: https://lnkd.in/gNbGPmwJ We have already discussed about the different concepts of Databricks, now its time to discuss about best practices which are as below: Caching: Utilize the cache() function to store frequently accessed DataFrame, Dataset, or RDD in memory to speed up repeated queries. Partitioning: Implement table partitioning to enhance query performance, especially on large fact tables, by using key columns such as country_code or market_code. Data Storage: Store data in Azure Blob Storage or Azure Data Lake Storage, structured in a way to minimize directory listing costs. Delta Lake Features: Leverage Delta Lake's OPTIMIZE and ZORDER functions to improve data co-locality and read efficiency. Auto Optimization: Enable features like Optimized Writes and Auto Compaction for staging tables to manage file sizing and compaction automatically. Query Performance Hints: Use SQL hints such as BROADCAST to suggest the optimal join strategy, reducing shuffle and improving query response times. Managing Temporary Data: Regularly clean up temporary tables and explicitly delete old metadata to maintain a tidy and efficient workspace. Adaptive Query Execution (AQE): Turn on AQE to enhance large query performance by adjusting Spark's execution strategies based on real-time workload characteristics. Security and Cost Management: Implement cluster autoscaling, use Azure Key Vault for secure credential management, and optimize cost by controlling cluster operation times and resource allocation. #gritsetgrow #databricks #dataengineering #azuredatabricks #upskill

🌐 🌐 🌐 𝐀𝐳𝐮𝐫𝐞 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 𝐰𝐢𝐭𝐡 𝐝𝐛𝐮𝐭𝐢𝐥𝐬! 🌐 🌐 🌐 💬💬dbutils is a utility library in Azure Databricks that simplifies common tasks related to data management, workflow automation, and integration with external services. It’s a crucial tool for enhancing productivity within Databricks notebooks. 💬💬Spotlight on dbutils.fs One of the most useful components of dbutils is dbutils.fs, which provides functions for file system operations within the Databricks File System (DBFS). Here are some key functions: 1.       dbutils.fs.ls(path): List files and directories at a specified path. Great for exploring your data! 2.       dbutils.fs.cp(src, dst): Copy files or directories from one location to another. Easy data duplication! 3.       dbutils.fs.rm(path, recurse=True): Remove files or directories, with an option to delete non-empty directories. Use wisely! 4.       dbutils.fs.mkdirs(path): Create new directories effortlessly. Keep your data organized! 5.       dbutils.fs.mount(source, mount_point): Mount external storage systems (like Azure Blob or AWS S3) to DBFS, making data access seamless. 6.       dbutils.fs.unmount(mount_point): Unmount a previously mounted storage system when you no longer need access. 🌟 𝔻𝕠 𝕤𝕙𝕒𝕣𝕖 & 𝕗𝕠𝕝𝕝𝕠𝕨 𝕗𝕠𝕣 𝕞𝕠𝕣𝕖 𝕔𝕠𝕟𝕥𝕖𝕟𝕥!Vinoth P ❤️ 🌟 #Databricks #spark #ApacheSpark #BigData #DataEngineering #DatabricksLakehouse #DatabricksSQL #DatabricksML #DatabricksCommunity #DatabricksForDataScience #DatabricksTraining #SparkStreaming #SparkSQL #SparkML #SparkArchitecture #DataAnalysis #DataVisualization #CloudComputing #AI  #DataPlatform #dbutils

Azure Databricks: Creating Your First Cluster 🚀 Welcome to Day 3 of our Azure Databricks journey! Today, we're diving into creating and configuring your first Databricks cluster. Let's get started! 💪 What's a Databricks Cluster? 🤔 • A set of computation resources and configurations on which you run data engineering, data science, and data analytics workloads Steps to Create a Cluster: 1. Access Cluster UI 🖥️   • In your Databricks workspace, click "Compute" in the sidebar 2. Create a New Cluster ➕   • Click "Create Cluster"   • Choose "All Purpose Cluster" 3. Configure Your Cluster ⚙️   • Cluster Name: Choose a descriptive name   • Cluster Mode: Select "Standard" for most use cases   • Databricks Runtime Version: Choose latest stable version   • Node Type: Select based on your workload (e.g., Standard_DS3_v2)   • Enable autoscaling: Toggle on for cost efficiency   • Terminate after: Set idle time for automatic shutdown 4. Advanced Options (Optional) 🔧   • Init Scripts: For cluster customization   • Spark Config: For fine-tuning Spark settings 5. Create and Start ▶️   • Click "Create Cluster"   • Wait for the cluster to start (usually takes a few minutes) Pro Tips: • 💡 Start small and scale up as needed • 🔒 Use Cluster ACLs for fine-grained access control • 💰 Leverage autoscaling and auto-termination for cost optimization Congratulations! You now have a Databricks cluster ready for action. In our next post, we'll explore Databricks notebooks and run our first code. #AzureDatabricks #BigData #CloudComputing #DataEngineering #Spark Questions about cluster configuration? Drop them below! 👇

My first points of feedback on the new For Each activity in Databricks Workflows. 🧱 ➡ There is no way to add multiple activities within the same For Each activity. ➡ There is no documentation available about how to effectively iterate through more complex json. The documentation only seems to contain simply looping through the main key values, not an array of strings for example. At least I haven't been able to find out. It seems you can only pass a simple array or list of strings. ➡ The input json can only contain 48Kib of text, meaning 49.000 characters. This can sometimes be limiting. To compare: Azure Data Factory allows up to 4Mib of input to iterate over. (I really dislike ADF but I have to give it to them). A workaround for this would be to retrieve more configuration values belonging to a key inside the loop itself. ➡ There is no easy way to catch return values outside of the iteration. ➡ When trying out the concurrency performance, I found that just running a simple notebook with a print statement in parallel takes at least 30 seconds per iteration to complete. Some even over a minute. 🐌 ➡ If one iteration fails, it fails the entire loop. I would like to be able to control this behavior. In my opinion these are the absolute basics of a robust and flexible looping mechanism, we need to do better. 😁 At this point, it would not be something I would implement in production. Looking forward to the next iterations of this functionality (see what I did there?). #databricks #dataengineering

Hey everyone, I just published a new blog post on the 9 things I wish I knew before I started using Databricks! Whether you're a seasoned pro or just starting out, these insights can help you navigate and optimize your Databricks environment more effectively and reduce future technical debt. As a customer for the last 5 years, I've learned some of these lessons the hard way and wanted to save others the learning curve. I'd love to hear your thoughts and feedback via the comments or a private message. Disclaimer: All opinions are my own and not official Databricks recommendations #DataEngineering #Databricks #BigData #TechTips https://lnkd.in/gzBAGQi2

I've completed the Run Azure Databricks Notebooks with Azure Data Factory module! This has helped me improve my data engineering and data science skills, and I can now build more complex data pipelines using Azure Databricks and Azure Data Factory together. I'm excited to continue developing my skills and building powerful data pipelines that will contribute to data-driven decision making. #AzureDatabricks #AzureDataFactory #DataEngineering #DataScience