Cloud-Fundis’ Post

Another #Fabric Blog post! This time we want to introduce you to MSSparkUtils. MSSparkUtils for Microsoft Fabric allows data engineers, data scientists, and developers to use the full potential of Apache Spark within the Azure ecosystem. By providing a set of tailored utilities, MSSparkUtils boosts working with Apache Spark. 🌟 In this technical blog post, Tom Van de Velde explores the key features and benefits of MSSparkUtils, and how it can be effectively utilized to accelerate working with Spark on Azure Fabric. #FabricBlogPost #MicrosoftFabric #TechnicalFabricBlog

Through completing this lab, I've learned to Use Delta Lake with Apache Spark in Azure Synapse Analytics. This enabled me to perform data operations with relational semantics on top of a data lake. Exploring Delta Lake's functionality, I discovered its capability to process both batch and streaming data, paving the way for the creation of a Lakehouse architecture with Spark. https://lnkd.in/eGJ9xpNM #Azure #DataEngineering #DeltaLake

Hello Linkedin Family! Excited to share the recent learning from my experience!!! 🚀 Speeding Up Data Processing in Azure Databricks with PySpark and ThreadPoolExecutor! 🚀 Harnessing parallel processing in Azure Databricks with PySpark and Python’s ThreadPoolExecutor can significantly improve efficiency, especially for tasks that benefit from multithreading. Recently, I implemented this setup to parallelize function execution across Spark partitions—leading to faster, more efficient workflows. 🔥 How it Works: Use ThreadPoolExecutor to manage and execute functions across multiple threads. Run tasks on each Spark partition in parallel, cutting down processing time for complex operations. Example: python Copy code from concurrent.futures import ThreadPoolExecutor # Define the function to be applied to each partition def process_partition(partition_data): # Simulate data processing return [process_item(item) for item in partition_data] # Apply the function in parallel across Spark partitions rdd = spark.sparkContext.parallelize(data) results = rdd.mapPartitions(lambda partition: ThreadPoolExecutor().map(process_partition, partition)).collect() Result: Reduced latency, faster execution, and scalability for large datasets—ideal for real-time data processing in the cloud! ☁️💡 #AzureDatabricks #PySpark #ParallelProcessing #DataEngineering #BigData

5 Tips to Optimize Your Azure Databricks Workflow🚀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.🌟 💬 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

Are you ready to embark on your data engineering journey? Let us help you streamline your ability to manage datasets seamlessly and efficiently, using Google Cloud Platform (GCP) services, Python and Kaggle. In our recent blog, Matt Yao, Lead Software Engineer at DiUS - tackles real world data concepts, in the educational step by step instructive blog below 👇 This one gets into the nuts and bolts, so aspiring Data Engineers, this one's for you. You’ll be running commands before you can say BigQuery. Find it here: https://lnkd.in/dS7wHCXZ

🚀 Our wait is finally over, In Databricks we can now perform recursive directory listing, pattern-matching for files, and many more operations for DBFS and external file systems like ADLS, Azure Blob Storage, AWS S3, Google Storage, and many more. Below is the complete list of new operations we can perform: ✅ Perform recursive directory listings 🔄 ✅ Match files based on specified patterns 🎯 ✅ Conduct case-sensitive or case-insensitive file pattern matches 🔤 ✅ Filter listings to display only directories, only files, or both 📂📄 ✅ Generate sorted outputs for easier analysis and management 📊 https://lnkd.in/dJFJkcri The code is open-source and available on GitHub: https://lnkd.in/dMnBn_MG #Databricks #dataengineering #BigData #python #Programming #coding Databricks Data Engineer Things

Mastering Data Ingestion with PySpark: Handling Multi-Source Pipelines with Precision Dealing with CSV, JSON, and SQL in a single pipeline can feel like trying to herd cats—each format behaves differently. For my latest project, I used PySpark’s connectors to pull from Azure Blob Storage, Event Hub, and SQL Server, efficiently processing large datasets. PySpark’s ability to scale and optimize these reads, especially with partitioning, is a game changer for performance. And let’s be honest—if it weren’t for PySpark, I’d probably still be manually wrangling those files. 😅 #Azure #DataEngineering #PySpark #DataIngestion #BigData

Just wrapped up another exciting week at Data Engineering Zoomcamp with @DataTalksClub: Integrated Mage AI to process NYC taxi trip data 🚕 Successfully managed data exports to Google Cloud Storage ☁️ Delved into BigQuery for table creation and data partitioning 📊 Gained invaluable insights on Data Warehouses Pushed my Python and SQL abilities to new heights 🐍🔍 One third down, two more to go. The learning journey continues ! #dezoomcamp #datatalks #dataengineering

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

Excited to share a new blog post on the Google Cloud blog featuring our the work from Giulia Carella and many other people at CARTO and Google Cloud In this post, we look into how BigQuery DataFrames and CARTO can be used together for advanced geospatial analysis directly within Jupyter notebooks. This integration allows data scientists to leverage the power of BigQuery’s engine using familiar Python syntax, eliminating the need for data transfers and enhancing efficiency. And that matters a lot for maps! We provide a detailed example of building a composite indicator for climate risk and healthcare accessibility across the US. By combining datasets like ERA5 temperature data, walking accessibility to healthcare services, and PM2.5 concentration data, we illustrate how to create powerful visualizations and conduct in-depth spatial analysis using pydeck-CARTO. https://lnkd.in/dDXsG6Ns