Shaik Abdul Haffeez

Read our new Blog on - "Apache Spark: RDD vs. DataFrame vs. Datasets" https://lnkd.in/gSXPxEhm A popular and effective distributed computing platform for handling massive data is called Apache Spark. Developers using Spark have three primary choices for processing data: DataFrame, Dataset, and RDD (Resilient Distributed Datasets). The variations between these choices, as well as their advantages and disadvantages, will be discussed in this article. For more detailed Blog, please refer to the our Website's Blog Section. https://lnkd.in/gSXPxEhm #TBC #ApacheSpark #BigData #DataScience #SparkRDD #SparkSQL #DataFrame #DataSet #DataEngineering #DataAnalysis #websitetraffic #onlinespeed #Windows11 #Windows10Pro #Angular #GooglePlay #agile #s #socialmedia #CyberThreats #digitalwellbeing #csharp #DotNet #softwaredevelopment #programming #vr #coding #webdevelopment

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We're thrilled to announce our latest YouTube tutorial on How to Create a Free Account in Databricks Community Edition! If you're looking to get started with Databricks, this video is the perfect guide for you. Whether you're new to Databricks or looking to explore its capabilities without any cost, this video will help you get up and running quickly and efficiently. Don't forget to like, comment, and subscribe for more tutorials! 📺 Watch the full video here: https://lnkd.in/gk_RtAFp #Databricks #DataScience #BigData #MachineLearning #TechTutorial #CommunityEdition #DataAnalytics #FreeResources

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Here are some recently asked Databricks interview questions 👇 ✅ What is the architecture of Databricks, and how do its components work together? ✅ How does Databricks support collaborative data exploration and visualization? ✅ What is the process of creating and managing Spark DataFrames in Databricks? ✅ How do you implement and track a machine learning model in Databricks? ✅ What are the key performance optimization techniques in Databricks? ✅ How do you schedule, manage, and monitor jobs in Databricks? ✅ What is Delta Lake, and how does it improve data reliability and performance in Databricks? ✅ What are the advantages of using Databricks over traditional Apache Spark setups? ✅ How do you ensure data security and compliance within a Databricks environment? ✅ How would you handle schema evolution in Delta Lake while processing data in Databricks? 🚨 Winter Sale is Live now on Grow Data Skills for all BootCAMPS ❄️ Grab best offers to kick start your journey as top notch Data Professional with most affordable & industry grade content 👇🏻 👉 Explore All Courses Here - https://lnkd.in/gjEGX5d4 🔗 Use Code "WINTEROFF"  🚀 Dedicated placement assistance & doubt support 📲 Call/WhatsApp for any query (+91) 9893181542 #dataengineering #Databricks #AWS #GCP #Azure #bigdata #datapipelines #dataanalytics Grow Data Skills Shashank Mishra 🇮🇳

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🚀 Master Nested JSON Integration: Read from Azure Storage in Databricks Like a Pro! Unlock the full potential of your data with our latest blog post on reading nested array JSON from Azure Storage using Databricks. Learn step-by-step methods, best practices, and insider tips to streamline your data workflows and enhance productivity. This guide is perfect for data engineers, analysts, and enthusiasts looking to efficiently process and analyze complex JSON structures. Read the full blog here: https://lnkd.in/dZvrTpWb Let's discuss and share ideas on how we can make data processing smoother and more effective. Feel free to leave your thoughts, questions, and feedback in the comments #DataEngineering #BigData #Azure #Databricks #JSON #DataIntegration #DataAnalytics #TechBlog #DataScience #DataProcessing #TechInsights

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Here are 10 Apache Iceberg interview questions for data engineering roles 👇 ✅ What is Apache Iceberg, and how does it differ from other data lake table formats like Parquet or ORC? ✅ How does Apache Iceberg handle schema evolution, and why is it beneficial in a data lake environment? ✅ Can you explain how partitioning works in Apache Iceberg and how it optimizes query performance? ✅ What is the role of metadata in Apache Iceberg, and how does Iceberg manage metadata differently from traditional file formats? ✅ How does Apache Iceberg support time travel queries, and what are the use cases for this feature? ✅ What are the key advantages of Apache Iceberg in terms of data versioning and snapshot isolation? ✅ How does Apache Iceberg integrate with query engines like Spark, Flink, or Hive? ✅ What are the benefits of using Apache Iceberg for large-scale data ingestion and management in a cloud-based environment? ✅ How does Iceberg handle data compaction, and why is it important for performance optimization? ✅ What are the best practices for using Apache Iceberg in a multi-tenant data lake architecture? 🚨 Join my high quality, industry & modern tech stack driven (AWS, GCP, Snowflake, Databricks, Flink, Iceberg, Hudi and so on) and practical project driven Data Engineering 4.0 With AWS 👇 👉 Enroll Here - https://lnkd.in/d4SS8JTm 🚀 Live Classes Starting on 9-Nov-2024 📲 Call/WhatsApp for any query (+91) 9893181542

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Our last poll was about data and transparency. Today, Databricks has created The State Reader API , a new capability to help users have access and analyze internal state data for full transparency and ease of use __ Will this new capability make a real difference from what we are used to use so far? #Databricks #Newcapability #datatransparency #accessibility #easeofuse #nuboTed

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💡 Unlocking AI with Snowflake Cortex AI, Snowpark, and Streamlit Revefi recently hosted a live deep dive session into Snowflake Cortex AI with Shankar Narayanan SGS and Vivekanandan Srinivasan the authors of the book “Ultimate Guide to Snowpark". During this session, Girish Bhat and the authors explored how Snowpark simplifies AI workflows and how Streamlit allows fast app development for data teams. 🌟 Read the full blog for insights: https://lnkd.in/g8yD8eMN

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In Apache Spark, RDD, DataFrame, and Dataset are the core data structures. Each of these offers different levels of abstraction and optimization. detail at https://lnkd.in/gDfyjkUB

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A common misconception with AI and machine learning: “all of the resources, infrastructure and technology" it takes to get started. With Databricks ML Flow, that’s simply not the case. In the video ⬇️ Kenny Shaevel breaks down how Databricks makes it easier to manage and track your models while ensuring full transparency, traceability, and governance—without the headache. Kenny covers everything from: - Simplifying Machine Learning Implementation - Utilizing AutoML for Quick Data Evaluation - The Importance of Feature Engineering - Leveraging Feature Store for Reusability - Governance and Traceability with the Model Registry - Seamless Integration and Application of Models Excited for more? Kenny will be diving even deeper into Databricks in our upcoming live stream series. Keep an eye out for details coming soon! 👀 #machinelearning #databricks #dataanalytics #MLFlow #AutoML #featurestore #dataengineering #techinnovation

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Discover the power of metadata management in Databricks with Apache Hive Metastore (HMS). In his latest blog, "Hive Metastore (HMS) and hive_metastore in Databricks", Gentrit Mehmeti explores how Hive Metastore supports large-scale data organization and enhances compatibility with Apache Spark. Gentrit also highlights the evolution of data governance with Unity Catalog, which offers advanced features like auditing and improved access control, shaping the future of data management. Learn how mastering metadata is key to handling today’s data complexity. 🖇 https://lnkd.in/dZrvDnnc #DataManagement #Databricks #ApacheHive #Metadata #UnityCatalog #CloudComputing #ApacheSpark

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Have you ever struggled with really big tables - billions of billions of records? Have you wondered what partition keys to use and felt the pain of having to rewrite your table if your partitioning strategy needed changing? Liquid clustering eliminates the need for this. 🚀 Liquid clustering is a game-changing feature revolutionizing data management in Databricks. It eliminates the constraints of traditional partitioning on Delta, offering unparalleled adaptability for your data strategy. 👉 Simplifies data layout decisions 👉 Allows flexible redefinition of clustering keys without data rewrites 👉 Enables data layout evolution alongside changing analytic needs It's really worth using! Check it out and don't miss out on optimizing your data flows by up to 12x. Learn more about how Liquid Clustering can transform your data management: #DataManagement #Analytics #BigData #LiquidClustering #Databricks

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👨‍💻 Databricks is a unified analytics platform designed to accelerate innovation by bringing together data science, engineering, and business teams to collaborate and derive insights from data effectively. 👩‍💻 💭 Databricks provides a cloud-based platform built on Apache Spark, which enables organizations to process and analyze massive volumes of data quickly and efficiently. 🌟 It offers a unified workspace where data engineers, data scientists, and analysts can work collaboratively using languages like SQL, Python, R, and Scala. 🤩 Databricks offers several certifications designed to validate expertise in various aspects of its platform.These certifications 📜 demonstrate your proficiency in using Databricks to solve real-world data challenges and can enhance your credibility and marketability in the data analytics and machine learning domains. 💡 Depending on your role and career goals, you can choose the certification that best aligns with your expertise and interests. 🚀 Here are some notable certifications you can pursue 🚀 Check out the study materials at Whizlabs 🧡 ☁ #Databricks #databricks #cloudcomputing #software #whizlabs #technology #certifications #cloudservices

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Scheduled Query Execution Report in Snowflake Notebooks with Streamlit Chanin Nantasenamat, PhD (aka Data Professor), Snowflake Senior Developer Advocate, walks you through the process of building a notebook that reports on failed or long-running scheduled queries, thereby providing insights into reliability issues. Here's a breakdown of the steps: 1. Retrieve Data 2. Convert Table to a DataFrame 3. Create an Interactive Slider Widget & Data Preparation 4. Create a Heat Map for Visualizing Scheduled Query Execution 📺 Watch the Tutorial: https://okt.to/TUSfnK 📓 Demo Notebook: https://okt.to/7SBmgA

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🔒 Fine-Tune LLMs with Differential Privacy in Databricks   Databricks simplifies the specialization of foundation models and offers a range of tutorials to showcase their capabilities. However, standard fine-tuning doesn't guarantee #dataprivacy. This is where #DP fine-tuning comes into play, providing the necessary privacy assurances. In our latest project, we demonstrate the ease of using Sarus and Databricks to fine-tune an open-source model without worrying about training set leakage. We compared models fine-tuned with and without DP (Epsilon 3, Delta 1e-5). Using GPT-4 to judge the answers' correctness and similarity, we found that in 90% of cases, the models achieved the highest grades. While DP models had slightly lower mean utility, they offered guaranteed privacy! This approach ensures robust model fine-tuning while safeguarding sensitive information. 🚀 If you're a Databricks user looking to harness the power of LLMs without compromising on privacy, let's connect! #GenAI #Databricks #MachineLearning #LLM https://lnkd.in/euRPtYid

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𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀' 𝗙𝗮𝗻𝘁𝗮𝘀𝘁𝗶𝗰 𝟰 Here are the top 4 Databricks features that are revolutionizing Generative AI deployments for businesses. But these are just the tip of the iceberg. To learn more about all the Databricks features, read our latest eBook "Mastering Generative AI with Databricks."   Some of the top features include: ➡️ LLM Fine-Tuning: Tailor Gen AI to your business needs ➡️ Databricks Vector Search: Unlock intelligent data retrieval ➡️ Unity Catalog: Ensure compliance and traceability ➡️ Delta Sharing: Accelerate global AI deployment Designed for executives, our eBook is packed with practical insights and real-world examples to help you harness Gen AI for growth, creativity, and optimized operations.   https://lnkd.in/d-3ssTGr   #GenerativeAI #Databricks #AIDevelopment 

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Snowflake Optimization - Summary of the week 1. Remove failing workloads Failing workloads are just using credits and not performing any real work. To identify them, use this: https://lnkd.in/gP8bgV-a 2. Identify unused storage Identify and remove unused storage. Here's a blog to identify: https://lnkd.in/gMTrHxmM 3. Set query timeouts Set appropriate query timeouts based on the size of the warehouse and the business use case. 4. Split or merge files before ingestion The appropriate file size for ingestion into Snowflake is 100-250MB. Split or merge files before ingesting to ensure the file size is correct. 5. Use deferred merge If the frequency of ingestion is high, use the concept of deferred merge. Here's a blog: https://lnkd.in/gk4jgz2g

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Thrilled to share an insightful blog post by Community Member Harshal Pagar on Running Databricks Notebook with Airflow. This article delves into the integration of Databricks Notebooks with Apache Airflow, offering a step-by-step guide on how to leverage this powerful combination for your data workflows. Whether you're looking to streamline your data processing or explore new capabilities, this post is a must-read! Kudos to the author for providing such valuable content. Let’s dive in, learn, and continue to innovate together! #ApacheAirflow #Databricks #DataEngineering #DataScience #BigData #DataPipeline

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