Sagar Prajapati’s Post

I’m excited to share that I’ve successfully completed the Databricks certified Data Engineer course on Udemy, taught by the knowledgeable Derar Alhussein! 🥇 📈 📊 During the course, I gained a solid understanding of Databricks, a powerful multi-cloud Lakehouse platform built on Apache Spark. I learned how to work with Databricks for building efficient ELT processes using Spark SQL and Python, as well as implementing incremental data processing and production pipelines. Additionally, the course gave me insights into data governance, helping to ensure that data is secure, compliant, and well-managed. Databricks is truly transforming how organizations process and analyze data at scale, enabling seamless collaboration across data engineering, data science, and analytics. I’m excited to apply these skills to drive real-world data solutions. #Databricks #Lakehouse #ApacheSpark

🌟 Master Databricks: Your Path to Data Mastery! 🌟 In the rapidly evolving world of data engineering and analytics, mastering Databricks can be a game-changer! This powerful platform combines the best of Apache Spark with collaborative features that enable teams to unlock insights faster than ever. Here’s how you can embark on your journey to Databricks mastery: 1️⃣ Get Started with the Basics: Familiarize yourself with the core concepts of Databricks and Apache Spark. Understand how to set up a workspace, create clusters, and navigate notebooks effectively. 2️⃣ Hands-On Practice: The best way to learn is by doing! Use the Databricks Community Edition to experiment with notebooks, run Spark jobs, and explore different data sources. Dive into real datasets to see how you can manipulate and analyze data efficiently. 3️⃣ Leverage Learning Resources: Explore the wealth of resources available: • Official Databricks Documentation: A comprehensive guide to all features and functionalities. • Online Courses: Platforms like Coursera, Udemy, or Databricks Academy offer courses tailored to various skill levels. 4️⃣ Master Delta Lake: Understand how to use Delta Lake for data lakes. Learn about ACID transactions, schema enforcement, and time travel features. Delta Lake enhances data reliability and performance! 5️⃣ Optimize Your Workflows: Explore the power of jobs and workflows within Databricks. Learn how to schedule and automate data processing tasks, ensuring efficiency and reliability in your data pipelines. 6️⃣ Collaborate and Share: Databricks fosters a collaborative environment. Work with peers, share notebooks, and utilize version control features to enhance teamwork and project outcomes. 7️⃣ Stay Current with Updates: Databricks is continually evolving. Follow their blog and join community forums to stay updated on new features and best practices. 🌈 Motivational Tip: Remember, every expert was once a beginner! Embrace the learning process, and don’t hesitate to reach out to the community for support and guidance. Your efforts will pay off as you unlock new data possibilities! Are you ready to harness the full power of Databricks? Let’s embark on this exciting journey together! 💪 Pavan K. Python Databricks #Databricks #DataEngineering #ApacheSpark #LearningJourney #DataAnalytics #DeltaLake #CareerGrowth #BigData

Databricks Certifications: Test Your Knowledge! 🚀 ↪️ Exam: Data Engineer Professional ↪️ Section: 6 - Testing & Deployment ❓What is the purpose of the `databricks workspace` command? A. To create a new cluster B. To install a Python library C. To list the files in a workspace D. To delete a file in a workspace Curious about the answer? Ready to ace your Databricks certification? Unlock all the answers and boost your chances of success with my Databricks certification prep exams on Udemy: https://lnkd.in/d4_xhR8U 🔹 Follow me for daily Databricks certification questions and other great data engineering content! #databricks #databrickscertified #dataengineer #dailyquiz

Databricks Certifications: Test Your Knowledge! 🚀 ↪️ Exam: Data Engineer Professional ↪️ Section: 6 - Testing & Deployment ❓What is the purpose of the `databricks fs` command? A. To create a new cluster B. To install a Python library C. To upload a file to DBFS D. To download a file from DBFS Curious about the answer? Ready to ace your Databricks certification? Unlock all the answers and boost your chances of success with my Databricks certification prep exams on Udemy: https://lnkd.in/d4_xhR8U 🔹 Follow me for daily Databricks certification questions and other great data engineering content! #databricks #databrickscertified #dataengineer #dailyquiz

I recently passed my Databricks Data Engineer Associate Exam with a score of 97.77%! Having worked with Databricks for 4 years, I decided to pursue certification. I utilized two main resources: 1. Derar Alhussein's Udemy course, which provided a concise overview, covering topics of Delta Lake, PySpark, Structured Streaming, Delta Live Tables, Workflows and Unity Catalog. Its short but very well structured. 2. Databricks own Data Engineer Associate self-training, offering in-depth video lessons, notebooks, and slides. I would recommend doing this as well if you want to learn beyond certification I also used practice tests from Derar Alhussein, which were beneficial for reinforcing concepts. Overall, I'm excited to continue my journey with Databricks! Next One - Databricks Data Engineer Professional Any tips and guide for this? I hear this one is difficult but as I am already preparing would love to hear from anyone of you. Also, if anyone is preparing for the Associate Exam, please feel free to reach out for any questions. #databrickscertified #databricks #dataengineer

I wanted to try something beyond GCP 😉, so I decided to dive into the world of Databricks! I'm happy to share that I've passed the 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐀𝐬𝐬𝐨𝐜𝐢𝐚𝐭𝐞 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 with a whopping 92% score! 🏆✨ This exam covers: ⭕ Databricks Lakehouse Platform ⭕ ELT With Spark SQL and Python ⭕ Incremental Data Processing ⭕ Production Pipelines ⭕ Data Governance This was a short but rewarding journey where I explored the powerful Databricks ecosystem. Things that I liked about Databricks: ⚙️ The Spark/SQL-based flexible coding style that makes data processing efficient and intuitive. ⚙️ The impressive DLT pipelines, which simplify pipeline creation—almost like declaring materialized views but with much more flexibility and power. ⚙️ And of course Notebooks with seamless collaboration between data folks/teams. Excited to continue exploring and leveraging the capabilities of Databricks in my data engineering journey! #Databricks #DataEngineering

💥💥 #AzureDatabricks free resources to learn 💥💥 🔔 #MachineLearning with #AzureDatabricks ✔ Implement a machine learning solution with Azure Databricks DP-3014 - Training | Microsoft Learn ❄ #DataEngineering with #AzureDatabricks ✔ Implement a data lakehouse analytics solution with Azure Databricks DP-3011 - Training | Microsoft Learn 🔥 Wizard Driven AI Anomaly Detection with #Databricks in Azure ✔ Wizard Driven AI Anomaly Detection with Databricks in Azure > Designing for Citizen Data Scientists | Class Central Classroom 📢 Master Databricks and Apache Spark Step by Step: #PySpark Using RDDs ✔ Master Databricks and Apache Spark Step by Step - PySpark Using RDDs > Introduction | Class Central Classroom 💧 #AzureDatabricks using Python with #PySpark ✔ Azure Databricks Using Python With PySpark > Introduction | Class Central Classroom 🎁 Tech Talk: Top Tuning Tips for Spark 3.0 and #DeltaLake on #Databricks ✔ Tech Talk - Top Tuning Tips for Spark 3.0 and Delta Lake on Databricks > Welcome | Class Central Classroom ❌ Hope this is "helpful" 😊 🚀 I frequently post about various topics related to #MachineLearning, #DataScience, #BigData, #ArtificialIntelligence, etc, #Cloud_Materials, #Demos and #Training opportunities, 💎 So if you find it interesting don't forget to 🍁 follow me, 🍀 Leave your comment and share the content 😍 with your network for a better reach ✅ #azure #microsoft #hadoop #bigdata #businessintelligence #amazon #agile #scrum #datawarehouse #datalake #dataengineering #databricks #spark #artificialintelligence #machinelearning #datascience #python #AzureAutoML #azurebigdataengineer

Happy to share that I have completed my Databricks Data Engineer Associate Exam. Ended my long stream preparation with action, awaited to complete this for a long time. Thanks for the awesome course, Derar Alhussein which helps to have an understanding of data bricks from a data engineering perspective with clean and crisp contents,  👉 Databricks Lakehouse Platform 👉 ELT with Spark SQL and Python 👉 Incremental Data Processing 👉 Production Pipelines 👉 Data Governance   The next preparation for the professional one started, hoping to be completed soon by keeping consistency in learning.

Why am I doing it for FREE 🤔 Someone asked me this in comments on Databricks playlist 👉 When I was learning Data Engineering, there was no stuff which taught all the basics and background. The course that existed only explained code but not the reason and way of working. Even today most Paid courses do the same. I don't want beginners or learners to face the issues, that I faced. And, it makes me a better Data Engineer as I have to research a lot before recording a video ☺️ Even now, I occasionally revisit my video to refresh my memory on concepts I've forgotten 😅 Some influencers said - "It's free because it's not valuable" If it's Free that doesn't mean it's not valuable. It's free because I chose it to be free for all ! I am not doing it for free, I am gaining your love and support as reward ❤️ Plus a little ads revenue to keep the channel up, live and for motivation 👍 If you want to checkout the Databricks YouTube Playlist, checkout the link in the comments below 👇 Follow Subham Khandelwal for more content on Data Engineering ❤️ #spark #dataengineering #databricks

🚀 From Theory to Practice: Hands-on Learning with PySpark on Databricks 🌟 This past week, I've been immersed in a PySpark learning journey! A huge thanks to Manish Kumar sir's YouTube playlist for providing an exceptional foundation. But as they say, theory is only half the battle. To truly solidify my understanding, I dove headfirst into hands-on practice using Databricks. Here's a glimpse of the key PySpark concepts I tackled: 🔍 Data Wrangling: Mastering data input/output, schema management, and cleaning techniques to ensure top-notch data quality. 💾 Storage and Retrieval: Leveraging Parquet files for efficient storage and employing partitioning/bucketing for lightning-fast data access. 🔄 Data Manipulation: Transforming DataFrames with various operations and mastering core concepts like RDDs, transformations, and actions. 💼 SQL Power: Exploring Spark SQL for effective querying of structured data. 🎯 Advanced Functionality: Delving deep into operations like union, union all, conditional logic, handling duplicates, aggregate functions, group by, joins, and window functions. By combining video tutorials with real-world practice on Databricks, I now feel confident in my ability to tackle even the most complex Big Data challenges with PySpark. #PySpark #DataScience #Learning #BigData #Databricks #ApacheSpark #DataEngineer #DataEngineering