GetYourGuide’s Post

The ‘𝗙𝗼𝗿 𝗘𝗮𝗰𝗵’ task is now available in 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀! 🎉 This allows for a variety of input formats like key-value pairs, JSON, strings, and numbers. 

" 5 Reasons Why Databricks is Essential for Data Engineers " 🌟 Databricks is more than just a data platform; it’s an innovation hub for data engineers. Here’s why: 1️⃣ Unified Platform: Simplifies data integration by combining ETL, analytics, and machine learning in one ecosystem. 2️⃣ Delta Lake: Offers ACID compliance, time travel, and schema enforcement for reliable and scalable data lakes. 3️⃣ Collaborative Workspace: Seamlessly integrates notebooks for Python, SQL, and Scala, enabling teams to collaborate effectively. 4️⃣ Optimized Data Processing: Accelerates big data processing with Spark and Delta Engine, ensuring real-time insights. 5️⃣ Scalable Infrastructure: Automatically scales clusters based on workload, optimizing cost and performance. 🎥 Check out this https://lnkd.in/g9_-34qY for a deeper dive into how Databricks empowers modern data engineering! 🏷️Tagging Shashank Singh 🇮🇳 Priyanka Banerjee Manali Kulkarni R GANESH Korrapati Jaswanth Venkata Naga Sai Kumar Bysani for better reach. #databricks #dataengineering #muvidatalab #datalake #deltalake #bigdata #dataanalytics #spark #etl #dataplatform #collaboration

Data Engineering 101: Day 12 - Delta Today we have an incredible resource for all data enthusiasts, engineers, and architects out there - "Data Engineering 101 - Delta". This comprehensive guide dives deep into Delta Lake, covering everything from the basics to advanced concepts with practical PySpark examples. Happy Learning! Follow Shwetank Singh for more learnings... #dataengineering #upskill #gritsetgrow #interviewprep #data #interview #basics #Spark #Databricks #delta

Mastering the art of data engineering starts with strong fundamentals and a vision for the future. 🚀 On Day 3 of our Data Engineering series, we’re diving into the Essential Skills every data engineer needs—from SQL and Python to cloud platforms and orchestration tools. Let’s build the foundation for success! 💡 #DataEngineering #TechSkills #BigData

Error Handling in Data Ingestion: Saving Your Pipelines from Chaos Data ingestion pipelines are like road trips—one unexpected bump, and the whole journey can go off track. Effective error handling ensures your pipeline stays on course, even when things go wrong. Logging: Use structured logging (e.g., log4j or Python’s logging module in Databricks) to capture detailed error messages and pipeline events. These logs are your GPS for troubleshooting. Retry Mechanisms: Set up retries for transient errors like network issues or temporary file locks. In Databricks, you can configure retry policies in workflows to automatically handle these hiccups. Dead Letter Queues: For unprocessable records, route them to a dead letter queue (DLQ) for review later. This avoids halting the pipeline for a single bad record. Pro Tip: Always validate data at the source to catch common errors early—because no one wants to debug a million-row ingestion job to find one pesky NULL. #DataEngineering #ErrorHandling #DataPipelines #AzureDatabricks

Still a couple of days to take advantage of this 26% OFF offer and get yourself a book with a comprehensive discussion of modern Data Engineering and how to apply it with dbt, including a sample project with dbt Cloud and Snowflake. dbt and Snowflake has been shaping the modern data engineering landscape bringing on one side the best practices of software engineering to data and on the other side making available to anybody (including individuals and small companies) with no upfront investment the most powerful data platform that powers the innovative workloads in many Fortune 500 companies. This books discusses both the high level topics and then delves in the more technical details, down to the level code that you can take and bring to your projects.

 🚀 Master the Essentials of PySpark! 🔥 I've been diving deep into PySpark Fundamentals, and I'm excited to share some core takeaways that are essential for data professionals. From understanding RDDs and DataFrames to mastering transformations and actions, PySpark simplifies big data processing with unparalleled efficiency. This framework is perfect for tackling both batch and streaming data seamlessly. 🌟 💡 If you're looking to enhance your data engineering skills or transition into the big data world, PySpark is a must-learn! #PySpark #BigData #DataEngineering#LearningJourney#TechSkills

If you are wondering what data engineers do in day-to-day work in databricks and pyspark, here is a list of top things you should know. 1. Different types of compute and clusters 2. Different ways to run a notebook from another notebook and differences between them. 3. Creating and Using Widgets 4. Creating workflows 5. Reading from different file formats csv, json, parquet, delta and database 6. Most used transformations : adding columns, aggregates, joins, filter. 7. Writing dataframes to external location like data lake or database 8. Creating and managing delta tables 9. Using different PySpark sql functions 10. Using unity catalog Follow me : Yusuf Didighar as I bring you unique and immense amount of content on data analytics and azure data engineering. Glad to contribute to help the community, lets learn and grow! #databricks #pyspark #dataengineering

Embarking on the path to becoming a certified Spark developer with Databricks. Stay tuned for regular updates on this thrilling learning journey! Instead of traditional note-taking, I've decided to share my learning journey through daily Medium articles! Expect a fresh post every day (I’ll try), documenting insights, challenges, and triumphs. Let's embark on this learning adventure together! P.S.: If you have any useful resources or insights to share on data engineering, please drop them in the comments or DM! #DataEngineering #TechJourney #ContinuousLearning #MediumArticle #LinkedInConnect

I’m thrilled to announce that I’ll soon start sharing my real-time experiences and learnings around Data Engineering while working with dbt (Data Build Tool). As I dive deeper into this , I’m eager to contribute back to the community, helping others navigate the world of analytics engineering. Stay tuned for insights on: • Best practices • Common challenges (and solutions!) • How dbt is shaping modern data workflows. I plan to share also about GCP, Airflow, Bigquery and some more things. Looking forward to learning and growing together! #AnalyticsEngineering #DataEngineering #DataTransformation #LearningJourney