yuan luo

At ByteDance, the engineering team has revolutionized the data landscape by constructing a cutting-edge data lake capable of handling exabyte-scale datasets using Apache Hudi. This remarkable achievement addresses numerous challenges in large-scale data management, offering significant insights for data engineer 👇🏻 🚨 Key Innovations and Solutions: ✅ Efficient Data Ingestion and Upsert Capabilities: ByteDance leveraged Hudi’s support for upserts to efficiently manage data ingestion. By integrating Hudi with their existing data pipelines, they achieved seamless data ingestion with the ability to update and insert records in real-time. This ensures data consistency and freshness across their vast datasets. ✅ Incremental Data Processing: Hudi’s incremental pull capability allowed ByteDance to process only the changed data, significantly reducing the processing time and resource consumption. This approach is crucial for handling the massive scale of data generated by ByteDance’s applications and services. ✅ Optimized Storage and Query Performance: Using Hudi's storage management features, ByteDance optimized their storage layout by clustering and indexing the data. This optimization resulted in faster query performance and reduced storage costs, enabling efficient data retrieval and analytics. ✅ Scalable Metadata Management: Managing metadata at an exabyte scale is a significant challenge. ByteDance adopted Hudi's advanced metadata management capabilities to handle large volumes of metadata efficiently, ensuring that the data lake remains performant and scalable. ✅ Real-time Data Lake Architecture: By implementing a real-time data lake architecture with Apache Hudi, ByteDance enabled real-time analytics and insights. This architecture supports various analytical workloads, from batch processing to real-time streaming, catering to diverse business needs. 🚨 Why Apache Hudi? Apache Hudi stands out for its robust framework designed to streamline data lake operations, particularly in environments requiring high-throughput data ingestion and real-time processing. ByteDance's successful implementation showcases Hudi’s potential in transforming data infrastructure to meet the demands of modern data engineering 🙌🏻 Reference - https://lnkd.in/gtTxhqCc 🚨 After months of planning finally we have launched our high quality BootCAMP "DevOps For BigData With CI/CD" ✌🏻 If you are an aspiring Data Engineer then you must checkout this, you won't be disappointed regarding affordability 😉 👇 👉 Enroll Here - https://lnkd.in/ghT9eUiz 🚀 Offline Doubt Support 📲 Call/WhatsApp for any query ( 91) 9893181542 #dataengineering #apachehudi #bigdata

177

5 Comments

Hello everyone, In the following link, you will discover the Databricks glossary, which encompasses a range of important subjects relevant not only to the Databricks environment but also to broader Data Engineering concepts. If you explore the links further, there are plenty of technical details that worth a glance :)

32

In his latest PySpark tutorial, Tom Reid turns to user-defined functions, explaining what they are, how they work, and when (and how) you should use them in your project.

18

There's still time to register for Distributed SQL Summit (DSS) Asia on April 24th!🙌 This year's event will focus on embracing a modern data ecosystem and simplifying database modernization.💡 PLUS, don't miss the keynote 'No Downtime, No Limits with YugabyteDB,' from Yugabyte co-founder, Karthik Ranganathan, who will dive into how distributed PostgreSQL is architected to meet the demands of modern cloud-native applications, as well as sharing how real-life customers are using YugabyteDB to power a range of business-critical applications. 👀 Discover more and reserve your place!⬇️ https://hubs.la/Q02sN1CB0 #DSSAsia #distributedsql #opensource #database #YugabyteDB

16

𝐀𝐜𝐞 𝐘𝐨𝐮𝐫 𝐏𝐲𝐒𝐩𝐚𝐫𝐤 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬 𝐰𝐢𝐭𝐡 𝐓𝐡𝐞𝐬𝐞 20 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞, 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨-𝐁𝐚𝐬𝐞𝐝 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬! 🚀 Are you preparing for PySpark interviews at top companies? Get ready with these hands-on questions designed to evaluate your real-world experience! 🔥 𝐋𝐞𝐭'𝐬 𝐛𝐫𝐞𝐚𝐤 𝐢𝐭 𝐝𝐨𝐰𝐧 𝐢𝐧𝐭𝐨 4 𝐤𝐞𝐲 𝐚𝐫𝐞𝐚𝐬: 1. 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐚𝐧𝐝 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 1️⃣ How would you handle large datasets in PySpark and optimize for performance? 2️⃣ How do you efficiently join two massive datasets (e.g., 100GB each) in PySpark? 3️⃣ Faced with millions of records, how do you identify and remove duplicates using PySpark? 4️⃣ Given a DataFrame with nested JSON, how would you flatten it in PySpark? 5️⃣ How do you handle missing or null values in a DataFrame? What strategies work best? 2. 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐓𝐮𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 6️⃣ How do you debug and optimize PySpark jobs that are taking too long to finish? 7️⃣ What is a shuffle operation in PySpark, and how do you minimize its performance impact? 8️⃣ Have you dealt with data skew in PySpark? What steps did you take to resolve it? 9️⃣ How do you optimize PySpark jobs in a YARN cluster environment? 🔟 What’s the difference between repartition() and coalesce()? When should you use each? 3. 𝐃𝐚𝐭𝐚 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 1️⃣1️⃣ How would you build an ETL pipeline in PySpark to process streaming data? 1️⃣2️⃣ How do you ensure data consistency and fault tolerance in PySpark jobs? 1️⃣3️⃣ You need to aggregate data from multiple sources and save it as a partitioned Parquet file. How would you achieve this? 1️⃣4️⃣ How do you orchestrate and manage a complex PySpark job with multiple stages? 1️⃣5️⃣ How do you handle schema evolution in PySpark while reading and writing data? 4. 𝐃𝐞𝐛𝐮𝐠𝐠𝐢𝐧𝐠 𝐚𝐧𝐝 𝐄𝐫𝐫𝐨𝐫 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 1️⃣6️⃣ Have you encountered out-of-memory errors in PySpark? How did you solve them? 1️⃣7️⃣ What steps would you take if a PySpark job fails midway? How do you recover? 1️⃣8️⃣ A Spark task keeps failing due to data corruption in one partition. How do you handle it? 1️⃣9️⃣ Have you used custom UDFs in PySpark? What challenges did you face, and how did you overcome them? 2️⃣0️⃣ Have you ever debugged a PySpark job producing incorrect results? What was your approach? ✨ Follow for more interview insights and tips to ace your next PySpark challenge! ✨ #SQL #BigData #DataEngineering #Hadoop #HDFS #Sqoop #Hive #Spark #Snowflake #AWS #Azure #GCP #Interview #PySpark

75

Learn two useful functions to nest and un-nest datasets in PySpark — explore and collect_list — by following along Tom Reid's latest tutorial.

8