Real questions from top companies in System Design/Architecture · hard
How do you ensure data quality and consistency in your pipelines?
How do you ensure data quality in a big data pipeline, and what strategies do you use for data validation?
How do you ensure data quality in an automated pipeline?
How do you ensure fault tolerance during large-scale data migrations?
How do you ensure your pipelines are serving reliable and correct data?
How do you handle production deployment?
How do you handle schema evolution in a system with multiple data sources and consumers?
How do you monitor and troubleshoot data pipeline failures in Data Fusion?
How do you optimize data ingestion?
How do you pass global variables between pipelines?
How do you use dependency tracing to identify root causes in pipeline failures?
How does HDFS handle fault tolerance?
How does Presto fetch data from a data catalog?
How does Spark handle distributed computing, and what challenges have you faced while working on distributed systems?
How does data flow through the system? From ingestion to processing and storage?
How to adapt the same pipeline to a cloud environment?
How to capture data lineage for Spark code, using a DataHub-based example?
How to create a database from scratch and architect it for scalability and performance?
How to set up ETL pipelines using Apache Airflow?
How to store massive data in a distributed system?
Type or paste your answer to any of these questions and our AI Coach scores it, highlights gaps, and rewrites it at FAANG quality. Free to try.