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Data Engineering
12 topics · 194 questions
1Data Engineering Fundamentals0/10102Batch vs Stream Processing0/553Data Formats: Parquet, Avro, ORC0/664Apache Spark: Core0/72725Spark SQL0/336Spark Streaming & Structured Streaming0/22227Apache Flink0/33338Kafka in Data Pipelines0/229Pipeline Design & Operations0/210Databricks & Delta Lake0/7711Performance Tuning0/8812Advanced Data Engineering0/2424
1Data Engineering Fundamentals102Batch vs Stream Processing53Data Formats: Parquet, Avro, ORC64Apache Spark: Core725Spark SQL36Spark Streaming & Structured Streaming227Apache Flink338Kafka in Data Pipelines29Pipeline Design & Operations210Databricks & Delta Lake711Performance Tuning812Advanced Data Engineering24
12 topics194 Qs0%
01Core Java17002OOP3203Collections6704Exception Handling2805Concurrency & Multithreading12706Java Streams, Lambda & Functional Programming32807Spring & Spring Boot2608Transactions3209JPA & Hibernate6910Microservices19411Kafka & Messaging9112DevOps, CI/CD & Tooling13313Database & SQL14114REST API & Web4015Design Patterns & LLD5716Advanced Java7117Coding Problems: DSA49218AWS & Cloud4119Data Engineering19420QA, Testing & SDET4121System Design3522Security2023Behavioral / HR: STAR Method37
12Topics
194Questions
0%Complete
9 of 12 topics
Topics
1Data Engineering Fundamentals0/10102Batch vs Stream Processing0/553Data Formats: Parquet, Avro, ORC0/664Apache Spark: Core0/72725Spark SQL0/336Spark Streaming & Structured Streaming0/22227Apache Flink0/33338Kafka in Data Pipelines0/22
9Pipeline Design & Operations0/22
10Databricks & Delta Lake0/7711Performance Tuning0/8812Advanced Data Engineering0/2424

Pipeline Design & Operations

Data Engineering·Topic 9 of 12·2 questions
Q1Ensuring data quality and handling pipeline failures in production

Enforce quality with schema, null, range, uniqueness, reconciliation, contracts, and anomaly checks; recover from failures with dead-letter queues, idempotent re-runs, backfills, and clear ownership.

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Q2Handling late / out-of-order data and reprocessing in a streaming pipeline

Use event-time with watermarks and allowed lateness for correct windows despite stragglers, and reprocess by replaying with idempotent writes so re-runs never duplicate.

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Previous · Topic 8Kafka in Data Pipelines2 questionsNext · Topic 10Databricks & Delta Lake7 questions
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