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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 Pipelines0/29Pipeline Design & Operations0/2210Databricks & 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
8 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 Flink0/3333
8Kafka in Data Pipelines0/22
9Pipeline Design & Operations0/2210Databricks & Delta Lake0/7711Performance Tuning0/8812Advanced Data Engineering0/2424

Kafka in Data Pipelines

Data Engineering·Topic 8 of 12·2 questions
Q1Delivery semantics and how exactly-once is achieved

At-most-once may lose, at-least-once may duplicate, and exactly-once applies each message once via idempotent producers, transactional writes, and offsets committed atomically with the output.

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Q2Consumer lag, rebalancing, back-pressure, and keeping a pipeline healthy

Lag is unprocessed backlog; rebalancing redistributes partitions; back-pressure slows producers; and you stay healthy by scaling consumers, monitoring lag, and dead-lettering poison records.

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Previous · Topic 7Apache Flink33 questionsNext · Topic 9Pipeline Design & Operations2 questions
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