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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 SQL0/36Spark Streaming & Structured Streaming0/22227Apache Flink0/33338Kafka in Data Pipelines0/229Pipeline 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
5 of 12 topics
Topics
1Data Engineering Fundamentals0/10102Batch vs Stream Processing0/553Data Formats: Parquet, Avro, ORC0/664Apache Spark: Core0/7272
5Spark SQL0/33
6Spark Streaming & Structured Streaming0/22227Apache Flink0/33338Kafka in Data Pipelines0/229Pipeline Design & Operations0/2210Databricks & Delta Lake0/7711Performance Tuning0/8812Advanced Data Engineering0/2424

Spark SQL

Data Engineering·Topic 5 of 12·3 questions
Q1What is the difference between Flink's DataSet API and DataStream API, and when do you use DataStream vs Table API/Flink SQL

DataSet is the deprecated batch-only low-level API; DataStream is the recommended low-level API unifying streaming and batch; the Table API and Flink SQL are the declarative layer for SQL-like analytics.

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Q2Adaptive Query Execution (AQE) and how it complements Catalyst

Catalyst optimizes the plan before execution using estimates, and AQE re-optimizes the physical plan during execution using real runtime statistics at shuffle boundaries : the two work together.

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Q3The Catalyst Optimizer

Catalyst turns a DataFrame or SQL query into an efficient plan through parse, analyze, optimize, and physical planning, cutting data read via predicate pushdown and column pruning and speeding execution with whole-stage code generation.

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Previous · Topic 4Apache Spark: Core72 questionsNext · Topic 6Spark Streaming & Structured Streaming22 questions
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