All 43 questions
What is Apache Iceberg, and what are its key features for managing data lake tables?
Asked in 4 of 274 interviews · last asked Jul 16 · Concept · Senior
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Apache Iceberg is an open table format for data lakes, not a database engine. It tracks data files through metadata and snapshots, supporting ACID transactions and consistent reads. Its key features include schema and partition evolution, time travel, and efficient queries across large tables.
- Atomic snapshot updates support concurrent readers and writers without exposing partial changes.
- Schema evolution supports changes such as adding or renaming columns without rewriting existing data.
- Hidden partitioning simplifies queries; partition layouts can change without rewriting old files.
- Engines such as Spark, Trino, and Flink can share tables, though feature support varies.
What the interviewer checks: The interviewer is checking whether you understand how Iceberg adds reliable table management to data lake files.
Other ways it was asked
- “What have you built with Apache Iceberg, and how would you get up to speed using it as the table format across a data lake?”
- “What problems does Apache Iceberg solve that Delta Lake does not?”
- “How is Apache Iceberg different from Hive external tables?”
What is Delta Lake, and how do Delta tables support ACID transactions, schema enforcement, and time travel?
Asked in 4 of 274 interviews · last asked Oct 1 · Concept · Junior
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Delta Lake is an open-source storage layer that adds reliability to data lakes. Delta tables use Parquet files and a transaction log to provide ACID transactions, so writes commit fully or not at all. Schema enforcement rejects incompatible writes unless supported schema changes are explicitly allowed. Time travel reads earlier table versions while the required history and files remain available.
- ACID means atomicity, consistency, isolation, and durability; the transaction log tracks committed table versions.
- Readers see consistent snapshots, and conflicting writes may fail rather than corrupt data.
- Schema enforcement checks write compatibility; schema evolution allows supported schema changes.
- Time travel supports versions or timestamps; VACUUM can remove files needed to read older versions.
What the interviewer checks: The interviewer wants to know whether you understand how Delta adds reliable writes, schema control, and historical reads.
Other ways it was asked
- “What is Delta Lake, and how does it support ACID transactions, schema enforcement, and time travel?”
- “Can you tell me what advantages Delta Tables bring?”
- “What is Delta Lake in Databricks?”
How does Apache Iceberg store data internally?
Asked in 2 of 274 interviews · last asked Aug 20 · Concept · Senior
Other ways it was asked
- “How does Apache Iceberg work, specifically regarding data storage and search functionality?”
What is the difference between external tables and managed tables?
Asked in 2 of 274 interviews · last asked Aug 20 · Concept · Junior
Other ways it was asked
- “What is the difference between managed and external tables in Databricks?”
- “What are the benefits of managed tables versus external tables?”
When should you run VACUUM on a Delta table, and what does it remove?
Asked once · last asked Apr 17 · Tooling · Mid-level
Other ways it was asked
- “What does the VACUUM command do to a Delta table?”
What types of Apache Iceberg catalog integrations are available, and where can the catalog be stored?
Asked once · last asked Jun 9 · Concept · Senior
Other ways it was asked
- “Where can an Apache Iceberg catalog be hosted?”
How can Apache Iceberg data be exposed through a REST API?
Asked once · last asked Jun 9 · Design · Senior
What is the use case for Iceberg, and why should I use it?
Asked once · last asked Jun 9 · Concept · Mid-level
What do you understand by a lakehouse, how does Iceberg fit into it, and can you design a lakehouse without Iceberg or with something else?
Asked once · last asked Jun 9 · Design · Senior
If you built an Iceberg lakehouse, how would you expose its data to downstream consumers outside AWS so they could access, download, and work with it?
Asked once · last asked Jun 9 · Design · Senior
How would consumers connect to a file-based Apache Iceberg lakehouse using JDBC?
Asked once · last asked Jun 9 · Design · Senior
Other ways it was asked
- “Where is the metadata required for JDBC access to a file-based Apache Iceberg lakehouse hosted?”
- “Where should a JDBC query be sent when querying an Apache Iceberg lakehouse?”
What solution would you use to expose Apache Iceberg data to someone outside AWS who does not have EMR?
Asked once · last asked Jun 9 · Design · Senior
How does schema evolution work in Apache Iceberg?
Asked once · last asked Jun 9 · Concept · Senior
If an Apache Iceberg manifest file were deleted, what would happen to the data, and would you still be able to access it?
Asked once · last asked Jun 9 · Scenario · Senior
Other ways it was asked
- “Can deleting an Apache Iceberg manifest file cause data corruption?”
How do you perform time travel on Apache Iceberg tables?
Asked once · last asked Jun 23 · Tooling · Mid-level
Walk me through the design decisions and considerations for implementing a hybrid lake house in a customer environment.
Asked once · last asked Jul 16 · Design · Senior
Other ways it was asked
- “How would you approach designing a hybrid lakehouse architecture?”
How does Iceberg federation work end-to-end and how would you implement it?
Asked once · last asked Jul 16 · Design · Senior
In AWS, how would you design a scalable, cost-effective lakehouse pattern for ML and BI using S3 with Iceberg, Hudi, or Delta, and what would drive your choice among those table formats?
Asked once · last asked Jun 30 · Design · Senior
What are the differences and benefits of Apache Iceberg and Hive ACID?
Asked once · last asked Jul 16 · Concept · Senior
What is the difference between a Data Lake and a Delta Lake?
Asked once · last asked Aug 11 · Concept · Junior
What is the difference between the Parquet file format and the Delta Lake table format?
Asked once · last asked Aug 12 · Concept · Mid-level
Other ways it was asked
- “What is the difference between Parquet and Delta file formats?”
For a star schema, would you store data in Delta tables or an external database such as PostgreSQL, and why?
Asked once · last asked Aug 14 · Scenario · Mid-level
How is Unity Catalog different from a traditional Hive Metastore?
Asked once · last asked Aug 14 · Concept · Mid-level
How does Delta Lake differ from plain Parquet, and what capabilities does it add?
Asked once · last asked Aug 19 · Concept · Mid-level
Can you explain how lakehouse architecture and Apache Iceberg work together in Databricks?
Asked once · last asked Aug 20 · Concept · Senior
Other ways it was asked
- “How does Apache Iceberg function within a lakehouse architecture?”
How does dropping an external table affect the underlying files?
Asked once · last asked Aug 20 · Concept · Mid-level
How would you design a data architecture that lets Spark, Snowflake, Microsoft Fabric, and other engines access shared data without creating separate copies, while allowing teams to subscribe to specific datasets?
Asked once · last asked Aug 31 · Design · Senior
What is the difference between mergeSchema and overwriteSchema?
Asked once · last asked Sep 1 · Concept · Mid-level
When would you choose Avro, and when would you choose Parquet?
Asked once · last asked Sep 10 · Concept · Mid-level
How do you choose between Delta and Iceberg across the data lifecycle?
Asked once · last asked Sep 16 · Scenario · Senior
How do open table formats enable data to be accessed by multiple compute engines?
Asked once · last asked Sep 16 · Concept · Senior
If you write data to OneLake instead of ADLS, how would that affect your technology and data-format choices?
Asked once · last asked Sep 16 · Scenario · Senior
What are Parquet and Avro, and how does data orchestration differ from data file formats?
Asked once · last asked Sep 17 · Concept · Mid-level
How does Parquet differ from a row-oriented file format?
Asked once · last asked Sep 17 · Concept · Mid-level
How is a Delta table different from data stored in Azure Data Lake Storage (ADLS)?
Asked once · last asked Oct 1 · Concept · Junior
Which file format would you choose for storing analytics data in Azure Blob Storage?
Asked once · last asked Oct 7 · Scenario · Mid-level
What tool or service would let an external consumer read data from your lakehouse?
Asked once · last asked Jun 9 · Design · Mid-level
What is the role of a metastore database in a data platform?
Asked once · last asked Jul 16 · Concept · Mid-level
What are the benefits of Parquet?
Asked once · last asked Sep 10 · Concept · Junior
Does Delta Lake support all table types?
Asked once · last asked Sep 17 · Concept · Mid-level
What data formats are commonly used in big data, including Parquet?
Asked once · last asked Sep 17 · Concept · Junior
What is the Parquet file format?
Asked once · last asked Sep 17 · Concept · Junior
How does Delta Lake help with performance?
Asked once · last asked Sep 22 · Concept · Mid-level
More Data Modeling interview questions
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How this list is made
Every question here was asked by a real interviewer in a real job interview that a candidate ran MeetAssist in. After the call, the interviewer's questions are pulled from the transcript. Practice sessions, tests, exams and anything that was not a job interview are dropped.
Questions are rewritten so they make sense on their own: names of people, employers and clients are removed, and speech-to-text mistakes are fixed. Small talk, logistics and questions about one candidate's own résumé are left out. Rephrasings of the same question are merged, and the count is the number of different interviews it came up in.
The interviews come from candidates who use MeetAssist, so they lean towards software, data and AI roles: the shares describe these interviews, not every Data Modeling interview. Answers to the most asked questions are written and checked by an editor; a "common mistake" is shown only when reviews of real answers in these interviews show it, with the count.
274 interviews, 168 candidates, Dec 2025 to Oct 2026. Updated every month with new interviews.
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