Advanced~3h
Data Lakes & Lakehouse in Data Engineering CoreInterview Readiness Check
Object storage as the foundation, file formats (Parquet/Avro), table formats (Delta Lake, Apache Iceberg, Hudi), the Lakehouse model, schema evolution, ACID on S3..
data lakelakehouseDelta LakeIcebergHudiParquetAvroschema evolutionACIDobject storage
Learning path
Try a sample question
What is the primary advantage of storing data in a data lake over a traditional data warehouse?
A.Data lakes store raw data in any format at low object storage cost, deferring structure decisions until query time
B.Data lakes enforce schema-on-write for better consistency
C.Data lakes provide faster SQL queries than warehouses
D.Data lakes eliminate the need for data engineering
Sign up free to take the full check and see the answer with an explanation.
What this module tests
- Conceptual understanding of data lakes & lakehouse fundamentals
- Trade-offs and design decisions in real system scenarios
- data lake and lakehouse — applied knowledge
- Interview-style reasoning and explanation quality
Free to start
Take the Data Lakes & Lakehouse check →
10 questions · Dimension scoring · AI coach feedback · Shareable result
Test your knowledge — free →No credit card · Takes 10–15 min
Frequently asked questions
- What does the Data Lakes & Lakehouse check test?
- The check covers object storage as the foundation, file formats (parquet/avro), table formats (delta lake, apache iceberg, hudi), the lakehouse model, schema evolution, acid on s3.. Questions span beginner to advanced difficulty and test both conceptual understanding and practical trade-off reasoning.
- What keywords should I know for Data Lakes & Lakehouse?
- Key concepts include: data lake, lakehouse, Delta Lake, Iceberg, Hudi, Parquet, Avro, schema evolution, ACID, object storage. These frequently appear in data engineering core interviews at product and infrastructure companies.
- How many questions are in the check?
- Each check has 10 questions — a mix of MCQ and short-answer. You get scored by dimension (e.g., scalability, consistency) so you know exactly where to focus.
- How long does it take to study Data Lakes & Lakehouse?
- Most engineers spend around 3 hours on this module. The check itself takes about 10–15 minutes. You can retake it up to 3 times per day.
- What prerequisite knowledge do I need before Data Lakes & Lakehouse?
- Data Lakes & Lakehouse builds on Data Warehousing. Complete those first for the best results.
- Is data lake, lakehouse, Delta Lake covered?
- Yes. The Data Lakes & Lakehouse module covers data lake, lakehouse, Delta Lake among other key concepts. The check includes questions that reflect real data engineering core interview patterns.