Practise vocabulary for data lakes, lakehouses, medallion architecture, Delta Lake, Apache Iceberg, and ACID transactions.
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The medallion architecture (bronze/silver/gold layers) organises data as:
Medallion architecture progressively improves data quality: Bronze stores source-faithful raw data (audit, replay). Silver applies cleansing, deduplication, type casting, and joins. Gold produces business-level aggregations optimised for specific use cases (finance reporting, product analytics).
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A data lakehouse combines a data lake and a data warehouse by:
The lakehouse pattern: data stored as Parquet files in S3/ADLS/GCS, with a metadata layer (Delta Lake transaction log, Iceberg catalog) providing ACID guarantees, schema evolution, and time travel. Query engines (Spark, Trino, DuckDB) read from object storage directly.
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ACID transactions in a lakehouse context (Delta Lake, Iceberg) provide:
Object storage (S3) has no transaction support: concurrent writers can corrupt each other's data. Delta Lake and Iceberg add a transaction log (DeltaLog / Iceberg metadata) that serialises writes, enabling safe concurrent operations that plain Parquet-on-S3 cannot support.
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Time travel in Delta Lake or Apache Iceberg allows:
Time travel example: SELECT * FROM orders VERSION AS OF 5 or SELECT * FROM orders TIMESTAMP AS OF '2024-01-15'. Delta Lake retains transaction log history; Iceberg retains metadata snapshots. Both enable reproducing the exact data state at any past point.
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Schema-on-read (data lake) compared to schema-on-write (data warehouse) means:
Data lake (schema-on-read): ingest anything, figure out the schema when querying — flexible ingestion, complex queries. Data warehouse (schema-on-write): enforce schema on insert — guaranteed data quality, simpler queries. Lakehouses bridge this: store like a lake, query like a warehouse.
What does the "Data Lakehouse Architecture Vocabulary" exercise practise?How many questions are in this exercise?
This exercise has 5 questions, each multiple-choice with a full explanation shown after you answer.
What English level is this exercise for?
This exercise is tagged Intermediate. If the vocabulary feels difficult, browse the Data Engineering Language category page for an easier module to start with.
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