Delta Lake vs Apache Parquet: Key Differences
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Architecture / Concept Overview: Delta Lake vs Apache Parquet: Key Differences
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flowchart LR
classDef source fill:#3F4B59,stroke:#9CA3AF,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef ingestion fill:#5A4B36,stroke:#C9A86B,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef processing fill:#535072,stroke:#8E82B4,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef storage fill:#2E4A4A,stroke:#5FAFA8,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef serving fill:#3D5550,stroke:#6BB7AA,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef governance fill:#5A3F52,stroke:#C28BB0,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
PQ[Parquet Files] -->|No Metadata Layer| READ1[Reader A]
PQ -->|No Metadata Layer| READ2[Reader B]
DT[Delta Table] -->|Transaction Log| LOG[_delta_log]
LOG -->|Consistent Snapshot| READ3[Reader A]
LOG -->|Consistent Snapshot| READ4[Reader B]
PQ:::source
READ1:::ingestion
READ2:::ingestion
DT:::storage
LOG:::governance
READ3:::serving
READ4:::serving
*Parquet readers must list files directly, risking inconsistency. Delta Lake readers consult the transaction log for a guaranteed-consistent file set.*
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graph TD
classDef source fill:#3F4B59,stroke:#9CA3AF,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef ingestion fill:#5A4B36,stroke:#C9A86B,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef processing fill:#535072,stroke:#8E82B4,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef storage fill:#2E4A4A,stroke:#5FAFA8,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef serving fill:#3D5550,stroke:#6BB7AA,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef governance fill:#5A3F52,stroke:#C28BB0,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
DELTA[Delta Lake Capabilities] --> ACID[ACID Transactions]
DELTA --> TT[Time Travel]
DELTA --> SE[Schema Enforcement]
DELTA --> DV[Deletion Vectors]
DELTA --> MERGE[MERGE / Upsert]
DELTA --> CDF[Change Data Feed]
PARQUET[Parquet Capabilities] --> COL[Columnar Compression]
PARQUET --> PRED[Predicate Pushdown]
PARQUET --> NEST[Nested Types]
DELTA:::storage
ACID:::processing
TT:::processing
SE:::governance
DV:::processing
MERGE:::ingestion
CDF:::serving
PARQUET:::source
COL:::source
PRED:::source
NEST:::source
*Delta Lake inherits all Parquet benefits (columnar storage, predicate pushdown) and adds transactional and operational capabilities.*
Key Terms
Prerequisites and Setup
- A Databricks workspace or open-source Spark with the Delta Lake library
- An existing Parquet dataset you want to compare or convert
CREATE TABLEprivilege in the target schema for writing Delta tables
Step-by-Step Implementation
Configuration Reference
| Property | Parquet | Delta Lake |
|---|---|---|
| ACID Transactions | No | Yes |
| Schema Enforcement | No (reader responsibility) | Yes (writer-side) |
| Time Travel | No | Yes (via versioned log) |
| MERGE / UPDATE / DELETE | No | Yes |
| Streaming Source & Sink | Append-only | Full support with exactly-once |
| File Compaction | Manual | Auto-optimise / OPTIMIZE |
| Data Skipping | Row-group statistics only | Per-file min/max in transaction log |
| Change Data Feed | No | Yes |