Semi-Structured Data
Learn how Tacnode handles JSON, nested objects, and schema-flexible data without sacrificing query performance. This demo covers ingestion patterns, indexing strategies, and query optimization for semi-structured workloads.
Overview
Modern applications generate data that doesn't fit neatly into rigid schemas. Tacnode Context Lake's semi-structured data support lets you ingest JSON, handle schema evolution, and query deeply nested objects—all with the performance of a columnar database.
Topics Covered
- •JSON document storage and retrieval
- •Path expressions and nested field access
- •Schema inference and evolution handling
- •Indexing strategies for JSON fields
- •Flattening nested structures for analytics
Key Takeaways
- ✓Store JSON documents without predefined schemas
- ✓Query nested fields with SQL-like syntax
- ✓Handle schema changes without migrations
- ✓Achieve columnar performance on semi-structured data
Technical Highlights
- →JSON path support: Full JSONPath expression language
- →Indexing: Automatic path-based index recommendations
- →Compression: Type-aware compression for JSON fields
- →Query pushdown: Filter evaluation at storage layer
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Overview (Start Here)
The first installment of our Product Demo series — a complete walkthrough of the Context Lake architecture.
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