GirişYazıyı (10 Data Models Every Data Engineer Must Know (Before They Break Production)) ilk olarak
burada gördüm.
10. 10. Star Schema: The Legacy Workhorse (That Fails at Scale)
Açıklaması şöyle.
Star schemas are intuitive and analyst-friendly, but at scale they become a performance bottleneck, especially with massive fact tables, high-cardinality dimensions, and near-real-time workloads.
9. Snowflake Schema: Over-Engineered & Slow
Açıklaması şöyle.
Snowflake schemas optimize storage, not query performance. In modern analytics (cloud OLAP, dashboards, ad-hoc queries), compute is the bottleneck, not disk. Excessive normalization explodes join depth and kills latency.
8. Data Vault: The Enterprise Monster (When You Need Auditability)
Açıklaması şöyle.
Data Vault excels at auditability, lineage, and full historization, critical for regulated industries (banking, healthcare). But its multi-layer architecture makes it fundamentally unsuited for low-latency analytics.
7. Wide-Column Stores (Cassandra, Bigtable) for Time-Series Chaos
Açıklaması şöyle.
Wide-column databases dominate high-velocity ingest (IoT, metrics, logs) where writes never stop. But they sacrifice query flexibility, no joins, limited filtering, and rigid access patterns. You win on writes, lose on exploration.
6. Graph Models (Neo4j, TigerGraph) for Hidden Relationships
Açıklaması şöyle.
When insight lives in relationships (fraud rings, social influence, network hops), relational joins collapse under recursive depth. Graph databases treat relationships as first-class citizens, making multi-hop traversals fast and natural.
5. Streaming Event Sourcing (Kafka + CDC)
Açıklaması şöyle.
Batch ETL is fundamentally incompatible with real-time systems. CDC turns database mutations into immutable events, enabling near-zero-latency pipelines, replayable state, and system-wide consistency across microservices.
4. Columnar Storage (Parquet, Delta Lake) for Cheap, Fast Analytics
Parquet bir örnek
Açıklaması şöyle.
Row-based databases are optimized for point lookups, not scans. Analytics workloads read a few columns across billions of rows, exactly what columnar storage is built for. The result: orders-of-magnitude faster queries at a fraction of the cost.
Örnek
Şöyle yaparız
CREATE TABLE sales_parquet (
order_id BIGINT,
region STRING,
amount DECIMAL(10,2),
order_ts TIMESTAMP
)
USING PARQUET
PARTITIONED BY (region, order_date);
SELECT
region,
SUM(amount) AS total_sales
FROM sales_parquet
WHERE order_date = '2025-12-25'
AND region = 'US'
GROUP BY region;
Why this is fast
- Only amount and region columns are read
- Only the order_date=2025-12-25 and US partitions are scanned
- All other files are skipped entirely
3. Multi-Model Hybrids (When SQL + NoSQL Collide)
Açıklaması şöyle. Burada veri tabanının JSONB sütunları desteklemesi önemli
Real-world data is rarely one shape. Modern apps mix relational facts, semi-structured JSON, and relationships. Multi-model databases let you query everything in one place, without forcing awkward ETL or duplicating data.
2. Reverse ETL (Operational Analytics) to Put Data Back in Apps
1. The Unified Serving Layer (The Future of Production Data)
One dataset. Many engines. Zero rewrites. Açıklaması şöyle
Modern data stacks fracture data across OLTP, OLAP, search, and streaming systems, creating sync lag and duplicated logic. A Unified Serving Layer uses one logical data layer (Iceberg/Hudi/Delta) with multiple access modes: SQL analytics, near-real-time reads, ML, and even graph/search workloads.
