Big Data analytics
Posted: Wed Jun 18, 2025 3:02 am
Specialty: Petabyte-scale data, high write availability, excellent for time-series data or data where analytics involve scanning specific columns across many rows.
Use Cases: , IoT data, real-time recommendations, large-scale logging, high-volume transactional data that doesn't require complex joins.
Examples: Apache Cassandra, Apache HBase, Google Bigtable.
Graph Databases:
Concept: Based on graph theory, data is stored as nodes (entities) and edges (relationships between entities). Both nodes and edges can have properties.
Specialty: Exceptionally efficient at traversing complex relationships and discovering patterns within highly interconnected data. Unlike relational databases that struggle with multi-level joins, graph databases perform these traversals natively.
Use Cases: Social networks, fraud detection, recommendation engines, special database knowledge graphs, supply chain tracking, identity management.
Examples: Neo4j, ArangoDB (multi-model), Amazon Neptune, TigerGraph.
Time Series Databases (TSDBs):
Concept: Optimized for handling data points that are indexed by time. Data typically arrives as a continuous stream of measurements or events.
Specialty: High-volume ingestion of time-stamped data, efficient storage (often with specialized compression), and rapid aggregation/querying over time ranges.
Use Cases: IoT sensor data, application performance monitoring (APM), financial market data, industrial equipment monitoring, DevOps metrics.
Examples: InfluxDB, TimescaleDB (PostgreSQL extension), Prometheus, Apache Druid.
Use Cases: , IoT data, real-time recommendations, large-scale logging, high-volume transactional data that doesn't require complex joins.
Examples: Apache Cassandra, Apache HBase, Google Bigtable.
Graph Databases:
Concept: Based on graph theory, data is stored as nodes (entities) and edges (relationships between entities). Both nodes and edges can have properties.
Specialty: Exceptionally efficient at traversing complex relationships and discovering patterns within highly interconnected data. Unlike relational databases that struggle with multi-level joins, graph databases perform these traversals natively.
Use Cases: Social networks, fraud detection, recommendation engines, special database knowledge graphs, supply chain tracking, identity management.
Examples: Neo4j, ArangoDB (multi-model), Amazon Neptune, TigerGraph.
Time Series Databases (TSDBs):
Concept: Optimized for handling data points that are indexed by time. Data typically arrives as a continuous stream of measurements or events.
Specialty: High-volume ingestion of time-stamped data, efficient storage (often with specialized compression), and rapid aggregation/querying over time ranges.
Use Cases: IoT sensor data, application performance monitoring (APM), financial market data, industrial equipment monitoring, DevOps metrics.
Examples: InfluxDB, TimescaleDB (PostgreSQL extension), Prometheus, Apache Druid.