High-Volume Ingestion: Capable of writing millions of events per second

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Time-Series Focus: Events are inherently ordered by time, demanding efficient temporal indexing and querying.
Immutability: Events are typically appended and never modified, forming an immutable log.
Real-time Analytics: Ability to perform aggregations, detect patterns, or trigger alerts on incoming data with minimal latency.
Database Technologies:

Time Series Databases (TSDBs): InfluxDB, TimescaleDB, Prometheus, Apache Druid.
Stream Processing Platforms (with storage capabilities): Apache Kafka (often combined special database with ksqlDB), Apache Flink, Apache Storm.
Columnar Databases: ClickHouse (excellent for analytical queries over massive event data).
Schema Considerations:

Timestamp: Primary key or crucial part of a composite key. Often stored with millisecond or microsecond precision.
Event Type: Categorization of the activity (e.g., 'sensor_read', 'user_login', 'transaction_attempt').
Payload/Attributes: JSON or other flexible format to store varying event-specific details.
Source Identifier: Which device, user, or application generated the event.
Special Activities Examples:

IoT Telemetry: Tracking the state of every component in a smart factory in real-time.
Fraud Detection: Analyzing payment events as they occur to identify suspicious patterns.
Network Intrusion Detection: Monitoring network traffic logs for anomalous activities indicative of a cyber attack.
Real-time Personalization: Adapting website content instantly based on user clicks and browsing behavior.