This podcast episode explores exotic databases like MultiValue DBMS storing multiple values efficiently, and Spatial DBMS for maps and polygons. They discuss implementations in real estate, event sourcing with Event Store databases, and database setup with Docker and Kubernetes. The hosts maintain a fun atmosphere with casual discussions on pronunciations, weather, moving, and Wi-Fi systems.
Multi-value databases store arrays in attributes, ideal for scenarios needing multiple values per record.
Spatial databases efficiently handle spatial data like maps and polygons using custom data types.
Event store databases support event sourcing, track historical changes, and offer efficient querying of historical data.
Deep dives
Multi-Value Database Management Systems
Multi-value database management systems store data in tables similar to relational databases but have the capability to store multiple values in a particular record's attribute, allowing arrays or lists to be stored. Popular examples include Adabas, UniData/UniVerse, and JBase. This type of database differs from the relational model by enabling the storage of more than one value in an attribute, making it suitable for scenarios requiring arrays or lists.
Geospatial Database Management Systems
Geospatial database management systems are designed for efficiently storing, modifying, and querying spatial data like maps or polygons. Popular systems, such as PostGIS, Aerospike, and SpatiaLite, utilize custom data types for shapes like points, lines, and polygons, facilitating quick retrieval of spatial information. These systems employ R-trees and quad trees for location-based data searches to efficiently locate points and regions within maps or geographic areas.
Event Store Database Systems
Event store database systems support the concept of event sourcing, storing immutable transaction records to track historical changes and system states. These databases, such as EventStoreDB and Event Store, cater to scenarios where auditing history, replaying transactions for state analysis, and maintaining an unchangeable record of events are critical. Unlike traditional relational databases, event stores focus on capturing a living audit trail of entity changes, offering a transparent and comprehensive view of system movements over time.
Event Store DB Usage in Enhancing Data Querying Capabilities
Event Store DB provides a purpose-built solution for storing events and allows for efficient querying of historical data. By enabling the materialization of events up to certain points in time, users can access snapshots without replaying all events, optimizing performance. Additionally, the database offers simplicity with only three essential API calls for managing events, enhancing ease of operation. The ability to subscribe to new events enables reactive applications, triggering responses based on incoming event updates.
Utilizing Event Store DB for Advanced Data Analysis and Projections
Event Store DB offers revolutionary features for complex data analysis, enabling users to define intricate criteria across time-related events. By leveraging projections based on new events, the database facilitates the creation of analytical outputs from event streams. This functionality allows for dynamic event-triggered actions, such as identifying specific patterns in data streams. The database's capacity for storing incremental state changes over time is crucial for enabling intricate data patterns and streamlined analytics.
We are mixing it up on you again, no Outlaw this week, but we can offer you some talk of exotic databases. Also, Joe pronounces everything correctly and Allen leaves you with a riddle. The full show notes are available on the website at https://www.codingblocks.net/episode229 News Multivalue DBMS Spatial DBMS Event Stores Resources we like […]
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