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Update Redis url and add hybrid search support #531

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merged 2 commits into from
Nov 24, 2024
Merged

Update Redis url and add hybrid search support #531

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63c4a7e
update some links and add hybrid search
tylerhutcherson Nov 18, 2024
0c61c24
update link per Daniel's feedback
tylerhutcherson Nov 21, 2024
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2 changes: 1 addition & 1 deletion docs/building_blocks/data_sources/data_velocity.md
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Expand Up @@ -30,7 +30,7 @@ The choice of data processing velocity is pivotal in determining the kind of dat
|**Example Data**| Social media feeds, stock market transactions, sensor readings, clickstream data, ad requests and responses. A credit card company aiming to detect fraudulent transactions in real-time benefits from streaming data. Real-time detection can prevent financial losses and protect customers from fraudulent activities.|
|**Properties**| Stream processing handles data in real-time, making it highly dynamic. It's designed to support immediate updates and changes, making it ideal for use cases that require up-to-the-second insights.|
|**Formats**| Data in stream processing is often in Protobuf, Avro, or other formats optimized for small footprint and fast serialization.|
|**Databases**| Real-time databases include [Clickhouse](https://clickhouse.com/), [Redis](https://redis.com/), and [RethinkDB](https://rethinkdb.com/). There are also in-memory databases, such as [DuckDB](https://duckdb.org/) and [KuzuDB](https://kuzudb.com/), which can be used to create real-time dashboards. However, depending on the deployment strategy chosen, these databases may lose the data once the application is terminated.|
|**Databases**| Real-time databases include [Clickhouse](https://clickhouse.com/), [Redis](https://redis.io/), and [RethinkDB](https://rethinkdb.com/). There are also in-memory databases, such as [DuckDB](https://duckdb.org/) and [KuzuDB](https://kuzudb.com/), which can be used to create real-time dashboards. However, depending on the deployment strategy chosen, these databases may lose the data once the application is terminated.|

Most systems deployed in production at scale combine stream and batch processing. This enables you to leverage the immediacy of real-time updates and the depth of historical data. But reconciling stream and batch processes in a single system introduces trade-off decisions – trade-off decisions you must make to keep your data consistent across systems and across time.

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8 changes: 4 additions & 4 deletions docs/tools/vdb_table/data/redis.json
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Expand Up @@ -3,7 +3,7 @@
"links": {
"docs": "https://redis.io/docs/get-started/vector-database/",
"github": "https://github.com/RediSearch/RediSearch",
"website": "https://redis.com/solutions/use-cases/vector-database/",
"website": "https://redis.io/solutions/vector-database/",
"vendor_discussion": "https://github.com/superlinked/VectorHub/discussions/81",
"poc_github": "https://github.com/adrianoamaral",
"slug": "redis"
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"comment": ""
},
"hybrid_search": {
"support": "",
"source_url": "",
"comment": ""
"support": "full",
"source_url": "https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/02_hybrid_search.ipynb",
"comment": "Supported using the aggregation api."
},
"facets": {
"support": "full",
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