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In general, there are two different approaches, one is to choose a common NoSQL database, such as MongoDB, ElasticSearch or Apache Cassandra. All of these NoSQL databases have good scalability and can support complex queries. In addition, these databases are very mature, so the learning curve is low for both use and operation.
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Apache Superset
Streamlit
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MEM GRAPH: Graph database built for real-time streaming and compatible with Neo4j
Even though these technical stacks are listed by category, some fields actually overlap. For example, although Materialize is classified as a stream processor, it makes sense to treat as a serving layer because it is essentially a streaming database, and the same is true for ksqlDB.
The most common option is Kafka, but there are also various open source software and managed services. By the way, the following order does not represent the recommendation order.
The Apache Flink, which is often mentioned, is one of these options, and there are many others.
Faust: Python framework
Apache Druid
To capture data changes from these databases and deliver them to the stream platform, we often use Debezium.
ClickHouse
Apache Beam: Streaming framework which can be run on several runner such as Apache Flink and GCP Dataflow