Based on our record, Apache Cassandra should be more popular than Apache Storm. It has been mentiond 44 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
In fact, even in the absence of these commercial databases, users can effortlessly install PostgreSQL and leverage its built-in pgvector functionality for vector search. PostgreSQL stands as the benchmark in the realm of open-source databases, offering comprehensive support across various domains of database management. It excels in transaction processing (e.g., CockroachDB), online analytics (e.g., DuckDB),... - Source: dev.to / 12 days ago
All messages are persisted durably for two minutes, but Pub/Sub channels can be configured to persist messages for longer periods of time using the persisted messages feature. Persisted messages are additionally written to Cassandra. Multiple copies of the message are stored in a quorum of globally-distributed Cassandra nodes. - Source: dev.to / 6 months ago
Cassandra is a highly scalable, distributed NoSQL database designed to handle large amounts of data across many commodity servers without a single point of failure. - Source: dev.to / 10 months ago
Distributed storage Distributed storage systems like Cassandra, DynamoDB, and Voldemort also use consistent hashing. In these systems, data is partitioned across many servers. Consistent hashing is used to map data to the servers that store the data. When new servers are added or removed, consistent hashing minimizes the amount of data that needs to be remapped to different servers. - Source: dev.to / 12 months ago
On the other hand, NoSQL databases are non-relational databases. They store data in flexible, JSON-like documents, key-value pairs, or wide-column stores. Examples include MongoDB, Couchbase, and Cassandra. - Source: dev.to / about 1 year ago
There are several frameworks available for batch processing, such as Hadoop, Apache Storm, and DataTorrent RTS. - Source: dev.to / over 2 years ago
Although this article lists a lot of targets for technical selection, there are definitely others that I haven't listed, which may be either outdated, less-used options such as Apache Storm or out of my radar from the beginning, like JAVA ecosystem. - Source: dev.to / over 2 years ago
Storm, a system for real-time and stream processing. - Source: dev.to / over 2 years ago
Google has scaled well and has helped others scale, Twitter has always been behind by years. I think the only thing they did well was Twitter Storm, now taken up by Apache Foundation. Source: over 2 years ago
Streaming: Sparks Streamings's latency is at least 500ms, since it operates on micro-batches of records, instead of processing one record at a time. Native streaming tools like Storm, Apex or Flink might be better for low-latency applications. - Source: dev.to / over 3 years ago
Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.
Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.
ArangoDB - A distributed open-source database with a flexible data model for documents, graphs, and key-values.
Google BigQuery - A fully managed data warehouse for large-scale data analytics.