neo4j
ArangoDB
Redis
OrientDB
MongoDB
Azure Cosmos DB
Apache Cassandra
CouchBase
Apache Flink
Apache Spark
Amazon Kinesis
Spring Framework
Spark Mail
Apache Airflow
Grails
Confluent
neo4j Apache FlinkNo features have been listed yet.
Apache Flink might be a bit more popular than neo4j. We know about 30 links to it since March 2021 and only 27 links to neo4j. 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.
Neo4j: An ACID-compliant graph database with a high-performance distributed architecture. Ideal for complex relationship and pattern analysis in domains like social networks. - Source: dev.to / 4 months ago
The GenAI Stack came about through a collaboration between Docker, Neo4j, LangChain, and Ollama. The goal of the collaboration was to create a pre-built GenAI stack of best-in-class technologies that are well integrated, come with sample applications, and make it easy for developers to get up and running. The goal of the collaboration was to create a pre-built GenAI stack of best-in-class technologies that are... - Source: dev.to / 8 months ago
NoSQL is a term that we have become very familiar with in recent times and it is used to describe a set of databases that don't make use of SQL when writing & composing queries. There are loads of different types of NoSQL databases ranging from key-value databases like the Reddis to document-oriented databases like MongoDB and Firestore to graph databases like Neo4J to multi-paradigm databases like FaunaDB and... - Source: dev.to / 10 months ago
Recently I have taken an interest in big data. https://neo4j.com/ , https://cassandra.apache.org/ , https://clickhouse.com/, https://www.elastic.co/ - are all databases I have experience with. Neo4j and Cassandra only as a hobby, but Clickhouse I have used in production, and Elasticsearch I have used for some 7 years now. Source: about 1 year ago
For organizations and their applications that are designed to detect fraud, like International Consortium of Investigative Journalists, or try to improve customer experience via personalization, as in the case of Tourism Media, a NoSQL graph database like Neo4j is a good match. In these kinds of use cases, the quantity of data we're dealing with is enormous, and the pattern we're searching for in the data is often... - Source: dev.to / about 1 year ago
Restate is built as a sharded replicated state machine similar to how TiKV (https://tikv.org/), Kudu (https://kudu.apache.org/kudu.pdf) or CockroachDB (https://github.com/cockroachdb/cockroach) since it makes it possible to tune the system more easily for different deployment scenarios (on-prem, cloud, cost-effective blob storage). Moreover, it allows for some other cool things like seamlessly moving from one log... - Source: Hacker News / 4 days ago
I’ve recently started my journey with Apache Flink. As I learn certain concepts, I’d like to share them. One such "learning" is the expansion of array type columns in Flink SQL. Having used ksqlDB in a previous life, I was looking for functionality similar to the EXPLODE function to "flatten" a collection type column into a row per element of the collection. Because Flink SQL is ANSI compliant, it’s no surprise... - Source: dev.to / 24 days ago
You should let the Apache Flink team know, they mention exactly-once processing on their home page (under "correctness guarantees") and in their list of features. [0] https://flink.apache.org/ [1] https://flink.apache.org/what-is-flink/flink-applications/#building-blocks-for-streaming-applications. - Source: Hacker News / about 1 month ago
Data scientists often prefer Python for its simplicity and powerful libraries like Pandas or SciPy. However, many real-time data processing tools are Java-based. Take the example of Kafka, Flink, or Spark streaming. While these tools have their Python API/wrapper libraries, they introduce increased latency, and data scientists need to manage dependencies for both Python and JVM environments. For example,... - Source: dev.to / 2 months ago
Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / 4 months ago
ArangoDB - A distributed open-source database with a flexible data model for documents, graphs, and key-values.
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.
Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.
OrientDB - OrientDB - The World's First Distributed Multi-Model NoSQL Database with a Graph Database Engine.
Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.