PouchDB VS Hadoop
Compare PouchDB VS Hadoop and see what are their differences
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PouchDB
CouchDB
GraphQL
RxDB
DataGrip
Redis
IndexedDB
Sequel Pro
Hadoop
Apache Spark
Apache Cassandra
Apache Flink
PostgreSQL
MongoDB
Apache Storm
Apache Kafka
PouchDB HadoopPouchDB might be a bit more popular than Hadoop. We know about 21 links to it since March 2021 and only 15 links to Hadoop. 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.
How does this compare to PouchDB[1]? [1]: https://pouchdb.com/. - Source: Hacker News / 4 months ago
Meteor wrapped the MongoDB API for this purpose. You are working with collections and can run the same queries over them, regardless of whether you are connected to a DB instance or the browser's local storage. For CouchDB an equivalent exists in the form of PouchDB: https://pouchdb.com/. - Source: Hacker News / 7 months ago
Not sure if you're thinking more of an official standard but PouchDB is open source and sounds similar to what you're talking about: https://pouchdb.com/. - Source: Hacker News / 9 months ago
I have another use case that DO would be perfect for, and that's sync for offline first apps. I have two offline first apps, both using PouchDB[1] as client database and CouchDB as server database. I'd love to replace CouchDB with DO. Maybe you can hire some of the people contributing to PouchDB to build a backend for it using DO? [1]: https://pouchdb.com. - Source: Hacker News / 10 months ago
PouchDB might be of interest - https://pouchdb.com/ - "PouchDB was created to help web developers build applications that work as well offline as they do online. Source: about 1 year ago
Did you check out tools like https://hadoop.apache.org/ ? Source: about 1 year ago
There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka. Source: about 1 year ago
There are several frameworks available for batch processing, such as Hadoop, Apache Storm, and DataTorrent RTS. - Source: dev.to / over 1 year ago
A copy of Hadoop installed on each of these machines. You can download Hadoop from the Apache website, or you can use a distribution like Cloudera or Hortonworks. - Source: dev.to / over 1 year ago
The Apache™ Hadoop™ project develops open-source software for reliable, scalable, distributed computing. - Source: dev.to / over 1 year ago
CouchDB - HTTP + JSON document database with Map Reduce views and peer-based replication
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
GraphQL - GraphQL is a data query language and runtime to request and deliver data to mobile and web apps.
Apache Cassandra - The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance.
RxDB - A fast, offline-first, reactive Database for JavaScript Applications
Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.