Based on our record, Hadoop should be more popular than TimescaleDB. It has been mentiond 15 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.
(:alert: I work for Timescale :alert:) It's funny, we hear this more and more "we did some research and landed on Influx and ... Help it's confusing". We actually wrote an article about what we think, you can find it here: https://www.timescale.com/blog/what-influxdb-got-wrong/ As the QuestDB folks mentioned if you want a drop in replacement for Influx then they would be an option, it kinda sounds that's not what... - Source: Hacker News / 6 months ago
If you like PostgreSQL, I'd recommend starting with that. Additionally, you can try TimescaleDB (it's a PostgreSQL extension for time-series data with full SQL support) it has many features that are useful even on a small-scale, things like:. Source: over 1 year ago
I have built a Django server which serves up the JSON configuration, and I'd also like the server to store and render sensor graphs & event data for my Thing. In future, I'd probably use something like timescale.com as it is a database suited for this application. However right now I only have a handful of devices, and don't want to spend a lot of time configuring my back end when the Thing is my focus. So I'm... Source: over 2 years ago
I've seen a lot of benchmark results on timescale on the web but they all come from timescale.com so I just want to ask if those are accurate. Source: over 2 years ago
Ryan from Timescale here. We (TimescaleDB) just launched the second annual State of PostgreSQL survey, which asks developers across the globe about themselves, how they use PostgreSQL, their experiences with the community, and more. Source: about 3 years ago
Did you check out tools like https://hadoop.apache.org/ ? Source: 12 months 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
InfluxData - Scalable datastore for metrics, events, and real-time analytics.
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Prometheus - An open-source systems monitoring and alerting toolkit.
Apache Cassandra - The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance.
OpenTSDB - OpenTSDB is a distributed, scalable Time Series Database (TSDB) written on top of HBase.
Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.