TimescaleDB
InfluxData
Prometheus
OpenTSDB
RRDTool
VictoriaMetrics
QuestDB
Redis
Amazon EMR
Google BigQuery
Google Cloud Dataflow
Google Cloud Dataproc
Databricks
Qubole
Snowflake
HortonWorks Data Platform
TimescaleDB Amazon EMRBased on our record, Amazon EMR should be more popular than TimescaleDB. It has been mentiond 10 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
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
I'm going to guess you want something like EMR. Which can take large data sets segment it across multiple executors and coalesce the data back into a final dataset. Source: almost 2 years ago
This is exactly the kind of workload EMR was made for, you can even run it serverless nowadays. Athena might be a viable option as well. Source: almost 2 years ago
Apache Spark is one of the most actively developed open-source projects in big data. The following code examples require that you have Spark set up and can execute Python code using the PySpark library. The examples also require that you have your data in Amazon S3 (Simple Storage Service). All this is set up on AWS EMR (Elastic MapReduce). - Source: dev.to / over 2 years ago
Check out https://aws.amazon.com/emr/. Source: about 2 years ago
InfluxData - Scalable datastore for metrics, events, and real-time analytics.
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
Prometheus - An open-source systems monitoring and alerting toolkit.
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
OpenTSDB - OpenTSDB is a distributed, scalable Time Series Database (TSDB) written on top of HBase.
Google Cloud Dataproc - Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost
