Based on our record, TimescaleDB should be more popular than Spark Streaming. It has been mentiond 5 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 / 8 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: almost 2 years 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: almost 3 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
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
Spark Streaming: The component for real-time data processing and analytics. - Source: dev.to / over 1 year ago
Is a big data framework and currently one of the most popular tools for big data analytics. It contains libraries for data analysis, machine learning, graph analysis and streaming live data. In general Spark is faster than Hadoop, as it does not write intermediate results to disk. It is not a data storage system. We can use Spark on top of HDFS or read data from other sources like Amazon S3. It is the designed... - Source: dev.to / over 2 years ago
InfluxData - Scalable datastore for metrics, events, and real-time analytics.
Confluent - Confluent offers a real-time data platform built around Apache Kafka.
Prometheus - An open-source systems monitoring and alerting toolkit.
Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.
VictoriaMetrics - Cost-effective database for huge amounts of time series data
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.