InfluxData
TimescaleDB
Prometheus
MongoDB
MapR Converged Data Platform
Amazon EMR
Google Cloud Dataproc
OpenTSDB
Apache Flink
Apache Spark
Amazon Kinesis
Spring Framework
Spark Mail
Apache Airflow
Confluent
Grails
InfluxData Apache FlinkBased on our record, Apache Flink seems to be a lot more popular than InfluxData. While we know about 28 links to Apache Flink, we've tracked only 2 mentions of InfluxData. 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.
I would highly recommend using a proper Time Series Database like QuestDB or InfluxDB to do this instead. You can always export data from wither of those two into Excel if your boss wants it in excel, but it's much easier to do data transformations, create graphs and reports, etc. If you have all the data in a proper database. Source: over 2 years ago
I would suggest using something better suited to IoT data than ... a spreadsheet. I'd recommend looking at one of the Time Series Databases for this. 1) QuestDB or 2) InfluxDB as these are much better suited to streaming data. Source: over 2 years 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 / 3 days 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 / about 1 month 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 / 3 months ago
Also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc. - Source: dev.to / 5 months ago
Apache SeaTunnel is a data integration platform that offers the three pillars of data pipelines: sources, transforms, and sinks. It offers an abstract API over three possible engines: the Zeta engine from SeaTunnel or a wrapper around Apache Spark or Apache Flink. Be careful, as each engine comes with its own set of features. - Source: dev.to / 5 months ago
TimescaleDB - TimescaleDB is a time-series SQL database providing fast analytics, scalability, with automated data management on a proven storage engine.
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.
Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.
MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.
Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.
