flink-statefun
Grafana
flink-statefun | Grafana | |
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18 | 379 | |
495 | 60,395 | |
1.4% | 0.7% | |
5.1 | 10.0 | |
5 months ago | 6 days ago | |
Java | TypeScript | |
Apache License 2.0 | GNU Affero General Public License v3.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
flink-statefun
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flink-statefun VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
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Snowflake - what are the streaming capabilities it provides?
When low latency matters you should always consider an ETL approach rather than ELT, e.g. collect data in Kafka and process using Kafka Streams/Flink in Java or Quix Streams/Bytewax in Python, then sink it to Snowflake where you can handle non-critical workloads (as is the case for 99% of BI/analytics). This way you can choose the right path for your data depending on how quickly it needs to be served.
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JR, quality Random Data from the Command line, part I
Sometimes we may need to generate random data of type 2 in different streams, so the "coherency" must also spread across different entities, think for example to referential integrity in databases. If I am generating users, products and orders to three different Kafka topics and I want to create a streaming application with Apache Flink, I definitely need data to be coherent across topics.
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Brand Lift Studies on Reddit
The Treatment and Control audiences need to be stored for future low-latency, high-reliability retrieval. Retrieval happens when we are delivering the survey, and informs the system which users to send surveys to. How is this achieved at Reddit’s scale? Users interact with ads, which generate events that are sent to our downstream systems for processing. At the output, these interactions are stored in DynamoDB as engagement records for easy access. Records are indexed on user ID and ad campaign ID to allow for efficient retrieval. The use of stream processing (Apache Flink) ensures this whole process happens within minutes, and keeps audiences up to date in real-time. The following high-level diagram summarizes the process:
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Query Real Time Data in Kafka Using SQL
Most streaming database technologies use SQL for these reasons: RisingWave, Materialize, KsqlDB, Apache Flink, and so on offering SQL interfaces. This post explains how to choose the right streaming database.
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How to choose the right streaming database
Apache Flink.
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5 Best Practices For Data Integration To Boost ROI And Efficiency
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.
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Forward Compatible Enum Values in API with Java Jackson
We’re not discussing the technical details behind the deduplication process. It could be Apache Flink, Apache Spark, or Kafka Streams. Anyway, it’s out of the scope of this article.
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Which MQTT (or similar protocol) broker for a few 10k IoT devices with quite a lot of traffic?
One can also consider https://flink.apache.org/ instead of Kafka for connecting a large number of devices.
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Apache Pulsar vs Apache Kafka - How to choose a data streaming platform
Both Kafka and Pulsar provide some kind of stream processing capability, but Kafka is much further along in that regard. Pulsar stream processing relies on the Pulsar Functions interface which is only suited for simple callbacks. On the other hand, Kafka Streams and ksqlDB are more complete solutions that could be considered replacements for Apache Spark or Apache Flink, state-of-the-art stream-processing frameworks. You could use them to build streaming applications with stateful information, sliding windows, etc.
Grafana
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Docker Log Observability: Analyzing Container Logs in HashiCorp Nomad with Vector, Loki, and Grafana
Monitoring application logs is a crucial aspect of the software development and deployment lifecycle. In this post, we'll delve into the process of observing logs generated by Docker container applications operating within HashiCorp Nomad. With the aid of Grafana, Vector, and Loki, we'll explore effective strategies for log analysis and visualization, enhancing visibility and troubleshooting capabilities within your Nomad environment.
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Golang: out-of-box backpressure handling with gRPC, proven by a Grafana dashboard
To help us visualize these scenarios, we'll build a Grafana Dashboard so we can follow along.
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Monitoring, Observability, and Telemetry Explained
Visualization and Analysis: Choose a tool with intuitive and customizable dashboards, charts, and visualizations. A question to ask is, "Are the visualization features of this tool user-friendly and adaptable to our team's specific needs?" Tools like Grafana and Kibana provide powerful visualization capabilities.
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4 facets of API monitoring you should implement
Prometheus: Open-source monitoring system. Often used together with Grafana.
- Grafana: Open and composable observability and data visualization platform
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The Mechanics of Silicon Valley Pump and Dump Schemes
Grafana
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Reverse engineering the Grafana API to get the data from a dashboard
Yes I'm aware that Grafana is open source but the method I used to find the API endpoints is far quicker than digging through hundreds of files in a codebase I'm not familiar with.
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Building an Observability Stack with Docker
So, you will add one last container to allow us to visualize this data: Grafana, an open-source analytics and visualization platform that allows us to see traces and metrics simply. You can set Grafana to read data from both Tempo and Prometheus by setting them as datastores with the following grafana.datasource.yaml config file:
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How to collect metrics from node.js applications in PM2 with exporting to Prometheus
In example above, we use 2 additional parameters: code (HTTP response code) and page (page identifier), which provide detailed statistics. For example, you can build such graphs in Grafana:
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Root Cause Chronicles: Quivering Queue
Robin switched to the Grafana dashboard tab, and sure enough, the 5xx volume on web service was rising. It had not hit the critical alert thresholds yet, but customers had already started noticing.
What are some alternatives?
opensky-api - Python and Java bindings for the OpenSky Network REST API
Thingsboard - Open-source IoT Platform - Device management, data collection, processing and visualization.
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
Apache Superset - Apache Superset is a Data Visualization and Data Exploration Platform [Moved to: https://github.com/apache/superset]
debezium - Change data capture for a variety of databases. Please log issues at https://issues.redhat.com/browse/DBZ.
Heimdall - An Application dashboard and launcher
redpanda - Redpanda is a streaming data platform for developers. Kafka API compatible. 10x faster. No ZooKeeper. No JVM!
Wazuh - Wazuh - The Open Source Security Platform. Unified XDR and SIEM protection for endpoints and cloud workloads.
Apache Pulsar - Apache Pulsar - distributed pub-sub messaging system
Thingspeak - ThingSpeak is an open source “Internet of Things” application and API to store and retrieve data from things using HTTP over the Internet or via a Local Area Network. With ThingSpeak, you can create sensor logging applications, location tracking applications, and a social network of things with status updates.
faust - Python Stream Processing. A Faust fork
uptime-kuma - A fancy self-hosted monitoring tool