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PostgreSQL
Mirror of the official PostgreSQL GIT repository. Note that this is just a *mirror* - we don't work with pull requests on github. To contribute, please see https://wiki.postgresql.org/wiki/Submitting_a_Patch
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risingwave
Cloud-native SQL stream processing, analytics, and management. KsqlDB and Apache Flink alternative. 🚀 10x more productive. 🚀 10x more cost-efficient.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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MySQL
MySQL Server, the world's most popular open source database, and MySQL Cluster, a real-time, open source transactional database.
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Pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
We know that real-time data is data that is generated and processed immediately, as it is collected from different data sources. Sources can be typical databases such as Postgres or MySQL, and message brokers like Kafka. A real-time data visualization consists of a few steps, first we ingest, then process, and finally show this data in a dashboard.
In the demo tutorial, we'll leverage the following GitHub repository with RisingWave demos where we assume that all necessary things are set up using Docker compose. You can check other ways to run RisingWave on the official website. We have a Kafka topic named delivery_orders that contains events for every order placed on a food delivery website. Each event includes information about the order, such as the order ID, restaurant ID, and delivery status. The workload generator (Python script called Datagen) simulates generating of random mock data continuously and streams them into Kafka topics. In reality, this mock data can be replaced with data coming from your web app or backend service.
We know that real-time data is data that is generated and processed immediately, as it is collected from different data sources. Sources can be typical databases such as Postgres or MySQL, and message brokers like Kafka. A real-time data visualization consists of a few steps, first we ingest, then process, and finally show this data in a dashboard.
To install Dash, you can also refer to Dash installation guide on the website. Basically, we need to install two libraries (Dash itself and Pandas) by running the following pip install command:
We know that real-time data is data that is generated and processed immediately, as it is collected from different data sources. Sources can be typical databases such as Postgres or MySQL, and message brokers like Kafka. A real-time data visualization consists of a few steps, first we ingest, then process, and finally show this data in a dashboard.