flink-statefun
PostgreSQL
flink-statefun | PostgreSQL | |
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18 | 408 | |
495 | 14,734 | |
1.4% | 2.0% | |
5.1 | 10.0 | |
5 months ago | 5 days ago | |
Java | C | |
Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
PostgreSQL
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System Design: Databases and DBMS
PostgreSQL
- Presentación del Operador LMS Moodle
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Introducing LMS Moodle Operator
The LMS Moodle Operator serves as a meta-operator, orchestrating the deployment and management of Moodle instances in Kubernetes. It handles the entire stack required to run Moodle, including components like Postgres, Keydb, NFS-Ganesha, and Moodle itself. Each of these components has its own Kubernetes Operator, ensuring seamless integration and management.
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Integrate txtai with Postgres
Another key feature of txtai is being able to quickly move from prototyping to production. This article will demonstrate how txtai can integrate with Postgres, a powerful, production-ready and open source object-relational database system. After txtai persists content to Postgres, we'll show it can be directly queried with SQL from any Postgres client
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Understanding SQL vs. NoSQL Databases: A Beginner's Guide
SQL (Structured Query Language) databases are relational databases. They organize data into tables with rows and columns, and they use SQL for querying and managing data. Examples include MySQL, PostgreSQL, and SQLite.
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From zero to hero: using SQL databases in Node.js made easy
Node.js, MySQL and PostgreSQL servers installed on your machine
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I Deployed My Own Cute Lil’ Private Internet (a.k.a. VPC)
Each app’s front end is built with Qwik and uses Tailwind for styling. The server-side is powered by Qwik City (Qwik’s official meta-framework) and runs on Node.js hosted on a shared Linode VPS. The apps also use PM2 for process management and Caddy as a reverse proxy and SSL provisioner. The data is stored in a PostgreSQL database that also runs on a shared Linode VPS. The apps interact with the database using Drizzle, an Object-Relational Mapper (ORM) for JavaScript. The entire infrastructure for both apps is managed with Terraform using the Terraform Linode provider, which was new to me, but made provisioning and destroying infrastructure really fast and easy (once I learned how it all worked).
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How to dump and restore a Postgres DB with new table ownership
I've used MySQL for years. But recently, I found myself working PostgreSQL and simple things like dumping and restoring a database are different enough that I decided to document the process. It's straightforward enough once I knew how.
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Test Driving a Rails API - Part One
A running Rails application needs a database to connect to. You may already have your database of choice installed, but if not, I recommend PostgreSQL, or Postgres for short. On a Mac, probably the easiest way to install it is with Posrgres.app. Another option, the one I prefer, is to use Homebrew. With Homebrew installed, this command will install PostgreSQL version 16 along with libpq:
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Um júnior e um teste técnico: The battle.
PostgreSQL
What are some alternatives?
opensky-api - Python and Java bindings for the OpenSky Network REST API
psycopg2 - PostgreSQL database adapter for the Python programming language
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
ClickHouse - ClickHouse® is a free analytics DBMS for big data
debezium - Change data capture for a variety of databases. Please log issues at https://issues.redhat.com/browse/DBZ.
phpMyAdmin - A web interface for MySQL and MariaDB
redpanda - Redpanda is a streaming data platform for developers. Kafka API compatible. 10x faster. No ZooKeeper. No JVM!
Firebird - FB/Java plugin for Firebird
Apache Pulsar - Apache Pulsar - distributed pub-sub messaging system
Adminer - Database management in a single PHP file
faust - Python Stream Processing. A Faust fork
SQLAlchemy - The Database Toolkit for Python