Based on our record, Apache Flink should be more popular than Travis CI. It has been mentiond 27 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.
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 / 25 days 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
Due to the technology transformation we want to do recently, we started to investigate Apache Iceberg. In addition, the data processing engine we use in house is Apache Flink, so it's only fair to look for an experimental environment that integrates Flink and Iceberg. - Source: dev.to / 5 months ago
We used Travis CI for our continuous integration (CI) pipeline. Travis is a highly popular CI on Github and its build matrix feature is useful for repositories which contain multiple projects like Grab's. We configured Travis to do the following:. - Source: dev.to / over 1 year ago
CI/CD for autobuild + autotests (Codemagic or Travis CI). Source: over 1 year ago
Step 2: Log on to Travis CI and sign up with your GitHub account used above. - Source: dev.to / over 1 year ago
Some other hosted CI products, such as CircleCI and Travis Cl, are completely hosted in the cloud. It is becoming more popular for small organizations to use hosted CI products, as they allow engineering teams to begin continuous integration as soon as possible. Source: almost 3 years ago
1. Let's create the account. Access the site https://travis-ci.com/ and click on the button Sign up. - Source: dev.to / almost 3 years ago
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Jenkins - Jenkins is an open-source continuous integration server with 300+ plugins to support all kinds of software development
Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.
CircleCI - CircleCI gives web developers powerful Continuous Integration and Deployment with easy setup and maintenance.
Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.
Codeship - Codeship is a fast and secure hosted Continuous Delivery platform that scales with your needs.