Based on our record, Apache Flink should be more popular than PostgreSQL. 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.
I’m on MacOS and erlang.org, elixir-lang.org, and postgresql.org all suggest installation via Homebrew, which is a very popular package manager for MacOS. - Source: dev.to / about 1 month ago
According to the documentation, crate sqlx is implemented in Rust, and it's database agnostic: it supports PostgreSQL, MySQL, SQLite, and MSSQL. - Source: dev.to / 8 months ago
Solution is just downloading and installilng pgAdmin from official pgAdmin homepage version, not the one that is included in the postgresql.org package. Source: 10 months ago
SQL immediately stands out here because it was designed for making relational algebra, the other side of the Entity-Relationship model, accessible. There are likely more people who know SQL than any programming language (for IaC) or data format you could choose to represent your cloud infrastructure. Many non-programmers know it, as well, such as data scientists, business analysts, accountants, etc, and there is... - Source: dev.to / about 1 year ago
Vapor[0] based on Swift. Advantage of this is that you don't have to evaluate multiple frameworks for Swift and suffer paralysis by analysis. All the Swift community is behind one framework. The next is Actix[1] based on Rust. There are many frameworks in Rust and most of them have not reached 1.0 And which framework will survive becomes a question. Other not so well-known is Wt[2] based on C++. This actually is... - Source: Hacker News / over 1 year 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 / 17 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 / 4 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 / 4 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 / 4 months ago
MySQL - The world's most popular open source database
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Microsoft SQL - Microsoft SQL is a best in class relational database management software that facilitates the database server to provide you a primary function to store and retrieve data.
Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.
SQLite - SQLite Home Page
Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.