Apache Arrow
Redis
Delta Lake
MongoDB
PostgreSQL
Apache Ignite
Apache Cassandra
Apache Parquet
Hadoop
Apache Spark
PostgreSQL
Apache Flink
Apache Cassandra
MongoDB
Apache Storm
Apache Kafka
Apache Arrow HadoopBased on our record, Apache Arrow should be more popular than Hadoop. It has been mentiond 34 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.
Apache Doris 2.1 has a data transmission channel built on Arrow Flight SQL. (Apache Arrow is a software development platform designed for high data movement efficiency across systems and languages, and the Arrow format aims for high-performance, lossless data exchange.) It allows high-speed, large-scale data reading from Doris via SQL in various mainstream programming languages. For target clients that also... - Source: dev.to / 15 days ago
In comes Polars: a brand new dataframe library, or how the author Ritchie Vink describes it... a query engine with a dataframe frontend. Polars is built on top of the Arrow memory format and is written in Rust, which is a modern performant and memory-safe systems programming language similar to C/C++. - Source: dev.to / 3 months ago
One is related to the heritage of being built around the NumPy library, which is great for processing numerical data, but becomes an issue as soon as the data is anything else. Pandas 2.0 has started to bring in Arrow, but it's not yet the standard (you have to opt-in and according to the developers it's going to stay that way for the foreseeable future). Also, pandas's Arrow-based features are not yet entirely on... - Source: dev.to / 6 months ago
IMO a good first step would be to use the txr FFI to write a library for Apache arrow: https://arrow.apache.org/. - Source: Hacker News / 6 months ago
Polars is an open-source library for Python, Rust, and NodeJS that provides in-memory dataframes, out-of-core processing capabilities, and more. It is based on the Rust implementation of the Apache Arrow columnar data format (you can read more about Arrow on my earlier blog post “Demystifying Apache Arrow”), and it is optimised to be blazing fast. - Source: dev.to / about 1 year ago
Did you check out tools like https://hadoop.apache.org/ ? Source: about 1 year ago
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. Source: about 1 year ago
There are several frameworks available for batch processing, such as Hadoop, Apache Storm, and DataTorrent RTS. - Source: dev.to / over 1 year ago
A copy of Hadoop installed on each of these machines. You can download Hadoop from the Apache website, or you can use a distribution like Cloudera or Hortonworks. - Source: dev.to / over 1 year ago
The Apache™ Hadoop™ project develops open-source software for reliable, scalable, distributed computing. - Source: dev.to / over 1 year ago
Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Delta Lake - Application and Data, Data Stores, and Big Data Tools
PostgreSQL - PostgreSQL is a powerful, open source object-relational database system.
MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.
Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.
