Apache Parquet VS Apache Hadoop

Apache Parquet

This go implementation, other than common advantages from go itself (small single executable, support multiple platforms, speed, etc.), has some neat features compare with Java parquet tool and Python one like:

Hi, I've been trying to write parquet files on android for the past couple of days, and have really been struggling to find a solution. My original hypothesis was to just use the java parquet implementation (https://github.com/apache/parquet-mr), but I've since realized that not all java libraries play well with Android. I've gone through essentially dependency hell trying to franken-fit the library into my project, and imported as much as i could before hitting walls such as this one (https://github.com/mockito/mockito/issues/841).

Like many of you probably do, I tend to work with Parquet files a lot. parquet-tools has been my tool of choice for inspecting parquet files, but that has been deprecated recently. So, I created a replacement for it using Rust and Apache Arrow.

Did you check out tools like https://hadoop.apache.org/ ?

check out the various dockerfiles bundled with hadoop on GitHub. you can point to them from within docker-compose. they haven't been updated in a couple years tho.

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.

There are several frameworks available for batch processing, such as Hadoop, Apache Storm, and DataTorrent RTS.

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.

The Apache™ Hadoop™ project develops open-source software for reliable, scalable, distributed computing.

So, clearly with your AP CS class and PLC logic knowledge, if you were dumped into a codebase like Hadoop, QT, or TensorFlow you'd be able to quickly and competently analyze what is going on with that code, understand all the libraries used, know the reasons why certain compromises were made, and be able to make suggestions on how to restructure the code in a different way? Because I've been programming for coming up on two decades and unless a system is within the domains that I have experience in, I would not be able to provide any useful information without a massive onboarding timeline, and definitely wouldn't be able to help redesign anything until actually coding within the system for a significant amount of time.

This requires the use of distributed computation tools such as Spark and Hadoop, Flink and Kafka are used. But for occasional experimentation, Pandas, Geopandas and Dask are some of the commonly used tools.