Based on our record, Apache Parquet should be more popular than Apache ORC. It has been mentiond 19 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.
The information can be stored in a database or as files, serialized in a standard format and with a schema agreed with your Data Engineering team. Depending on your information and requirements, it can be as simple as CSV, XML or JSON, or Big Data formats such as Parquet, Avro, ORC, Arrow, or message serialization formats like Protocol Buffers, FlatBuffers, MessagePack, Thrift, or Cap'n Proto. - Source: dev.to / over 1 year ago
Data formatting is another place to make gains. When dealing with huge amounts of data, finding the data you need can take up a significant amount of your compute time. Apache Parquet and Apache ORC are columnar data formats optimized for analytics that pre-aggregate metadata about columns. If your EMR queries column intensive data like sum, max, or count, you can see significant speed improvements by reformatting... - Source: dev.to / over 2 years ago
The following stack captures layers of software components that make up Hudi, with each layer depending on and drawing strength from the layer below. Typically, data lake users write data out once using an open file format like Apache Parquet/ORC stored on top of extremely scalable cloud storage or distributed file systems. Hudi provides a self-managing data plane to ingest, transform and manage this data, in a... - Source: dev.to / almost 3 years ago
The Apache Spark / Databricks community prefers Apache parquet or Linux Fundation's delta.io over json. Source: 5 months ago
Apache Parquet (Parquet for short), which nowadays is an industry standard to store columnar data on disk. It compress the data with high efficiency and provides fast read and write speeds. As written in the Arrow documentation, "Arrow is an ideal in-memory transport layer for data that is being read or written with Parquet files". - Source: dev.to / 12 months ago
Googling that suggests this page: https://parquet.apache.org/. Source: about 1 year ago
You should also consider distribution of data because in a company that has machine learning workflows, the same data may need to go through different workflows using different technologies and stored in something other than a data warehouse, e.g. Feature engineering in Spark and loaded/stored in binary format such as Parquet in a data lake/object store. Source: about 1 year ago
This section will teach you how to read and write data to and from a variety of file types, including CSV, Excel, SQL, HTML, Parquet, JSON etc. You’ll also learn how to manipulate data from other sources, such as databases and web sites. Source: about 1 year ago
Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.
Apache Arrow - Apache Arrow is a cross-language development platform for in-memory data.
Apache Kudu - Apache Kudu is Hadoop's storage layer to enable fast analytics on fast data.
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Impala - Impala is a modern, open source, distributed SQL query engine for Apache Hadoop.
SQream - SQream empowers organizations to analyze the full scope of their Massive Data, from terabytes to petabytes, to achieve critical insights which were previously unattainable.