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Apache ORC VS Apache Arrow

Compare Apache ORC VS Apache Arrow and see what are their differences

Apache ORC logo Apache ORC

Apache ORC is a columnar storage for Hadoop workloads.

Apache Arrow logo Apache Arrow

Apache Arrow is a cross-language development platform for in-memory data.
  • Apache ORC Landing page
    Landing page //
    2022-09-18
  • Apache Arrow Landing page
    Landing page //
    2021-10-03

Apache ORC videos

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Apache Arrow videos

Wes McKinney - Apache Arrow: Leveling Up the Data Science Stack

More videos:

  • Review - "Apache Arrow and the Future of Data Frames" with Wes McKinney
  • Review - Apache Arrow Flight: Accelerating Columnar Dataset Transport (Wes McKinney, Ursa Labs)

Category Popularity

0-100% (relative to Apache ORC and Apache Arrow)
Databases
21 21%
79% 79
Big Data
44 44%
56% 56
NoSQL Databases
0 0%
100% 100
Data Dashboard
100 100%
0% 0

User comments

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Social recommendations and mentions

Based on our record, Apache Arrow seems to be a lot more popular than Apache ORC. While we know about 34 links to Apache Arrow, we've tracked only 3 mentions of Apache ORC. 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 ORC mentions (3)

  • Java Serialization with Protocol Buffers
    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
  • AWS EMR Cost Optimization Guide
    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
  • Apache Hudi - The Streaming Data Lake Platform
    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

Apache Arrow mentions (34)

  • Arrow Flight SQL in Apache Doris for 10X faster data transfer
    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 / 2 days ago
  • How moving from Pandas to Polars made me write better code without writing better code
    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 / 2 months ago
  • Time Series Analysis with Polars
    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 / 5 months ago
  • TXR Lisp
    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 / 5 months ago
  • A Polars exploration into Kedro
    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 / 12 months ago
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What are some alternatives?

When comparing Apache ORC and Apache Arrow, you can also consider the following products

Apache Parquet - Apache Parquet is a columnar storage format available to any project in the Hadoop ecosystem.

Delta Lake - Application and Data, Data Stores, and Big Data Tools

Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.

Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.

Apache Kudu - Apache Kudu is Hadoop's storage layer to enable fast analytics on fast data.

Impala - Impala is a modern, open source, distributed SQL query engine for Apache Hadoop.