What is ORC?

ORC (Optimized Row Columnar) is an open-source columnar storage file format for Hadoop and big data systems. It was originally developed by Hortonworks as a way to improve data processing and analytics performance. ORC is designed to optimize compression, data skipping, and predicate pushdowns for efficient and high-performance query execution.

How ORC works

ORC organizes data into columns rather than rows, which allows for better compression and faster data retrieval. It uses a combination of compression techniques, including dictionary encoding, run-length encoding, and delta encoding, to reduce the storage footprint. ORC also supports predicate pushdowns, which means filtering is done at the storage level, reducing the amount of data that needs to be read and processed.

Why ORC is important

ORC offers several benefits that make it important for businesses:

  • Improved query performance: ORC's columnar storage and compression techniques allow for faster query execution, as only the relevant columns need to be read from disk.
  • Reduced storage costs: The efficient compression methods used by ORC can significantly reduce the amount of storage required for large datasets, resulting in cost savings.
  • Optimized analytics: ORC's support for predicate pushdowns and data skipping enables faster analytical queries, improving the speed and efficiency of data analysis.
  • Compatibility: ORC is widely supported by various big data processing frameworks and tools, making it easy to integrate into existing data pipelines.

Important ORC use cases

ORC is commonly used in the following use cases:

  • Data warehousing: ORC's columnar storage and compression make it well-suited for data warehousing applications, where fast query performance and efficient storage are critical.
  • big data analytics: ORC enables faster and more efficient analysis of large datasets, making it ideal for big data analytics use cases, such as ad hoc querying, reporting, and machine learning.
  • Data archiving: Due to its efficient storage capabilities, ORC is often used for long-term data archiving, where space optimization is essential.

ORC is closely related to the following technologies and terms:

  • Parquet: Parquet is another popular columnar storage file format for big data systems that offers similar benefits as ORC.
  • Hadoop: ORC is commonly used in Hadoop environments, where it can take advantage of the distributed processing capabilities provided by the Hadoop ecosystem.
  • Apache Arrow: Apache Arrow is an in-memory columnar data format that can be used with ORC and other file formats to further optimize data processing and interchange between different tools.

Why Dremio users would be interested in ORC

Dremio, a data lakehouse platform, provides users with the ability to easily access, analyze, and optimize their data. ORC aligns well with Dremio's capabilities and can bring several benefits to Dremio users:

  • Improved performance: By leveraging ORC's columnar storage and compression techniques, Dremio users can experience faster query performance and reduced time-to-insight.
  • Cost savings: ORC's efficient storage capabilities can help Dremio users save on storage costs, especially when dealing with large volumes of data.
  • Effective data analysis: ORC's support for predicate pushdowns and data skipping enhances Dremio's ability to optimize data processing and improve analytical query performance.
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