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Intake-spark is an Intake plugin that provides a unified interface for loading and accessing data in Apache Spark using the Intake data catalog system. Spark is a powerful distributed computing framework for processing large-scale data, but working with it can be challenging because it requires specific knowledge of Spark's API and data sources. Intake-spark simplifies this process by providing a consistent and intuitive interface for loading data into Spark DataFrame. Intake-spark supports several file formats, including Apache Parquet, Avro, CSV, and JSON, and can read data from various storage systems such as HDFS, Amazon S3, and Azure Blob Storage. Intake-spark also allows users to configure advanced settings such as partitioning and caching for improved performance.

Spark SQL 

Using SQL in accessing Apache Spark DataFrames provides a convenient and familiar way for users to query and manipulate data, particularly for those who are already familiar with SQL. The Spark SQL module provides an interface for executing SQL queries against Spark DataFrames, making it easier for users to work with structured data and perform complex data analysis.

To use SQL in accessing Spark DataFrames, you first need to create a SparkSession object, which is the entry point to using Spark functionality. Then, you can load your data into a DataFrame, register the DataFrame as a temporary table, and execute SQL queries on the DataFrame using the Spark SQL module.

Using SQL in accessing Spark DataFrames can provide several benefits, including the ability to leverage the powerful query optimization and distributed processing capabilities of Spark. By executing SQL queries on Spark DataFrames, users can take advantage of Spark's multi-node, distributed processing capabilities to quickly process large volumes of data and gain valuable insights from it.

NCI software

NCI provides multiple platforms for users to use our data indexes, such as the interactive ARE JupyterLab session, or the Gadi PBS jobs.

NCI also provides software environments that each support the NCI data indexes:


Here we introduce the workflow for using intake-spark and spark SQL. User can adopt other working tools and methods to access the NCI data indexes.

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