Parquet to redshift data types12/28/2022 ![]() ![]() option( "tempdir", tempS3Dir) //User provides a temporary S3 folder. option( "url", jdbcURL) //Provide the JDBC URL. Val jdbcURL = "jdbc:redshift://.:5439/testredshift?user=redshift&password=W9P3GC42GJYFpGxBitxPszAc8iZFW" val tempS3Dir = "s3n://spark-redshift-testing/temp/" val salesDF = sqlContext.read The set of commands to load the Redshift table (query) data into a schema compliant DataFrame instance is: Say you want to process an entire table (or a query which returns a large number of rows) in Spark and combine it with a dataset from another large data source such as Hive. This will simplify ETL pipelines and allow users to operate on a logical and unified view of the system. However, this package will allow Redshift to interoperate seamlessly (via the Unified Data Sources API) with data stored in S3, Hive tables, CSV or Parquet files on HDFS. Traditionally, data had to be moved from HDFS to Redshift for analytics. We will also explore how this package expands the range of possibilities for Redshift as well as Spark users. To understand how it does so, let us look at how you would integrate large datasets from a Redshift database with datasets from other data sources. #Parquet to redshift data types manualUsing this package simplifies the integration with the Redshift service by automating the set of manual steps that would otherwise be required to move large amounts of data in and out of Redshift. For users hoping to load or store large volumes of data from/to Redshift, JDBC leaves much to be desired in terms of performance and throughput. The JDBC-based INSERT/UPDATE queries are only practical for small updates to Redshift tables. Furthermore, the use of JDBC to store large datasets in Redshift is only practical when data needs to be moved between tables inside a Redshift database. ![]() The reason being that JDBC provides a ResultSet based approach, where rows are retrieved in a single thread in small batches. While this method is adequate when running queries returning a small number of rows (order of 100’s), it is too slow when handling large-scale data. Prior to the introduction of Redshift Data Source for Spark, Spark’s JDBC data source was the only way for Spark users to read data from Redshift. This post discusses a new Spark data source for accessing the Amazon Redshift Service. Redshift Data Source for Spark is a package maintained by Databricks, with community contributions from SwiftKey and other companies. Third party data sources are also available via. Spark users can read data from a variety of sources such as Hive tables, JSON files, columnar Parquet tables, and many others. The Spark SQL Data Sources API was introduced in Apache Spark 1.2 to provide a pluggable mechanism for integration with structured data sources of all kinds. This is a guest blog from Sameer Wadkar, Big Data Architect/Data Scientist at Axiomine. ![]()
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