This post discusses which use cases can benefit from nested data types, how to use Amazon Redshift Spectrum with nested data types to achieve excellent performance and storage efficiency, and some of the limitations of nested data types. The JSON data I am trying to query has several fields which structure is fixed and expected. The first step in configuring the S3 Load component is to provide the Redshift table which the data in the S3 file is to be loaded into. I am trying to cast a variable type JSON field in Redshift Spectrum as a plane string but keep getting column type VARCHAR for column STRUCT is incompatible. Here is the most recent spectrum-s3.json ... You can also manually enter an IAM role if you don’t see it included the list (for example, if the IAM role hasn’t been created yet). Amazon Redshift Spectrum extends Redshift by offloading data to S3 for querying. Amazon Redshift Array Support and Alternatives – Example; Redshift JSON_EXTRACT_PATH_TEXT Function. However, it gets difficult and very time consuming for more complex JSON data such as the one found in the Trello JSON. Customers already have nested data in their Amazon S3 data lake. Amazon Redshift Spectrum supports the following formats AVRO, PARQUET, TEXTFILE, SEQUENCEFILE, RCFILE, RegexSerDe, ORC, Grok, CSV, Ion, and JSON. This approach works reasonably well for simple JSON documents. The given JSON path can be nested up to five levels. You create Redshift Spectrum tables by defining the structure for your files and registering them as tables in an external data catalog. Redshift Spectrum does not have the limitations of the native Redshift SQL extensions for JSON. This tutorial assumes that you know the basics of S3 and Redshift. When trying to query from Spectrum, however, it returns: Top level Ion/JSON structure must be an anonymous array if and only if serde property 'strip.outer.array' is set. Redshift Spectrum also scales intelligently. It is recommended by Amazon to use columnar file format as it takes less storage space and process and filters data faster and we can always select only the columns required. Nested data support enables Redshift customers to directly query their nested data from Redshift through Spectrum. As a best practice to improve performance and lower costs, Amazon suggests using columnar data formats such as Apache Parquet . Redshift Spectrum is a feature of Amazon Redshift that allows you to query data stored on Amazon S3 directly and supports nested data types. For example, commonly java applications often use JSON as a standard for data exchange. Redshift Spectrum can query data over orc, rc, avro, json,csv, sequencefile, parquet, and textfiles with the support of gzip, bzip2, and snappy compression. I am trying to use the copy command to load a bunch of JSON files on S3 to redshift. “Redshift Spectrum can directly query open file formats in Amazon S3 and data in Redshift in a … The JSON format is one of the widely used file formats to store data that you want to transmit to another server. The function JSON_EXTRACT_PATH_TEXT returns the value for the key:value pair referenced by a series of path elements in a JSON string. Based on the demands of your queries, Redshift Spectrum can potentially use thousands of instances to take advantage of massively parallel processing. Example structure of the JSON file is: { message: 3 time: 1521488151 user: 39283 information: { bytes: 2342343 speed: 9392 location: CA } } The JSON file format is an alternative to XML. Getting setup with Amazon Redshift Spectrum is quick and easy. Many web applications use JSON to transmit the application information. 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