In this section we will show how to control the various schemes that the 本製品 offers to bridge the gap with relational SQL and nested Parquet services. The CData Python Connector for Parquet provides a managed way for you to use the two prevailing techniques for dealing with nested Parquet data:

• Parsing the data structure and building a relational model based on the existing hierarchy.
• Drilling down into the nested arrays and objects using horizontal flattening.

Parsing Hierarchical Data

By default, the 本製品 automatically detects the rows in a document, so that you do not need to know the structure of the underlying data to query it with SQL. Set the DataModel property to choose a basic configuration of how the 本製品 models object arrays into tables. Set the FlattenObjects and FlattenArrays properties to configure how nested data is flattened into columns. See 階層データの解析 for a guide.