{'fraction_non_alphanumeric': 0.018094089264173704, 'fraction_numerical': 0.0012062726176115801, 'mean_word_length': 5.587301587301587, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Unified Spatial Analytics from Heterogeneous Sources with Amazon Redshift

Enterprise companies use spatial data for decision optimization and gain new insights regarding the locality of their business and services. Industries rely on efficiently combining spatial and business data from different sources, such as data warehouses, geospatial information systems, transactional systems, and data lakes, where spatial data can be found in structured or unstructured form. In this demonstration we present the spatial functionality of Amazon Redshift and its integration with other Amazon services, such as Amazon Aurora PostgreSQL and Amazon S3. We focus on the design and functionality of the feature, including the extensions in Redshift's state-of-the-art optimizer to push spatial processing close to where the data is stored.