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Welcome to HEAVY.AI Documentation
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Overview
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Loading and Exporting Data
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Data Definition (DDL)
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Introduction to Heavy Immerse
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Measures and Dimensions
Using Parameters
Using Filters
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Data Science Foundation
JupyterLab Installation and Configuration
Using HEAVY.AI with JupyterLab
Introduction to Ibis
Interactive Data Exploration with Altair
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UDF and UDTF
Remote Backend Compiler (RBC)
APIs and Interfaces
Overview
heavysql
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Creating a Kafka Streaming Application
Getting Started with Open Source
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HeavyConnect
HeavyConnect Release Overview
Getting Started
Best Practices
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Command Reference
Parquet Data Wrapper Reference
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Getting Started

Foreign Wrappers, Servers, Tables, and User Mappings

HeavyConnect introduces four new types of database objects to HeavyDB. These objects follow the ISO/IEC 9075-9 (SQL/MED) standard, which defines constructs for managing external data for a SQL database.
Foreign data wrappers
Foreign servers
Foreign tables
User mappings
A foreign data wrapper represents an adapter object that connects HeavyDB to an external data source and accesses required data. Different types of data wrappers exist for different data formats.
A foreign server object represents a named external datasource, along with configuration information required to access the datasource. Server objects are referenced during foreign table creation to indicate the backing datasource. A foreign server object can be reused by multiple FOREIGN TABLES when the same external datasource is used. Three local file server objects, default_local_delimited, default_local_parquet, and default_local_regex_parsed, are available for use directly in the CREATE FOREIGN TABLE statement when a custom server is not used.
A foreign table acts as a proxy to an external data source for which access details are provided by a server object. A foreign table provides an interface for querying data from an external datasource in a similar way as for a local table.
A user mapping is an object that contains access credentials to an external datasource/server. User mappings provide secure handling of credentials when access to restricted external datasources or foreign servers is required.

Organizing Your Data

For file data sources, you can use directory partitioning to organize and filter your data. This can include hard and soft links in your directory structure. HeavyConnect processes all files in a directory or can reference a single file. You can structure directories to make it easier to introduce new data for processing.
For example, using the following graphic as a reference, you can:
  1. 1.
    Include all records from 2020
  2. 2.
    Include all records in March 2020
  3. 3.
    An individual record in March 2020
  4. 4.
    All records YoY for January (softlink)
Refer to AWS documentation on organizing your data and how folders are represented in S3.
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Contents
Foreign Wrappers, Servers, Tables, and User Mappings
Organizing Your Data