Parquet

1. Overview

Apache Parquet is a file format designed to support fast data processing for complex data, with several notable characteristics:

1. Columnar: Unlike row-based formats such as CSV or Avro, Apache Parquet is column-oriented – meaning the values of each table column are stored next to each other, rather than those of each record:

2. Open-source: Parquet is free to use and open source under the Apache Hadoop license, and is compatible with most Hadoop data processing frameworks. To quote the project website, “Apache Parquet is… available to any project… regardless of the choice of data processing framework, data model, or programming language.”

3. Self-describing: In addition to data, a Parquet file contains metadata including schema and structure. Each file stores both the data and the standards used for accessing each record – making it easier to decouple services that write, store, and read Parquet files.

Example Use Case: You have a parquet file that contains your Employee information. You want to use a batch sync to pull this info into a Cinchy table and liberate your data.

The Parquet source supports batch syncs.

2. Info Tab

You can review the parameters that can be found in the info tab below (Image 1).

Values

Parameter	Description	Example
Title	Mandatory. Input a name for your data sync	Employee Sync
Version	Mandatory. This is a pre-populated field containing a version number for your data sync. You can override it if you wish.	1.0.0
Parameters	Optional. Review our documentation on Parameters here for more information about this field.	Since we are doing a local upload, we use "@Filepath"

Image 1: The Info Tab

3. Source Tab

The following table outlines the mandatory and optional parameters you will find on the Source tab (Image 2).

Source Details

The following parameters will help to define your data sync source and how it functions.

Parameter	Description	Example
(Sync) Source	Mandatory. Select your source from the drop down menu.	Parquet
Source	The location of the source file. Either a Local upload, Amazon S3, or Azure Blob Storage The following authentication methods are supported per source: Amazon S3: Access Key ID/Secret Access Key Azure Blob Storage: Connection String	Local
Row Group Size	Mandatory. The size of your Parquer Row Groups. Review the documentation here for more on Row Group sizing.	The recommended disk block/row group/file size is 512 to 1024 MB on HDFS.
Path	Mandatory. The path to the source file to load. To upload a local file, you must first insert a Parameter in the Info tab of the connection (ex: filepath). Then, you would reference that same value in this location (Ex: @Filepath). This will then trigger a File Upload option to import your file.	@Filepath
Auth Type	This field defines the authentication type for your data sync. Cinchy supports "Access Key" and "IAM" role. When selecting "Access Key", you must provide the key and key secret. When selecting "IAM role", a new field will appear for you to paste in the role's Amazon Resource Name (ARN). You also must ensure that: The role must be configured to have at least read access to the source The Connections pods' role must have permission to assume the role specified in the data sync config

Schema

The Schema section is where you define which source columns you want to sync in your connection. You can repeat the values for multiple columns.

Parameter	Description	Example
Name	Mandatory. The name of your column as it appears in the source.	Name
Alias	Optional. You may choose to use an alias on your column so that it has a different name in the data sync.
Data Type	Mandatory. The data type of the column values.	Text
Description	Optional. You may choose to add a description to your column.

There are other options available for the Schema section if you click on Show Advanced.

Parameter	Description	Example
Mandatory	If both Mandatory and Validated are checked on a column, then rows where the column is empty are rejected If just Mandatory is checked on a column, then all rows are synced with the execution log status of failed, and the source error of "Mandatory Rule Violation" If just Validated is checked on a column, then all rows are synced.
Validate Data	If both Mandatory and Validated are checked on a column, then rows where the column is empty are rejected If just Validated is checked on a column, then all rows are synced.
Trim Whitespace	Optional if data type = text. If your data type was chosen as "text", you can choose whether to trim the whitespace (that is, spaces and other non-printing characters).

You can choose to add in a Transformation > String Replacement by inputting the following:

Parameter	Description	Example
Pattern	Mandatory if using a Transformation. The pattern for your string replacement, i.e. the string that will be searched and replaced.
Replacement	What you want to replace your pattern with.

Note that you can have more than one String Replacement

Filter

You have the option to add a source filter to your data sync. Please review the documentation here for more information on source filters.

4. Next Steps

• Configure your Destination

• Define your Sync Behaviour.

• Add in your Post Sync Scripts, if required.

• Define your Permissions.

• Click Jobs > Start a Job to begin your sync.