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Table of contents
1.
Introduction
2.
Getting Started with Trifacta
2.1.
Object Overview
2.2.
Import Basics
2.3.
Transform Basics
2.4.
Goal
2.5.
Recommended Methods for Building Recipes
2.6.
Sample 
2.7.
Cleanse
2.8.
Assess Data Quality
2.9.
Modify 
2.10.
Enrichment
2.11.
Sampling
2.12.
Profile
2.13.
Running Job Basics
2.14.
Configure Job 
2.15.
Run Job 
2.16.
Iterate
2.17.
Export Basics
3.
Setup of Trifacta
3.1.
Getting Started with Dataprep
3.1.1.
Set up a project
3.1.2.
Set up your storage bucket
3.1.3.
Set up your staging bucket
3.1.4.
Whitelist the IP address range of Dataprep
3.2.
Supported File Formats
3.2.1.
File Format
3.2.2.
Compressing Algorithms
4.
Common Tasks in Trifacta
4.1.
Import
4.2.
Discovery
4.3.
Validation
4.4.
Structuring
4.5.
Cleanse
4.6.
Enrichment
4.7.
Publishing
4.8.
Project Management
5.
Frequently Asked Questions
5.1.
Does Google own Trifacta?
5.2.
What is dataprep used for?
5.3.
Who uses Trifacta?
5.4.
From where can data be imported in dataprep?
5.5.
Is DataPrep open source?
6.
Conclusion
Last Updated: Mar 27, 2024

Dataprep by Trifacta

Author Aditi
0 upvote
Leveraging ChatGPT - GenAI as a Microsoft Data Expert
Speaker
Prerita Agarwal
Data Specialist @
23 Jul, 2024 @ 01:30 PM

Introduction

Have you ever worked on any data preparation platform? Or have you worked with a lot of data and cannot handle it?

Trifacta is a platform for data preparation. Many modern businesses employ it for its reliability and speed. It empowers and allows everyone to prepare diver and messy data faster. You can use the trifacta interactive platform to present visual data for exploration. It can be used for machine learning, analytics, or standard reporting.

In this article, we will understand Trifacta and its dataprep in detail.

Dataprep by Trifacta

Getting Started with Trifacta

Object Overview

In this section, we will explore the objects that can be created in Trifacta and their relationships. The sample data is transformed by creating the following:

  • Flows: It is the basic unit for organizing the work. It is a container used to hold one or more datasets. The datasets can be associated with recipes and other objects. It is also a means for packaging Dataprep by Trifacta objects. It can be for different types of actions. They are as follows:-
    • Copying
    • Creating relationships
    • Executing pre-configured jobs
    • Creating references between external flows and recipes.
  • Imported datasets: These are the data that are imported to the platform. It is a reference to the original data. Data may not exist within the platform. It can reference a database table, a file, multiple files, or other data types. When it is added to a flow, then it becomes usable.
    • It can be referenced in recipes.
    • They can be created by the Imported Data page.
  • Recipes: It is a user-defined step sequence. It is applied to transform or change a dataset. Outputs and references are associated with each recipe in a flow. The object of the recipe is created from an imported dataset. It can be created from another recipe too. You can chain recipes together by creating a recipe from another. Recipes are interpreted by Dataprep. 

Import Basics

Dataprep can import a variety of flat file formats by Trifacta. It can also be imported from other distributed sources. A reference to data imported is stored by the platform as an imported dataset. The steps are as follows:- 

  • Log in to the application.
  • Click Library in the menu bar. Click Import Data.
  • Follow the following steps to add a dataset:
    • Select the location. The location must be where your source is located.
    • Upload:
      • Select Upload to upload a file from your local desktop. You can also select multiple files to upload.
      • Select the needed files for your source and click Open.
    • Backend Storage (like S3):
      • Navigate and select files for your source. One or more files can be selected.
      • Click the plus icon to upload the dataset in the queue next to its name.
      • You can select multiple files.
    • Click Add to insert a new flow checkbox. This will create a new flow for you, Dataprep. The imported datasets are also added.
  • Click Continue to begin working with the dataset.
  • The imported dataset and its flow are created.
  • You can begin working on the Transformer page with the dataset.

Transform Basics

The transformer page is opened when you edit your dataset recipe. It is the place where you begin with wrangling tasks on a sample dataset. You build your transformation recipe from this interface. Results can also be seen in real-time in the sample. You can run a job against the complete dataset once you are happy with what you see.

Goal

Data transformations have done when all the following are done

  • Deleted any incorrect, missing, or incomplete values from your data
  • Data from other datasets can be added to your dataset as needed.
  • Change your dataset's values to follow the desired schema.
  • Executed job against the entire dataset
  • Exported the results from your recipe and dataset for use in further systems

Recommended Methods for Building Recipes

Dataprep supports the following methods by Trifacta. It is used for building recipes on the Transformation page. These methods are listed below in order of ease to use:-

  • Select something: You are presented with a list of ideas for steps you can do. It is based on the selection or patterns that match the selection when you choose data elements on the Transformer page. You can select columns or one or more values from a column.
  • Toolbar and column menus: Through the Transformer toolbar or the column context menus on the Transformer page, you can access pre-configured transformations.
  • Search and browse for transformations: You can assemble recipe steps using the Transform Builder and the Search panel. It can be done through a simple, menu-driven interface.

Sample 

It is challenging to load large datasets in Dataprep. It can overload the browser or can affect the performance of your browser. So the application can work on sample data. When the results are satisfying, you can apply the job to the entire dataset. The first set of rows of source data is the default sample.

Cleanse

It addresses the issues in data quality. It can be categorized as follows:-

  • Consistency
  • Validity
  • Reliability

When data is imported, it contains some parts that are not required in our final output. It can be multiple rows, columns, or specific values. So this phase focuses on the following activities:-

  • Change data types
  • Remove unused columns
  • Correcting mismatched and missing data
  • Improve validity, consistency, and reliability of data

Assess Data Quality

You can develop standards for data quality. It applies to the specific details of your dataset. For example, check the sqFt field for values less than zero if your dataset contains square footage for commercial rental properties. It can be a data quality rule. These values are highlighted in red in a data quality bar for the rule. It is for simple review and triage.

Modify 

After cleansing your data, you may need to modify the datasets. It is done to format the datasets for the targeted systems properly. You can also specify the level of aggregation, or any other modification can also be done.

Enrichment

You need to augment or enhance the dataset before you deliver it to the target system. It can be the addition of new columns or values from other datasets.

Sampling

Data present on the Transformation page is the sample of your entire dataset.

  • The sample is your complete dataset if it is small enough.
  • Dataprep by Trifacta automatically creates an initial data sample from your dataset. It is the first row of the dataset for larger datasets.

Profile

You can create and examine visual profiles of specific columns as part of the transformation process. It can be for your entire dataset also. Finding outliers, anomalies, and other problems with your data can be made much easier with the help of these interactive profiles.

Running Job Basics

In this section, you will learn about the overview of running job basics. 

Configure Job 

Click Run when you are ready to test your recipe with the entire dataset. It is present on the Transformer page. The output format and any compressions that need to be applied are specified on the Run Job page. Compression is not required until you are working with a large dataset.

Run Job 

Click  Run to queue jobs for execution. The job is then queued up for processing. You can track all the progress on the Job Details page. Click the Profile tab if visual profiling for the job was enabled. The results can be seen in the Output Destinations tab when a job is completed.

Iterate

You can see and review the recipe's transformation effects on the Job Details page in the Profile tab. It can be seen across the entire dataset. Data histograms and statistics provide a broad understanding of how well your transformation recipe works.

Export Basics

You can export the transformed data once you have improved your recipe and obtained a satisfactory result.

Steps

  • Click the Job History icon present in the left navbar.
  • Click the Job identifier on the Job History page. It will open the job on the Job Details page.
  • Select the Output Destinations tab.

Export by

  • Direct file download: Select the file you want to download. Click Download the result from the menu on the right-hand side.
  • Create a new dataset: New datasets can be created from a generated output. Click on the file. Click Create imported dataset from its right-hand side context menu.
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Setup of Trifacta

Getting Started with Dataprep

Dataprep enables you to transform disparate datasets. It can be of any size into usable data for your entire enterprise. You can explore, ingest, and transform your data from this leading-edge interface. It reduces the time of preparation of your data. Dataprep is integrated with the Google Cloud platform. It is operated by the Alteryx partner.

Set up a project

You should have the set up in the Google Cloud Platform to use any product edition.

Set up or create a Google Cloud project: Select a Cloud project on the project selector page in the Cloud Console.

Enable billing on that project: Billing should be enabled for your Google Cloud project. 

Enable services: Enable the services in your project given below:-

  • BigQuery
  • Dataflow
  • Cloud Storage APIs

Set up your storage bucket

You should have a bucket set up on Cloud storage for use with your project. Navigate the Cloud Storage Browser page in the Cloud Console. Click on Create bucket.

Specify the following attributes in the Create bucket dialog:-

  • Unique bucket name
  • Storage class
  • A location where bucket data will be stored

Set up your staging bucket

A Cloud Storage staging bucket for Dataflow use is established for you in a US region by default. It is when you enable Dataprep by Trifacta in a project. This staging bucket is necessary for using the product. It is used to store assets for usage in Dataflow processes. If you already have the authorization to build storage buckets in the US, you can move on to the next section. In this case, you don't need to create a storage bucket for staging.

Google Console and Google CLI can be used to create a bucket. The staging bucket can be changed by the following:

  • When a project's product is enabled, you could choose a different staging bucket if necessary.
  • When the product has been enabled, individual users can set up the bucket to use for staging their assets.

Whitelist the IP address range of Dataprep

You should whitelist the IP address range before you create connections to your source. The Dataprep by Trifacta Service's IP address range must be whitelisted in the relevant security groups. Dataprep of Trifacta Service has the IP range as follows:

34.68.114.64/28

Supported File Formats

This section of the article contains information about file formats for the input and output of Dataprep. It also has compression schemes that are supported for the same.

File Format

Native Input file format

These types of file formats can be read and directly imported in Dataprep:

  • CSV
  • JSON v1, including nested
  • Plain Text
  • LOG
  • Parquet
  • TSV
  • Avro
  • Google sheets (may not be available in all product addition)

File formats given below are not directly read into the product. It is first converted using the Conversion Service into the above file format and then used in the product.

  • Excel (XLS/XLSX)
  • Google sheets
  • PDF
  • JSON v2

Note: JSON v2 is a new version that reads source files through the Conversion service. It stores restructured data in tabular format. In contrast, JSON v1 reads JSON files directly as text files. It requires some extra work to restructure the data into a tabular format.

 

Native Output file format 

These types of file formats in which Dataprep can write:

  • JSON
  • CSV
  • Avro
  • BigQuery Table

Compressing Algorithms

When a file is imported, the Dataprep application tries to implement compression algorithms. It is based on the filename extension. 

Read Native file format

Read Native file format

Write Native file format

Write Native file format

Snappy compression formats

Dataprep supports the following variants of the compression format of Snappy:-

Snappy compression formats

Common Tasks in Trifacta

Common Tasks in Trifacta

Import

These are the tasks that refer to creating imported datasets. It is for use in the product. An imported dataset is not a copy of the data. It is the reference to a source of data. 

Discovery

This task involves using various techniques and tools to identify patterns in datasets. Inconsistencies, anomalies, and other issues are also identified in your datasets.

Validation

This task includes the detection of issues in your data. It also validates the data against target schemas or the source.

Structuring

These tasks explain various methods for changing the shape of your data. Some of these tasks are applied to data import. Other tasks can be managed in your recipe through a single transformation.

Cleanse

These tasks referred to the cleaning of data that has been imported into Dataprep by Trifacta.

Enrichment

These tasks cover various approaches to augment your data. It is achieved by fixing values and generating values or data from other datasets. It is done by:

  • Adding new column
  • Inserting metadata
  • Combining datasets
  • Reshaping datasets

Publishing

These tasks provide information about methods that will help you get your data. This data is present in the Dataprep of Trifacta. These tasks include imported recipes, datasets, generated results, and work-in-progress versions.

Project Management

These tasks guide better management of data. It tells you how to manage your data-wrangling efforts in Dataprep better.

Frequently Asked Questions

Does Google own Trifacta?

Trifacta is a privately owned software company. It is headquartered in San Francisco with offices in Bengaluru, Boston, Berlin, and London.

What is dataprep used for?

Dataprep by Trifacta is a data service. It is used for cleaning and visually exploring data. It is also used for preparing structured and unstructured data for analysis, machine learning, and reporting. Dataprep works at any scale and is serverless. There is no need for infrastructure to deploy or manage.

Who uses Trifacta?

Most users of Trifacta are from Mid-sized Companies and the Information Technology & Services industry.

From where can data be imported in dataprep?

You can import data from GCS, BigQuery, or your local computer.

Is DataPrep open source?

DataPrep is free and open-source software. It is released under the MIT license. 

Conclusion

In this article, we have discussed Dataprep by Trifacta. We have also explained how to start with Trifacta, how to set up Trifacta, common tasks in Trifacta, and more are also discussed in detail.
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