Cloud Dataprep by Trifacta

An intelligent cloud data service to visually explore, clean, and prepare data for analysis and machine learning.

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Intelligent data preparation

Intelligent data preparation

Cloud Dataprep by Trifacta is an intelligent data service for visually exploring, cleaning, and preparing structured and unstructured data for analysis, reporting, and machine learning. Because Cloud Dataprep is serverless and works at any scale, there is no infrastructure to deploy or manage. Your next ideal data transformation is suggested and predicted with each UI input, so you don’t have to write code. With automatic schema, datatype, possible joins, and anomaly detection, you can skip time-consuming data profiling and focus on data analysis.

Serverless simplicity

Cloud Dataprep is an integrated partner service operated by Trifacta and based on their industry-leading data preparation solution, Trifacta Wrangler. Google works closely with Trifacta to provide a seamless user experience that removes the need for up-front software installation, separate licensing costs, or ongoing operational overhead. Cloud Dataprep is fully managed and scales on demand to meet your growing data preparation needs so you can stay focused on analysis.

Fast exploration and anomaly detection

Understand and explore data instantly with visual data distributions. Cloud Dataprep automatically detects schemas, data types, possible joins, and anomalies such as missing values, outliers, and duplicates so you get to skip the time-consuming work of profiling your data and go right to the exploration and analysis.

Easy and powerful data preparation

With each gesture in the UI, Cloud Dataprep automatically suggests and predicts your next ideal data transformation. Once you’ve defined your sequence of transformations, Cloud Dataprep uses Cloud Dataflow under the hood, enabling you to process structured or unstructured datasets of any size with the ease of clicks, not code.

Features

Predictive transformation

Cloud Dataprep uses a proprietary inference algorithm to interpret the data transformation intent of a user’s data selection. A ranked set of suggestions and patterns for the selections to match are automatically generated.

Parameterization

Execute a recipe across multiple instances of identical datasets by parameterizing a variable to replace the parts of the file path that change with each refresh. This variable can be modified as needed at job runtime.

Collaboration

In team environments, it can be helpful to be able to have multiple users work on the same assets or to create copies of good quality work to serve as templates for others. Cloud Dataprep enables users to collaborate on the same flow objects in real time or to create copies for others to use for independent work.

Pattern matching

Utilize columnar pattern matching to identify data patterns of interest to you and to surface them in the interface for use in building your recipes. Additionally, in your recipe steps, you can apply regular expressions or Cloud Dataprep patterns to locate patterns and transform the matching data in your datasets.

Visual profiling

See and explore your data through interactive visual distributions of your data to assist in discovery, cleansing, and transformation. Visual representations help interpret large volumes of data, and Cloud Dataprep’s innovative profiling techniques visualize key statistical information in a dynamic, easy-to-consume format.

Sampling

For performance optimization, Cloud Dataprep automatically generates one or more samples of the data for display and manipulation in the client application. However, you can easily change the size of samples, the scope of the sample, and the method by which the sample is created.

Scheduling

Schedule the execution of recipes in your flows on a recurring or as-needed basis. When the scheduled job successfully executes, you can collect the wrangled output in the specified output location, where it is available in the published form you specify.

Target matching

Define target schemas, through imported or created datasets, and assign to an existing recipe to systematize and speed up your wrangling efforts. Targets appear in the Transformer page and can be applied against the entire dataset or selected columns of the dataset you need to wrangle.

Common data types

Transform structured or unstructured datasets, stored in CSV, JSON, or relational table formats, of any size — megabytes to petabytes — with equal ease and simplicity.

Integrated with Google Cloud Platform

Process data stored in Cloud Storage, BigQuery, or from your desktop, then export refined data to BigQuery or Cloud Storage for storage, analysis, visualization, or machine learning. User access and data security is seamlessly managed with Cloud Identity and Access Management.

Cloud Dataprep architecture

Cloud Dataprep Architecture

Cloud Dataprep allows us to quickly explore new datasets and its flexibility supports all our data transformation needs. Data preparation work at Merkle is now completed in minutes, not hours or days, accelerating our data preparation time by 90%.

Henry Culver, IT Architect, Merkle

Our customers

Resources

Pricing

Cloud Dataprep is an interactive web application in which users define the data preparation rules by interacting with a sample of their data. Use of the application for sample data exploration, defining transformation steps, and exporting the transformed sample incurs no charge. For execution of the flow over the complete dataset, the flow can be executed as a Cloud Dataprep job (using Google Cloud Dataflow). Learn more and view complete details in our pricing guide.

Google Cloud

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Cloud Dataprep by Trifacta