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.
Leverage hundreds of transformation functions to turn your data into the asset you want. With a click of a mouse, apply aggregation, pivot, unpivot, joins, union, extraction, calculation, comparison, condition, merge, regular expressions, and more.
Optimized processing throughput
Dataprep automatically selects the best underlying Google Cloud processing engine to transform the data as fast as possible. Based on the data locality and volume, Dataprep leverages BigQuery (in-place ELT transforms) to prepare the data, Dataflow, or for small volumes Dataprep's in-memory engine.
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 Dataprep’s innovative profiling techniques visualize key statistical information in a dynamic, easy-to-consume format.
Data quality rules
Data quality rules suggest data quality indicators to monitor and remediate the accuracy, completeness, consistency, validity, and uniqueness of the data, ensuring that you have a comprehensive view of the cleanliness of your data.
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. Dataprep enables users to collaborate on the same flow objects in real time or to create copies for others to use for independent work.
In addition to BigQuery, Cloud Storage, Microsoft Excel, and Google Sheets standard connectivity, enrich your self-service analytics with hundreds of data sources such as Salesforce, Oracle, Microsoft SQL Server, MySQL, PostgreSQL, and many more.
Data pipeline orchestration
Schedule and automate your data preparation jobs by chaining them together in sequential and conditional order. Alert users of success or failure, and trigger external tasks (such as Cloud Functions). Leverage comprehensive APIs to integrate Dataprep as part of an enterprise’s end-to-end solution.
Adopt a continuous deployment practice with recipe import/export across editions and versions, flow parameters, custom configuration for Dataflow or BigQuery, performance tuning, and advanced APIs to automate software development life cycles and monitoring.
Common data types
Transform structured or unstructured datasets stored in CSV, JSON, relational table formats, or SaaS application data of any size—megabytes to petabytes—with equal ease and simplicity.
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 Dataprep patterns to locate patterns and transform the matching data in your datasets.
Group values by similarities based on spelling or language-independent pronunciation and create standardized clusters of consistent values.
For performance optimization, 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.
Expand on current security standards by providing individual data access control using a combination of Google IAM roles and BigQuery, Cloud Storage, and Google Sheets access rights to determine access.
"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, MerkleRead story
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