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Exploring, cleaning and preparing structured and unstructured data for analysis or training machine-learning models. Provide consistency in big datasets.
One of the biggest challenges companies are facing is setting up and maintaining a reliable ETL process (extract, transform, and load) to extract value and insight from data. Traditional ETL tools are complicated to use, and it can take months to implement, test and deploy. After the ETL jobs are built, maintaining them can be painful because data formats and schemas change frequently and new data sources need to be added all the time.
Google Cloud Dataprep visually explores, cleans and prepares structured and unstructured data for analysis or training machine-learning models. Key Cloud Dataprep features include a visual experience that makes data preparation intuitive and approachable for analysts who want to modify or enrich their datasets directly without needing to rely on data engineers. And similarly to other services in the Google Cloud Platform, data analytics, machine learning stack and Cloud Dataprep run on serverless infrastructure that handles scalability, performance, availability and security.