(4)
4.9 out of 5
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Google Cloud Dataprep
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Google Cloud Dataprep Features
What are the features of Google Cloud Dataprep?
Platform
- Data Preparation
Processing
- Cloud Processing
Google Cloud Dataprep Categories on G2
Filter for Features
Database
Real-Time Data Collection | Collects, stores, and organizes massive, unstructured data in real time | Not enough data | |
Data Distribution | Facilitates the disseminating of collected big data throughout parallel computing clusters | Not enough data | |
Data Lake | Creates a repository to collect and store raw data from sensors, devices, machines, files, etc. | Not enough data |
Integrations
Hadoop Integration | Aligns processing and distribution workflows on top of Apache Hadoop | Not enough data | |
Spark Integration | Aligns processing and distribution workflows on top of Apache Hadoop | Not enough data |
Platform
Machine Scaling | Facilitates solution to run on and scale to a large number of machines and systems | Not enough data | |
Data Preparation | Curates collected data for big data analytics solutions to analyze, manipulate, and model This feature was mentioned in 10 Google Cloud Dataprep reviews. | 92% (Based on 10 reviews) | |
Spark Integration | Aligns processing and distribution workflows on top of Apache Hadoop | Not enough data |
Processing
Cloud Processing | Moves big data collection and processing to the cloud 10 reviewers of Google Cloud Dataprep have provided feedback on this feature. | 87% (Based on 10 reviews) | |
Workload Processing | Processes batch, real-time, and streaming data workloads in singular, multi-tenant, or cloud systems | Not enough data |
Data Source Access
Breadth of Data Sources | Provides a wide range of possible data connections, including cloud applications, on-premise databases, and big data distributions, among others | Not enough data | |
Ease of Data Connectivity | Allows businesses to easily connect to any data source | Not enough data | |
API Connectivity | Offers API connections for cloud-based applications and data sources | Not enough data |
Data Interaction
Profiling and Classification | Permits profiling of data sets for increased organization, both by users and machine learning | Not enough data | |
Metadata Management | Indexes metadata descriptions for easier searching and enhanced insights | Not enough data | |
Data Modeling | Tools to (re)structure data in a manner that enables quick and accurate insight extraction | Not enough data | |
Data Joining | Allows self-service joining of tables | Not enough data | |
Data Blending | Provides the ability to combine data sources into one data set | Not enough data | |
Data Quality and Cleansing | Allows users and administrators to easily clean data to maintain quality and integrity | Not enough data | |
Data Sharing | Offers collaborative functionality for sharing queries and data sets | Not enough data | |
Data Governance | Ensures user access management, data lineage, and data encryption | Not enough data |
Data Exporting
Breadth of Integrations | Provides a wide range of possible integrations, including analytics, data integration, master data management, and data science tools | Not enough data | |
Ease of Integrations | Allows businesses to easily integrate with analytics, data integration, master data management, and data science tools | Not enough data | |
Data Workflows | Operationalizes data workflows to easily scale repeatable preparation needs | Not enough data |
Generative AI
AI Text Generation | Allows users to generate text based on a text prompt. | Not enough data |
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