Tool Information
Watsonx.data is a fit-for-purpose data store optimized for governed data and AI workloads, designed to help enterprises scale their analytics and AI capabilities. With watsonx.data, users can quickly connect to data sources, get trusted insights and lower data warehouse costs. The tool is optimized for all data, analytics and AI workloads and features an open, hybrid and governed data store that allows users to access and share data. Watsonx.data includes a shared metadata layer to access all data through a single point of entry, and built-in governance, security and automation to enhance trust in data. The tool enables users to reduce the cost of their data warehouse by up to 50% by optimizing costly data warehouse workloads across multiple query engines and storage tiers.Watsonx.data supports a range of fit-for-purpose query engines, including Presto, Spark, Db2 and Netezza, which dynamically scale up and down to drive analytics costs down. Users can store vast amounts of data in vendor-agnostic open formats, such as Parquet, Avro and Apache ORC, and share a single copy of data across multiple query engines using Apache Iceberg table format and shared metadata. The tool includes semantic automation to help users discover, augment, refine and visualize watsonx.data and metadata through the power of watsonx.ai models. Watsonx.data enables enterprises to build, train, tune, deploy and monitor trusted AI models for mission-critical workloads with data in the lakehouse and ensure compliance with lineage and reproducibility of data used for AI. Watsonx.data helps streamline data engineering, reduce data pipelines, simplify data transformation and enrich data for consumption using SQL, Python or an AI-infused conversational interface. Finally, Watsonx.data enables enterprises to support self-service access for more users to more data while enabling security and compliance with centralized governance and local automated policy enforcement.
F.A.Q (20)
WatsonX.data is a fit-for-purpose data store by IBM, optimized for governed data and AI workloads. It is designed to help enterprises scale their analytics and AI capabilities, offering quick connection to data sources, trusted insights, and reduced data warehouse costs.
WatsonX.data offers several features including an open, hybrid, and governed data store that allows users to access and share data. It also includes a shared metadata layer, built-in governance, security and automation, query engine support for Presto, Spark, Db2, and Netezza, storage for vast amount of data in open formats, and semantic automation to refine and visualize data and metadata. It also helps in reducing data warehouse costs and supporting data-driven AI model training.
The shared metadata layer in WatsonX.data provides a single point of entry to access all data. It is built across clouds and on-premises environments, making it easily accessible regardless of the origin of data, thus expedite data discovery and usage.
WatsonX.data helps reduce data warehouse costs by up to 50%. It optimizes costly data warehouse workloads across multiple query engines and storage tiers, strategically aligning the right workload with the right engine. This optimization lowers the costs associated with maintaining and running these workloads.
WatsonX.data supports a variety of fit-for-purpose query engines such as Presto, Spark, Db2, and Netezza. These engines dynamically scale up and down to make analytics more cost-efficient and to meet real-time processing needs.
WatsonX.data allows data to be stored in vendor-agnostic open formats. These include formats like Parquet, Avro, and Apache ORC. Additionally, it leverages Apache Iceberg table format and shared metadata to share a single copy of data across multiple query engines.
Semantic automation in WatsonX.data helps users discover, augment, refine, and visualize data and metadata. It leverages the models of watsonx.ai to automate the process of understanding the meaning and context of data, thereby reducing manual interpretation efforts and enhancing data accuracy.
WatsonX.data enhances trust in data with its in-built governance, security, and automation features. It provides a shared metadata layer across clouds and on-premises environments and offers automated policy enforcement to ensure data privacy and compliance.
Yes, WatsonX.data can be used to build, train, tune, deploy, and monitor AI models. This includes mission-critical workloads with data in the lakehouse. It also ensures compliance with data lineage and reproducibility requirements for AI model development.
WatsonX.data offers detailed lineage and reproducibility compliance features. It incorporates automated policy enforcement to ensure data follows local laws and regulations. This built-in compliance component bolsters data integrity and trust, while aligning with business and regulatory compliance requirements.
WatsonX.data streamlines data engineering by reducing data pipelines, simplifying data transformation, and enriching data for consumption using SQL, Python, or AI-infused conversational interface. This helps businesses manage their data processes more efficiently and effectively.
WatsonX.data promotes self-service access by offering an open, hybrid, and governed data store that enables more users to access more data. It pairs this with centralized governance and local automated policy enforcement to maintain the balance between data accessibility and security.
WatsonX.data has security measures in place in the form of built-in governance, security, and automation. This ensures trusted data access and exchange and includes centralized governance and local automated policy enforcement, helping to secure the data while maintaining compliance with regulations.
Yes, WatsonX.data can connect with existing data analytics tools to unlock new insights without the cost and complexity of duplicating and moving data. It can integrate with IBM Cognos and other third-party business intelligence and dashboarding tools for efficient data visualization and analytics.
WatsonX.data supports data transformation using SQL and Python languages. It also includes an AI-infused conversational interface to simplify and enrich the data transformation process.
WatsonX.data enables scalable analytics and AI by providing an optimized data store for governed data and AI workloads. It quickly connects to data sources, offers trusted insights, and reduces data warehouse costs. In addition, it supports a range of query engines that dynamically scale and allows vast amounts of data to be stored in open formats.
WatsonX.data supports comprehensive data management capabilities including storage of vast amounts of data in vendor-agnostic open formats, sharing a single copy of data across multiple query engines, built-in governance, security and automation features, and centralized governance with local automated policy enforcement. It can connect to a range of data sources in minutes to provide trusted insights.
WatsonX.data can quickly connect to data sources within minutes. This includes storage and analytics environments across hybrid-cloud and on-premises setups. The connection process is designed to be quick and straightforward, enabling users to start deriving insights from their data as soon as possible.
WatsonX.data supports AI and machine learning at scale by providing a suitable environment to build, train, tune, deploy and monitor AI models for mission-critical workloads. It ensures compliance with lineage and reproducibility of data used for AI, enabling users to create trusted AI models at scale.
Enterprises can use WatsonX.data for business intelligence by connecting existing data with new data in minutes and unlocking new insights without the cost and complexity of duplicating and moving data. Integration with IBM Cognos and other third-party business intelligence and dashboarding tools enables data visualization and allows enterprises to access significant business insights in real-time.
Pros and Cons
Pros
- Optimized for all workloads
- Shared metadata layer
- Open
- hybrid
- governed data store
- Reduces data warehouse costs
- Supports multiple query engines
- Stores data in open formats
- Single copy of data shared
- Semantic automation included
- Compliance with lineage and reproducibility
- Streamlined data engineering
- Simplifies data transformation
- Enriches data for consumption
- Self-service access enabled
- Centralized governance and automated policy enforcement
Cons
- Lack of real-time analysis
- Vendor-agnostic formats only
- Limited query engines compatibility
- Complicated transformation procedure
- Compliance with only Watsonx.ai models
- Limited support
- No dedicated mobile application
- Usage can be complex for beginners
- Costly for small businesses
- No inbuilt visualization tool
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