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.

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WatsonX.data by IBM was manually vetted by our editorial team and was first featured on May 11th 2023.

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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

Q&A

What is WatsonX.data?

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.

What are the main features of WatsonX.data?

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.

What is the purpose of the shared metadata layer in WatsonX.data?

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.

How can WatsonX.data help reduce data warehouse costs?

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.

What types of query engines does WatsonX.data support?

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.

In what formats can data be stored in WatsonX.data?

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.

What is semantic automation in the context of WatsonX.data?

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.

How does WatsonX.data enhance data trust?

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.

Can WatsonX.data be used to train AI models?

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.

What are the compliance features offered by WatsonX.data?

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.

How does WatsonX.data streamline data engineering?

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.

How does WatsonX.data promote self-service access to data?

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.

What security measures are in place for WatsonX.data?

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.

Can WatsonX.data integrate with existing data analytics tools?

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.

What languages can be used for data transformation in WatsonX.data?

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.

How does WatsonX.data enable scalable analytics and AI?

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.

What kind of data management capabilities does WatsonX.data support?

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.

How does WatsonX.data connect to data sources?

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.

How does WatsonX.data support AI and machine learning at scale?

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.

How can enterprises use WatsonX.data for business intelligence?

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.