Data analysis 2023-05-06 by IBM icon by IBM

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Scaled analytics and data management for enterprises.
Generated by ChatGPT 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, 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. 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 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 and metadata through the power of models. 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. 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, 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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Pros and Cons


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


Lack of real-time analysis
Vendor-agnostic formats only
Limited query engines compatibility
Complicated transformation procedure
Compliance with only models
Limited support
No dedicated mobile application
Usage can be complex for beginners
Costly for small businesses
No inbuilt visualization tool


What is
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What is the purpose of the shared metadata layer in
How can help reduce data warehouse costs?
What types of query engines does support?
In what formats can data be stored in
What is semantic automation in the context of
How does enhance data trust?
Can be used to train AI models?
What are the compliance features offered by
How does streamline data engineering?
How does promote self-service access to data?
What security measures are in place for
Can integrate with existing data analytics tools?
What languages can be used for data transformation in
How does enable scalable analytics and AI?
What kind of data management capabilities does support?
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How does support AI and machine learning at scale?
How can enterprises use for business intelligence?

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