IBM watsonx.data Features

Supports programming languages such as Java, C, or Python. Supports front-end languages such as HTML, CSS, and JavaScript

Offers the ability for developers to drag and drop pieces of code or algorithms when building models

Provides users with pre-built algorithms for simpler model development

Supplies large data sets for training individual models

Transforms raw data into features that better represent the underlying problem to the predictive models

Offers image recognition services

Offers natural language processing services

Offers natural language generation services

Offers artificial neural networks for users

Offers natural language understanding services

Provides deep learning capabilities

Manages the intelligent application for the user, reducing the need of infrastructure

Allows users to insert machine learning into operating applications

Provides easily scaled machine learning applications and infrastructure

Provides On-Premise deployment options.

Provides Cloud deployment options (private or public cloud, hybrid cloud).

Collects, stores, and organizes massive, unstructured data in real time

Facilitates the disseminating of collected big data throughout parallel computing clusters

Creates a repository to collect and store raw data from sensors, devices, machines, files, etc.

Aligns processing and distribution workflows on top of Apache Hadoop

Aligns processing and distribution workflows on top of Apache Hadoop

Facilitates solution to run on and scale to a large number of machines and systems

Curates collected data for big data analytics solutions to analyze, manipulate, and model

Aligns processing and distribution workflows on top of Apache Hadoop

Moves big data collection and processing to the cloud

Processes batch, real-time, and streaming data workloads in singular, multi-tenant, or cloud systems

Facilitates analysis of high-volume, real-time data.

Allows user to query data through query languages like SQL.

Aligns processing and distribution workflows on top of Apache Hadoop

Aligns processing and distribution workflows on top of Apache Spark

Integrates data from multiple external databases.

Facilitates the dissemination of collected big data throughout parallel computing clusters.

Processes data and represents interpretations in a variety of graphic formats.

Strings together specific functions and datasets to automate analytics iterations.

Isolates certain datasets and facilitates management of data access.

Allows big data tool to run and record data within external applications.

Use notebooks for tasks such as creating dashboards with predefined, scheduled queries and visualizations

Tools to (re)structure data in a manner that allows extracting insights quickly and accurately

Analyzes data to find and recommend the highest value customer segmentations.

Tools to create and adjust workflows to ensure consistency.

Presents information and analytics in a digestible, intuitive, and visually appealing way.

Supports compliance with PII, GDPR, HIPPA, PCI, and other regulatory standards.

Provides guidelines or training related to sensitive data compliance requirements,

Allows administrators to set policies for security and data governance

Monitors data quality and send alerts based on violations or misuse

Curates collected data for big data analytics solutions to analyze, manipulate, and model

Facilitates the disseminating of collected big data throughout parallel computing clusters

Compile data from across all systems so that users can view relevant information easily.

View ETL process data via reports and visualizations like charts and graphs.

Record ETL historical data for auditing and potential data correction needs.

Lets users build a glossary of business terms, vocabulary and definitions across multiple tools.

Provides a built-in integrated data catalog that allows users to easily locate data across multiple sources.

Monitors and cleanses data with the help of business rules and analytical algorithms.

Visualize data flows and lineage that demonstrates compliance with reports and dashboards through a single console.

Provides an automated data lineage functionality which provides visibility over the entire data movement journey from data origination to destination.

Extract data from the designated source(s) like relational databases, JSON files, and XML files.

Cleanse and re-format extracted data to the needed target format.

Load reformatted data into target database, data warehouse, or other storage location.

Arrange ETL processes to occur automatically on needed time schedule (e.g., daily, weekly, monthly).

Capable of scaling processing power up or down based on ETL volume.

Gives user ability to import a variety of data sources for immediate use

Consolidates, Cleanses and Normalizes data from multiple disparate sources.

Helps save storage capacity and improves query performance.

Eliminates data inconsistency and duplications ensuring data integrity.

SQL based analytics functions like Time series, pattern matching, geospatial analytics etc.

Provides built in capabilities like machine learning algorithms, data preparation functions, model evaluation and management etc.

Allows data querying across data formats like parquet, ORC, JSON etc and analyze complex data types on HDFS

Provides movement of data from one location to another.

Provides an overall strategy for data governance.

Aids in providing secured storage solutions for data extracted.

Integrates with data science workflows, Machine Learning and artificial intelligence (AI) capabilities.

Integrates with BI Tools to transform data into Actionable Insights.

Provides speed in data processing and capturing unstructured, semi-structured and streaming data.

Manages huge volumes of data, upscale or downscale as per demand.

Policies, procedures and standards to manage and access data.

Restricts data access at a cell level, mask or hide parts of cells, and encrypt data at rest and in motion

Authenticates and authorizes individuals to access the data they are allowed to see and use.

Helps identify and manage the roles of owners and stewards of data.

Helps adhere to data privacy regulations and norms.

Defines, validates, and monitors business rules to safeguard master data readiness.

Allows users to create and review data policies to make them consistent across the organization.

Provides specialization in data-driven insights by direct access to data analysts or end users.

Provides data in structured and readable formats.

Aids in establishing a data fabric with a network of various tools to operationalize data.

Provides users with options of architecture such as centralized or decentralized.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

Provides the ability to generate images from a text prompt.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

Capability to perform complex tasks without constant human input

Ability to break down and plan multi-step processes

Works across multiple software systems or databases

Improves performance based on feedback and experience

Engages in human-like conversation for task delegation

Makes informed choices based on available data and objectives

Capability to perform complex tasks without constant human input

Ability to break down and plan multi-step processes

Works across multiple software systems or databases

Improves performance based on feedback and experience

Engages in human-like conversation for task delegation

Anticipates needs and offers suggestions without prompting

Makes informed choices based on available data and objectives