Databricks Data Intelligence Platform Features

Reports interface for standard and self-service reports is intuitive and easy to use.

Requires a minimal number of steps/clicks to answer business question.

Offers a variety of attractive graph and chart formats.

Score cards visually track KPI's.

Provides business users an interface to easily design, refine and collaborate on their dashboards

Using formulas based on existing data elements, users can create and calculate new field values.

Business users have the ability to filter data in a report based on predefined or automodeled parameters.

Users can drill down and explore data to discover new insights.

Ability to search global data set to find and discover data.

Ability for users to share data and reports they have built within the BI tool and outside the tool through other collaboration platforms.

Tool automatically suggests data types, schemas and hierarchies.

Analyze current and historical trends to make predictions about future events.

Communicate complex information clearly and effectively through advanced graphical techniques.

Ability to handle large, complex, and/or siloed data sets.

Converts data formats of source data into the format required for the reporting system without mistakes.

Ability to (re)structure data in a manner that allows extracting insights fast and accurate.

Provides business users an interface to easily design and refine their dashboards and reports. (What You See Is What You Get)

Application Programming Interface - Specification for how the application communicates with other software. API's typically enable integration of data, logic, objects, etc with other software applications.

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

Allows users to input models built in a variety of languages.

Allows users to choose the framework or workbench of their preference.

Records versioning as models are iterated upon.

Provides a way to quickly and efficiently deploy machine learning models.

Offers a way to scale the use of machine learning models across an enterprise.

Provides On-Premise deployment options.

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

Allows users to input models built in a variety of languages.

Allows users to choose the framework or workbench of their preference.

Records versioning as models are iterated upon.

Provides a way to quickly and efficiently deploy machine learning models.

Offers a way to scale the use of machine learning models across an enterprise.

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

Control model usage and performance in production

Deploy mission-critical ML applications where and when you need them

Easily compare experiments—code, hyperparameters, metrics, predictions, dependencies, system metrics, and more—to understand differences in model performance.

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.

Records and organizes all machine learning models that have been deployed across the business.

Tracks the performance and accuracy of machine learning models.

Provisions users based on authorization to both deploy and iterate upon machine learning models.

Allows users to manage model artifacts and tracks which models are deployed in production.

Stores the database metadata, that is the definitions of data elements, types, relationships etc.

Creates a copy of the database to maintain consistency and integrity.

Allows users to create, update and retrieve data in a database.

Defines the logical design of the data before building the schemas.

Monitors and analyzes critical database attributes like query performance, user sessions, dead lock detail, system errors etc and visualize them on a custom dashboard.

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.

Records and organizes all machine learning models that have been deployed across the business.

Tracks the performance and accuracy of machine learning models.

Provisions users based on authorization to both deploy and iterate upon machine learning models.

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

Ability to connect the analytics platform with a wide range of connector options for common data sources, including popular enterprise applications.

Connects to enterprise data governance software, or provides integrated data governance features to avoid misuse of data

Using formulas based on existing data elements, users can create and calculate new field values

Business users have the ability to filter data in a report based on predefined or automodeled parameters.

Allows the user to combine data from multiple sources into a functioning dataset.

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

Integrates data and data-related technologies into a single environment.

Provides metadata management capabilities.

Empowers the user via a self-service capability to manage data workflows.

Completely automates end-to-end data workflows across the data integration lifecycle.

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.

Allows data movement from one database to another.

Provides data backup and recovery functionality to protect and restore a database.

Allows users to access and work on data concurrently, supporting several views of the data.

Encrypts and transforms data at the database from a readable state into a ciphertext of unreadable characters.

Allows restricted user acess to modify depending on the access level.

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 a high performance, flexibile analytics platform to support data management and embrace data driven decision making.

Collect and displays metrics across the data integration via a dashboard.

Involved solely in monitoring data pipelines, sending alerts and troubleshooting data.

Deploys testing capabilities such as report testing, big data testing, cloud data migration testing, ETL and data warehouse testing.

Supports analytical platforms and data pipelines across complex hybrid environments.

Supports migration of component or pipeline to different cloud environments.

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.

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.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

Ensures that the service is reliable and available when needed, minimizing downtime and service interruptions.

Allows the user to scale the training of models efficiently, making it easier to deal with larger datasets and more complex models.

Provides the user the ability to get quick and low-latency responses during the inference stage, which is critical for real-time applications.

Offers the user a transparent pricing model for API calls, enabling better budget planning and cost control.

Provides the user the ability to allocate computational resources based on demand, making it cost-effective.

Allows the user to minimize energy usage during both training and inference, which is becoming increasingly important for sustainable operations.

Offers the user the flexibility to deploy across multiple cloud providers, reducing the risk of vendor lock-in.

Provides the user the ability to seamlessly connect with various data sources and pipelines, simplifying data ingestion and pre-processing.

Allows the user to easily integrate the generative AI models into existing workflows and systems via APIs.

Helps the user maintain compliance with GDPR and other data protection regulations, which is crucial for businesses operating globally.

Allows the user to set up access controls based on roles within the organization, enhancing security.

Ensures that data is encrypted during transit and at rest, providing an additional layer of security.

Provides the user with comprehensive and clear documentation, aiding in quicker adoption and troubleshooting.

Allows the user to gauge the level of community support and third-party extensions available, which can be useful for problem-solving and extending functionality.

Supports configuration using natural language instructions.

Allows users to customize the tone of agent.

Enables definition of clear security guardrails for agent actions.

Provides support across web, mobile, messaging apps, and other channels.

Allows customization of agent branding, including visual appearance and conversational style.

Equips agents with proactive response capabilities based on predefined triggers.

Facilitates seamless escalation to human employees for complex issues.

Provides analytics and reporting on agent performance and interactions.

Offers agents the ability to maintain contextual awareness across interactions.

Ensures compliance with data privacy and governance requirements.

Automates workflows and actions based on agent responses.

Allows the use of APIs for advanced agent configuration.

Enables interoperability with multiple platforms for unified experiences.

Allows integration with CRM data to ground agent responses in business context.

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

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

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

Enables non-technical users to build dashboards through intuitive, drag-and-drop interfaces

Enables automated report generation and scheduled delivery to stakeholders

Allows dashboards and analytics to be embedded into external apps with branding flexibility

Supports integration with major data sources like cloud data warehouses, SQL/NoSQL databases, and SaaS applications

Efficiently processes large datasets with minimal lag and ensures high performance under load

Maintains performance and uptime during high traffic from multiple users or teams

Supports semantic data layers, role-based access controls, and metadata governance

Integrates with Jupyter, Python, or R for custom analytics and modeling

Provides native tools for statistical analysis, forecasting, and trend prediction

Uses AI to generate textual summaries, key takeaways, and data stories from dashboards

Allows users to query data and build reports using conversational or plain language

Detects and notifies users about KPI anomalies or significant metric changes in real time

Recommends next questions, analyses, or exploration paths using autonomous AI agents

Learns from historical user interactions to improve and personalize query results over time

Tailors dashboard views and suggestions based on user roles, access levels, and past behavior

Supports AI-driven exploration via prompts or multi-turn conversations for iterative querying