Dremio Features

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

Allows user to query data through query languages like SQL.

Aligns processing and distribution workflows on top of Apache Hadoop

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

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.

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.

Integrates with BI Tools to transform data into Actionable Insights.

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

Provides On-Premise deployment options.

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

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

Policies, procedures and standards to manage and access data.

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.

Supports deployment across multiple environments or cloud platforms with consistent configuration management

Provides APIs and SDKs for seamless integration with orchestration, governance, and custom data tools

Allows querying and joining data across multiple warehouses and lakes without requiring data replication

Automatically adapts to schema or metadata changes in connected data sources while maintaining consistency

Enables users to create derived or calculated metrics based on governed data definitions

Provides built-in support for time-based calculations such as YoY, MoM, and rolling averages

Enhances performance by using intelligent caching, precomputation, and acceleration techniques

Automatically tunes and optimizes queries based on data size, frequency, and usage patterns

Provides visibility and control over how AI systems or automated agents interact with the semantic layer

Tracks how governed metrics and datasets are used in AI/ML training to ensure transparency, compliance, and clear data traceability

Provides version control for semantic models and metrics, with change tracking, rollback, and impact analysis to see how updates affect downstream data or reports

Allows users or AI assistants to explore and query metrics through natural language prompts

Enables AI and machine learning systems to directly consume standardized, governed metrics and logic

Suggests relevant metrics, joins, or insights based on context and historical usage patterns

Enables autonomous AI agents to compose, execute, and refine analytical queries via the semantic layer

Provides semantic context graphs that help AI agents understand data relationships and business logic

Allows semantic agents to trigger automated actions such as data refreshes, alerts, or report generation