dbt Features

As reported in 32 dbt reviews. Facilitates analysis of high-volume, real-time data.

As reported in 39 dbt reviews. Allows user to query data through query languages like SQL.

As reported in 27 dbt reviews. Aligns processing and distribution workflows on top of Apache Hadoop

As reported in 28 dbt reviews. Aligns processing and distribution workflows on top of Apache Spark

Integrates data from multiple external databases. 34 reviewers of dbt have provided feedback on this feature.

As reported in 31 dbt reviews. Facilitates the dissemination of collected big data throughout parallel computing clusters.

Strings together specific functions and datasets to automate analytics iterations. This feature was mentioned in 38 dbt reviews.

Isolates certain datasets and facilitates management of data access. 31 reviewers of dbt have provided feedback on this feature.

Based on 30 dbt reviews. 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 30 reviewers of dbt have provided feedback on this feature.

As reported in 48 dbt reviews. Provides a wide range of possible data connections, including cloud applications, on-premise databases, and big data distributions, among others

Allows businesses to easily connect to any data source 50 reviewers of dbt have provided feedback on this feature.

Offers API connections for cloud-based applications and data sources 41 reviewers of dbt have provided feedback on this feature.

As reported in 41 dbt reviews. Permits profiling of data sets for increased organization, both by users and machine learning

As reported in 44 dbt reviews. Indexes metadata descriptions for easier searching and enhanced insights

As reported in 50 dbt reviews. Tools to (re)structure data in a manner that enables quick and accurate insight extraction

Allows self-service joining of tables 48 reviewers of dbt have provided feedback on this feature.

As reported in 42 dbt reviews. Provides the ability to combine data sources into one data set

Based on 48 dbt reviews. Allows users and administrators to easily clean data to maintain quality and integrity

Based on 46 dbt reviews. Offers collaborative functionality for sharing queries and data sets

Ensures user access management, data lineage, and data encryption 45 reviewers of dbt have provided feedback on this feature.

As reported in 42 dbt reviews. Provides a wide range of possible integrations, including analytics, data integration, master data management, and data science tools

Allows businesses to easily integrate with analytics, data integration, master data management, and data science tools 44 reviewers of dbt have provided feedback on this feature.

As reported in 46 dbt reviews. Operationalizes data workflows to easily scale repeatable preparation needs

Record ETL historical data for auditing and potential data correction needs. This feature was mentioned in 36 dbt reviews.

Provide follow-up information after data cleanings through a visual dashboard or reports. 38 reviewers of dbt have provided feedback on this feature.

As reported in 42 dbt reviews. Automatically run data identification, correction, and normalization on data sources.

Schedule automated audits to identify data anomalies over time based on set business rules. This feature was mentioned in 37 dbt reviews.

Based on 39 dbt reviews. Gives a view of the entire data quality management ecosystem.

Allows user role-based access and actions to authorization for specific tasks. 42 reviewers of dbt have provided feedback on this feature.

Cleanse and re-format extracted data to the needed target format. This feature was mentioned in 41 dbt reviews.

Arrange ETL processes to occur automatically on needed time schedule (e.g., daily, weekly, monthly). 38 reviewers of dbt have provided feedback on this feature.

As reported in 36 dbt reviews. Capable of scaling processing power up or down based on ETL volume.

Based on 39 dbt reviews. Correctly identify inaccurate, incomplete, or duplicated data from a data source.

As reported in 36 dbt reviews. Utilize deletion, modification, appending, merging, or other methods to correct bad data.

Based on 39 dbt reviews. Standardize data formatting for uniformity and easier data usage.

Clean data as it enters the data source to prevent mixing bad data with cleaned data. This feature was mentioned in 38 dbt reviews.

Finds duplicates using the fuzzy logic technology or an advance search feature. This feature was mentioned in 37 dbt reviews.

As reported in 34 dbt reviews. Automatic creation and management of documents.

The software supports a wide variety of data sources and data warehouses that require automation. 33 reviewers of dbt have provided feedback on this feature.

As reported in 33 dbt reviews. Supports a template system, making it easier to set up configuration parameters.

Integrates data and data-related technologies into a single environment. 50 reviewers of dbt have provided feedback on this feature.

Based on 52 dbt reviews. Provides metadata management capabilities.

Empowers the user via a self-service capability to manage data workflows. 49 reviewers of dbt have provided feedback on this feature.

Based on 50 dbt reviews. Completely automates end-to-end data workflows across the data integration lifecycle.

Provides a high performance, flexibile analytics platform to support data management and embrace data driven decision making. This feature was mentioned in 47 dbt reviews.

As reported in 35 dbt reviews. Collect and displays metrics across the data integration via a dashboard.

Involved solely in monitoring data pipelines, sending alerts and troubleshooting data. This feature was mentioned in 43 dbt reviews.

Based on 50 dbt reviews. 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. This feature was mentioned in 38 dbt reviews.

Supports migration of component or pipeline to different cloud environments. 30 reviewers of dbt have provided feedback on this feature.

As reported in 36 dbt reviews. Software is able to alert the user in case of any errors.

As reported in 34 dbt reviews. Utilizes data to proactively identify IT issues.

Based on 34 dbt reviews. Creates new or streamlines existing data models to ensure business continuity.

Automates all parts of the Data Warehouse lifecycle - warehouse design, provision data sources. This feature was mentioned in 34 dbt reviews.

As reported in 32 dbt reviews. Supports real-time data ingestions and updates in the cloud or on-premises.

Software delivers insights into data in order to identify gaps, risk within the data warehouse. 35 reviewers of dbt have provided feedback on this feature.

Allows users to generate text based on a text prompt. This feature was mentioned in 27 dbt reviews.

Allows users to generate text based on a text prompt. 20 reviewers of dbt have provided feedback on this feature.

Condenses long documents or text into a brief summary. 20 reviewers of dbt have provided feedback on this feature.

Allows users to generate text based on a text prompt. This feature was mentioned in 13 dbt reviews.

Condenses long documents or text into a brief summary. This feature was mentioned in 13 dbt reviews.

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 10 reviewers of dbt have provided feedback on this feature.

Improves performance based on feedback and experience

Makes informed choices based on available data and objectives

Anticipates needs and offers suggestions without prompting

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