About
Aqua Data Studio is a multiple-platform, integrated development environment (IDE) for data. It provides benefits to a variety of data-centric roles, allowing them to manage a wide range of data sources.
Aqua Data Studio provides scalable, cross-platform data management, supporting IT and data-centric specialists, including developers, database administrators, as well as data analysts, data modelers, and data architects.
Simplifies tedious tasks involving SQL queries, data, result sets, schema, data models, files, instances, servers, as well as automation.
Aqua Data Studio can be installed on the three popular operating systems: Microsoft Windows, Apple macOS, and Linux.
The graphical user interface can display the nine of the most widely spoken languages: English, Spanish, French, German, Korean, Portuguese, Japanese, and Chinese.
Aqua Data Studio supports over 40 of the most popular data source platforms, including relational, NoSQL, as well as managed cloud data source
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About
SQL is a domain-specific programming language used for accessing, managing, and manipulating relational databases and relational database management systems.
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About
dbt helps data teams transform raw data into trusted, analysis-ready datasets faster. With dbt, data analysts and data engineers can collaborate on version-controlled SQL models, enforce testing and documentation standards, lean on detailed metadata to troubleshoot and optimize pipelines, and deploy transformations reliably at scale. Built on modern software engineering best practices, dbt brings transparency and governance to every step of the data transformation workflow.
Thousands of companies, from startups to Fortune 500 enterprises, rely on dbt to improve data quality and trust as well as drive efficiencies and reduce costs as they deliver AI-ready data across their organization. Whether you’re scaling data operations or just getting started, dbt empowers your team to move from raw data to actionable analytics with confidence.
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Audience
database developers, database administrators, data scientists, data modelers, DevOps
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Audience
Developers and database admins
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Audience
SQL users looking for a ETL solution to engineer data transformations
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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API
Offers API
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API
Offers API
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API
Offers API
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Screenshots and Videos |
Screenshots and Videos |
Screenshots and Videos |
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Pricing
$499 per user per year
Free Version
Free Trial
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Pricing
Free
Free Version
Free Trial
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Pricing
$100 per user/ month
Free Version
Free Trial
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Reviews/
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Reviews/
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Reviews/
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Company InformationAquaFold, an Idera, Inc. company
Founded: 2001
United States
www.aquafold.com/aquadatastudio
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Company InformationSQL
Founded: 1974
sourceforge.net/software/product/SQL/
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Company Informationdbt Labs
Founded: 2016
United States
www.getdbt.com
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Alternatives |
Alternatives |
Alternatives |
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Categories |
Categories |
Categoriesdbt powers the transformation layer of modern data pipelines. Once data has been ingested into a warehouse or lakehouse, dbt enables teams to clean, model, and document it so it’s ready for analytics and AI. With dbt, teams can: - Transform raw data at scale with SQL and Jinja. - Orchestrate pipelines with built-in dependency management and scheduling. - Ensure trust with automated testing and continuous integration. - Visualize lineage across models and columns for faster impact analysis. By embedding software engineering practices into pipeline development, dbt helps data teams build reliable, production-grade pipelines to accelerate time to insight, and deliver AI-ready data. dbt brings rigor and scalability to data preparation by enabling teams to clean, transform, and structure raw data directly in the warehouse. Instead of siloed spreadsheets or manual workflows, dbt uses SQL and software engineering best practices to make data preparation reliable, repeatable, and collaborative. With dbt, teams can: - Clean and standardize data with reusable, version-controlled models. - Apply business logic consistently across all datasets. - Validate outputs through automated tests before data is exposed to analysts. - Document and share context so every prepared dataset comes with lineage and definitions. By treating data preparation as code, dbt ensures that prepared datasets aren’t just quick fixes — they’re trusted, governed, and production-ready assets that scale with the business. dbt modernizes the “T” in ETL: Transformation. Instead of relying on legacy pipelines or black-box transformations, dbt empowers data teams to build, test, and document transformations directly inside the data warehouse or lakehouse. With dbt, teams can: - Transform raw data into analytics-ready models using SQL and Jinja. - Ensure reliability with built-in testing, version control, and CI/CD. - Standardize workflows across teams with reusable models and shared documentation. - Leverage modern platforms like Snowflake, Databricks, BigQuery, and Redshift for scalable transformation. By focusing on the transformation layer, dbt helps organizations shorten pipeline development cycles, reduce data debt, and deliver trusted insights faster — complementing ingestion and loading tools in a modern ELT stack. |
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Data Analysis Features
Data Discovery
Data Visualization
High Volume Processing
Predictive Analytics
Regression Analysis
Sentiment Analysis
Statistical Modeling
Text Analytics
Data Visualization Features
Analytics
Content Management
Dashboard Creation
Filtered Views
OLAP
Relational Display
Simulation Models
Visual Discovery
Database Features
Backup and Recovery
Creation / Development
Data Migration
Data Replication
Data Search
Data Security
Database Conversion
Mobile Access
Monitoring
NOSQL
Performance Analysis
Queries
Relational Interface
Virtualization
IDE Features
Code Completion
Compiler
Cross Platform Support
Debugger
Drag and Drop UI
Integrations and Plugins
Multi Language Support
Project Management
Text Editor / Code Editor
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Big Data Features
Collaboration
Data Blends
Data Cleansing
Data Mining
Data Visualization
Data Warehousing
High Volume Processing
No-Code Sandbox
Predictive Analytics
Templates
Data Lineage Features
Database Change Impact Analysis
Filter Lineage Links
Implicit Connection Discovery
Lineage Object Filtering
Object Lineage Tracing
Point-in-Time Visibility
User/Client/Target Connection Visibility
Visual & Text Lineage View
Data Preparation Features
Collaboration Tools
Data Access
Data Blending
Data Cleansing
Data Governance
Data Mashup
Data Modeling
Data Transformation
Machine Learning
Visual User Interface
ETL Features
Data Analysis
Data Filtering
Data Quality Control
Job Scheduling
Match & Merge
Metadata Management
Non-Relational Transformations
Version Control
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Integrations
Snowflake
100x
Accops HyWorks
Acryl Data
Aleph
Amazon Q Business
ChatGPT
ChatGPT Pro
DBmaestro
Datakin
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Integrations
Snowflake
100x
Accops HyWorks
Acryl Data
Aleph
Amazon Q Business
ChatGPT
ChatGPT Pro
DBmaestro
Datakin
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Integrations
Snowflake
100x
Accops HyWorks
Acryl Data
Aleph
Amazon Q Business
ChatGPT
ChatGPT Pro
DBmaestro
Datakin
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