About
Enjoy the highest performance and unlimited possibilities when working with SQL Server. SQL Server Data Access Components (SDAC) is a library of components that provides native connectivity to SQL Server from Delphi and C++Builder including Community Edition, as well as Lazarus (and Free Pascal) for Windows, Linux, macOS, iOS, and Android for both 32-bit and 64-bit platforms. SDAC-based applications connect to SQL Server directly through OLE DB, which is a native SQL Server interface. SDAC is designed to help programmers develop faster and cleaner SQL Server database applications. SDAC, a high-performance, and feature-rich SQL Server connectivity solution is a complete replacement for standard SQL Server connectivity solutions and presents an efficient native alternative to the Borland Database Engine (BDE) and standard dbExpress driver for access to SQL Server. SDAC-based DB applications are easy to deploy, and do not require the installation of other data provider layers.
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About
TRichView is a suite of native Delphi/C++Builder VCL/FireMonkey and Lazarus (Free Pascal) LCL components for displaying, editing, and printing complex rich text documents. TRichView can be used to develop advanced text editors, web/help/book authoring applications, chats and messengers, organizers and diaries, multimedia encyclopedias, and other applications that need a high-quality rich text editor or a hypertext user interface. Supported FireMonkey platforms, Windows, 64-bit macOS. The components support various character attributes (fonts, subscripts/superscripts, colored text background, custom drawn). Documents can contain tables, pictures, images from image lists, footnotes and endnotes, and any Delphi controls. Left, right, center, or justify paragraph alignment, custom margins and indents, multilevel bullets and numbering, background images, print preview, data-aware versions, and more.
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About
ThinkSQL is a powerful, cross-platform relational database management system designed for modern operating systems and hardware. It aims to comply fully with the ISO SQL standard to provide an open and flexible SQL server for your applications. Compliance with the SQL standard means it supports transactions, sub-selects, views, set operators (union, except, intersect), access privileges and comprehensive referential constraints to ensure the integrity of your data. ThinkSQL uses a multi-versioning engine to provide consistent views of data and concurrent access to rows without locking. The server also has stored procedures and functions (using the standard syntax), and has support for large multimedia objects. It's easy to install, runs on Windows ® and Linux ®, and has native ODBC, JDBC™, dbExpress™ and Python drivers so your applications can connect directly from any platform.
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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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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Audience
Programmers in need of a tool to develop faster and cleaner SQL Server database applications
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Audience
Anyone seeking a solution for displaying, editing and printing complex rich text documents
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Audience
Companies searching for a solution to manage and improve their database operations
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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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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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API
Offers API
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Screenshots and Videos |
Screenshots and Videos |
Screenshots and Videos |
Screenshots and Videos |
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Pricing
$199.95 per year
Free Version
Free Trial
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Pricing
€310 one-time payment
Free Version
Free Trial
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Pricing
No information available.
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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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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Training
Documentation
Webinars
Live Online
In Person
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Company InformationDevart
Founded: 1997
Czech Republic
www.devart.com/sdac/
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Company InformationTRichView
Russia
www.trichview.com/features/trichview.html
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Company InformationThinkSQL
United Kingdom
www.thinksql.co.uk
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Company Informationdbt Labs
Founded: 2016
United States
www.getdbt.com
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Alternatives |
Alternatives |
Alternatives |
Alternatives |
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Categories |
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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RDBMS Features
Backup
Data Migration
Monitoring
Performance Analysis
Queries
Storage Optimization
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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
Amazon Redshift
Blotout
C++Builder
Cake AI
Cargo
Dagster
Databricks Data Intelligence Platform
Google Cloud BigQuery
Kestra
Lightdash
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Integrations
Amazon Redshift
Blotout
C++Builder
Cake AI
Cargo
Dagster
Databricks Data Intelligence Platform
Google Cloud BigQuery
Kestra
Lightdash
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Integrations
Amazon Redshift
Blotout
C++Builder
Cake AI
Cargo
Dagster
Databricks Data Intelligence Platform
Google Cloud BigQuery
Kestra
Lightdash
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Integrations
Amazon Redshift
Blotout
C++Builder
Cake AI
Cargo
Dagster
Databricks Data Intelligence Platform
Google Cloud BigQuery
Kestra
Lightdash
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