Top 10 PostgreSQL Data Access Components Alternatives & Competitors

Top 10 Alternatives to PostgreSQL Data Access Components Recently Reviewed By G2 Community

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Syncfusion Essential Studio®
By Syncfusion®
4.5/5
(663)
Product Description
Syncfusion® Essential Studio is a suite of components and frameworks for developing web, mobile, and desktop applications.
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Categories in common with PostgreSQL Data Access Components:
Component Libraries

Progress Kendo UI
By Progress Software
4.4/5
(252)
Product Description
The Most Complete UI Framework to Speed Up Your HTML/JS Development
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Categories in common with PostgreSQL Data Access Components:
Component Libraries

Progress Telerik
By Progress Software
4.5/5
(200)
Product Description
Progress Telerik provides controls and reporting for .NET Web (Blazor, ASP.NET Core, ASP.NET MVC, ASP.NET AJAX), JavaScript Web (Angular, React, Vue, jQuery), .NET Desktop (WPF, WinForms, WinUI, UWP), .NET Mobile (Xamarin, MAUI), Embedded Reporting, Automated Testing and Mocking tools.
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Categories in common with PostgreSQL Data Access Components:
Component Libraries
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DevExpress
By DevExpress
4.8/5
(115)
Product Description
DevExpress engineers feature-complete Presentation Controls, IDE Productivity Tools, Business Application Frameworks, and Reporting / Dashboard Systems for Visual Studio, Delphi, HTML5 or iOS & Android development. Our product line includes UI components and libraries for desktop and mobile development (WinForms / WPF / WinUI / MAUI / Xamarin / VCL), the web (ASP.NET Core / Blazor / Web Forms / MVC / JavaScript: Angular, React, Vue, jQuery), Office File API, .NET Application Frameworks, ORM Tools, and End-to-End Web Testing.
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Categories in common with PostgreSQL Data Access Components:
Component Libraries

pandas python
By pandas python
4.6/5
(96)
Product Description
Pandas is a powerful and flexible open-source Python library designed for data analysis and manipulation. It provides fast, efficient, and intuitive data structures, such as DataFrame and Series, which simplify handling structured (tabular, multidimensional, potentially heterogeneous) and time series data. Pandas aims to be the fundamental high-level building block for practical, real-world data analysis in Python, offering a wide range of functionalities to streamline data processing tasks. Key Features and Functionality: - Handling Missing Data: Pandas offers easy handling of missing data, represented as `NaN`, `NA`, or `NaT`, in both floating point and non-floating point data. - Size Mutability: Columns can be inserted and deleted from DataFrame and higher-dimensional objects, allowing for dynamic data manipulation. - Data Alignment: Automatic and explicit data alignment ensures that objects can be aligned to a set of labels, facilitating accurate computations. - Group By Operations: Powerful and flexible group by functionality enables split-apply-combine operations on datasets for both aggregating and transforming data. - Data Conversion: Simplifies converting differently-indexed data in other Python and NumPy data structures into DataFrame objects. - Indexing and Subsetting: Provides intelligent label-based slicing, fancy indexing, and subsetting of large datasets. - Merging and Joining: Facilitates intuitive merging and joining of datasets. - Reshaping and Pivoting: Offers flexible reshaping and pivoting of datasets. - Hierarchical Labeling: Supports hierarchical labeling of axes, allowing multiple labels per tick. - Robust I/O Tools: Includes robust tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultrafast HDF5 format. - Time Series Functionality: Provides time series-specific functionality, including date range generation, frequency conversion, moving window statistics, and date shifting and lagging. Primary Value and User Solutions: Pandas addresses the challenges of data analysis by offering a comprehensive suite of tools that simplify the process of data manipulation, cleaning, and analysis. Its intuitive data structures and functions allow users to perform complex operations with minimal code, enhancing productivity and enabling efficient handling of large datasets. By providing seamless integration with other Python libraries and tools, Pandas serves as a cornerstone for data science workflows, empowering users to extract insights and make data-driven decisions effectively.
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Categories in common with PostgreSQL Data Access Components:
Component Libraries

Selenium WebDriver
By Selenium
4.5/5
(95)
Product Description
Selenium WebDriver is an open-source tool that automates web browser interactions, enabling developers and testers to simulate real user actions for web application testing. It supports multiple programming languages, including Java, Python, C#, Ruby, and JavaScript, allowing users to write test scripts in their preferred language. WebDriver interacts directly with browser drivers, facilitating cross-browser testing across platforms like Chrome, Firefox, Safari, Edge, and Internet Explorer. This direct communication ensures more stable and reliable tests by mimicking real user behavior without the need for a separate server installation. Key Features and Functionality: - Cross-Browser Compatibility: Enables testing across various browsers, ensuring consistent application behavior. - Multi-Language Support: Offers bindings for languages such as Java, Python, C#, Ruby, and JavaScript, providing flexibility for testers. - Direct Browser Control: Interacts with browsers through native drivers, leading to more accurate and stable tests. - Parallel Test Execution: Integrates with Selenium Grid to run tests concurrently on multiple machines, reducing execution time. - Open-Source and Community-Driven: Freely available with a robust community, ensuring continuous improvements and support. Primary Value and Problem Solved: Selenium WebDriver addresses the need for efficient, reliable, and scalable web application testing. By automating browser interactions, it reduces manual testing efforts, accelerates the testing process, and ensures applications function correctly across different browsers and platforms. Its support for multiple programming languages and integration capabilities make it a versatile choice for teams aiming to enhance their testing frameworks and deliver high-quality web applications.
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Categories in common with PostgreSQL Data Access Components:
Component Libraries

Flutter
By Flutter
4.6/5
(68)
Product Description
Flutter is an open-source framework developed by Google for building natively compiled applications across multiple platforms, including mobile, web, desktop, and embedded devices, all from a single codebase. It empowers developers to create high-performance, visually appealing applications that maintain a consistent user experience across different platforms. Key Features and Functionality: - Single Codebase: Developers can write one set of code and deploy it across various platforms, significantly reducing development time and effort. - High Performance: Flutter compiles directly to native ARM or Intel machine code, ensuring fast execution and smooth performance on all devices. - Hot Reload: This feature allows developers to see the results of code changes in real-time without restarting the application, enhancing productivity and facilitating rapid iteration. - Customizable Widgets: Flutter offers a rich set of fully customizable widgets that adhere to both Material Design and Cupertino (iOS-style) guidelines, enabling the creation of visually consistent and adaptive user interfaces. - Extensive Ecosystem: With a vast array of packages and plugins, Flutter supports integration with various services and APIs, extending its functionality to meet diverse development needs. Primary Value and User Solutions: Flutter addresses the challenge of developing and maintaining separate codebases for different platforms by providing a unified framework. This approach not only accelerates the development process but also ensures consistency in app behavior and appearance across platforms. By leveraging Flutter, businesses can reduce costs, streamline workflows, and deliver high-quality applications to a broader audience efficiently.
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Categories in common with PostgreSQL Data Access Components:
Component Libraries

Appsmith
By Appsmith
4.6/5
(66)
Product Description
Appsmith is an open source framework to build admin panels, CRUD apps and workflows. Build everything you need, 10x faster.
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Categories in common with PostgreSQL Data Access Components:
Component Libraries

Qt
By Qt Group
4.3/5
(53)
Product Description
Fully-stocked cross-platform integrated development environment for easy creation of connected devices, UIs and applications.
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Categories in common with PostgreSQL Data Access Components:
Component Libraries

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