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The Python Connector for Microsoft Excel is a robust solution that enables Python applications to seamlessly interact with Microsoft Excel, Apache OpenOffice Calc, and LibreOffice Calc spreadsheets. It allows developers to perform create, read, update, and delete operations on spreadsheet data without the need for additional software installations. Fully compliant with the Python DB API 2.0 specification, this connector is distributed as a wheel package compatible with Windows, macOS, and Linux platforms. Key Features and Functionality: - Direct Connection: Establishes a direct link to Excel workbooks without requiring Microsoft Excel or Microsoft Access Database Engine Redistributable components. Supports file formats including .xlsx, .xls (read-only, and .ods. Enables multiple users to read data from a workbook simultaneously in read-only mode. - High Performance: Facilitates batch processing of multiple update statements to enhance execution speed. - Fast Deployment: Simplifies deployment across multiple user workstations by eliminating the need for additional components, thanks to its direct connection capability. - Platform Support: Available for Windows (32-bit and 64-bit, macOS (64-bit, and Linux (64-bit, ensuring broad compatibility. - Data Types Support: Supports all Microsoft Excel and Python data types, offering options to control data type mapping between them. Primary Value and User Solutions: This connector streamlines the integration of spreadsheet data into Python applications, eliminating the need for intermediary software and reducing deployment complexities. By providing a direct and efficient connection to Excel workbooks, it enhances data processing performance and supports cross-platform compatibility, making it an invaluable tool for developers working with spreadsheet data in Python environments.
The Python Connector for Google BigQuery is a robust and efficient solution designed to facilitate seamless interaction between Python applications and the Google BigQuery data warehouse. Fully implementing the Python DB API 2.0 specification, this connector enables developers to perform create, read, update, and delete operations on BigQuery data with ease. Distributed as a wheel package, it supports both 32-bit and 64-bit versions of Windows and Windows Server, ensuring broad compatibility across various systems. Key Features and Functionality: - Comprehensive SQL Support: The connector fully supports BigQuery's SQL dialects, data types, and query syntax, including functions, operators, and conditional expressions. - High Performance: Features such as connection pooling and local data caching enhance access speed. Additionally, the ability to submit multiple update statements as a batch improves execution time. - Platform Compatibility: Compatible with Windows and Windows Server , the connector supports Python versions from 3.7 to 3.13. - Unicode Compliance: Ensures accurate retrieval and updating of multilingual data, regardless of character encoding, facilitating seamless internationalization. - Extensive Data Type Support: Supports all BigQuery and Python data types, offering additional options to control data type mapping between them. Primary Value and User Benefits: The Python Connector for Google BigQuery addresses the need for a reliable and efficient means of integrating Python applications with Google BigQuery. By providing comprehensive SQL support, high performance, and broad compatibility, it simplifies data operations and enhances productivity for developers working with BigQuery. Its Unicode compliance and extensive data type support ensure accurate handling of diverse datasets, making it an invaluable tool for data-driven applications.
Devart's BigCommerce SSIS Components are designed to streamline the integration of BigCommerce data with various databases and cloud services through SQL Server Integration Services (SSIS. These components facilitate efficient ETL (Extract, Transform, Load processes, enabling users to manage BigCommerce products, customers, orders, brands, and other entities seamlessly. By providing optimized Source, Destination, and Lookup components, along with a dedicated BigCommerce Connection Manager, Devart ensures high-performance data import and export operations. Key Features and Functionality: - SQL Support for BigCommerce: The BigCommerce Source component supports SQL queries, allowing users to execute complex SELECT statements with grouping, filtering, and ordering directly within the SSIS environment. - User-Friendly Source Editor: A convenient editor displays all BigCommerce objects and fields, enabling users to build queries via drag-and-drop. It also lists available system and user variables, facilitating quick integration into SELECT statements. - Advanced Lookup Optimizations: The Lookup component employs advanced optimization techniques, processing multiple rows simultaneously and caching results to minimize server round-trips, thereby enhancing performance. - High-Performance Destination Component: The Destination component supports all DML operations—INSERT, UPDATE, and DELETE—allowing for rapid data loading into BigCommerce. Primary Value and Problem Solved: Devart's BigCommerce SSIS Components address the challenge of integrating BigCommerce data with other systems by providing a robust and efficient solution within the SSIS framework. Users can automate data import and export tasks, synchronize BigCommerce with SQL Server or other data sources, and manage CSV file transfers effortlessly. This integration enhances data consistency, reduces manual effort, and improves overall operational efficiency for businesses leveraging BigCommerce.
The Microsoft Access Python Connector by Devart is a robust solution that enables Python applications to interact seamlessly with Microsoft Access databases. Fully implementing the Python DB API 2.0 specification, this connector facilitates efficient create, read, update, and delete operations on Access databases without the need for additional software installations. It supports both .mdb and .accdb file formats, including those from the latest Microsoft Access versions, and is compatible across Windows, macOS, and Linux platforms. Key Features and Functionality: - Direct Connection: Establishes a direct link to Access databases without requiring Microsoft Access or the Access Database Engine Redistributable, simplifying deployment and reducing dependencies. - High Performance: Supports batch processing of multiple update statements, enhancing execution speed and overall performance. - Cross-Platform Support: Available for Windows , macOS , and Linux , ensuring broad compatibility. - Data Type Compatibility: Offers comprehensive support for all Microsoft Access and Python data types, with options to control data type mapping between them. - Read-Only Multi-User Mode: Enables multiple users to read data from a database simultaneously, overcoming the default single-user limitation of Access databases. Primary Value and User Solutions: The Microsoft Access Python Connector addresses the challenges of integrating Python applications with Access databases by eliminating the need for additional drivers or software installations. Its direct connection capability streamlines deployment, especially in multi-user environments, and its cross-platform support ensures flexibility across different operating systems. By enhancing performance through batch processing and providing comprehensive data type support, the connector simplifies database operations, making it an invaluable tool for developers seeking efficient and reliable Access database integration within their Python projects.
Devart Excel Add-in for Google Workspace allows you to connect Microsoft Excel to Google Workspace, quickly and easily load data from Google Workspace to Excel, instantly refresh data in an Excel workbook from the database, edit these data, and save them back to Google Workspace. It enables you to work with Google Workspace data like with usual Excel worksheets, easily perform data cleansing and de-duplication, and apply all the Excel's powerful data processing and analysis capabilities to these data.
The MySQL and MariaDB Python Connector by Devart is a robust and user-friendly solution designed to facilitate seamless interaction between Python applications and MySQL or MariaDB database servers. Fully implementing the Python DB API 2.0 specification, this connector enables developers to perform create, read, update, and delete operations efficiently. Distributed as a wheel package, it supports multiple platforms, including Windows, macOS, and Linux. Key Features and Functionality: - Direct Connection: Establishes a direct TCP/IP connection to MySQL or MariaDB servers without requiring a database client library, enhancing data transmission speed. - High Performance: Supports batch processing of multiple update statements, improving execution time and overall application performance. - Fast Deployment: Simplifies deployment across multiple user workstations by eliminating the need to distribute the database client library. - Secure Communication: Ensures encrypted communication using SSL/TLS, SSH tunneling, and HTTP/HTTPS tunneling, safeguarding data integrity and confidentiality. - Platform Support: Compatible with Windows (32-bit and 64-bit, macOS (64-bit, and Linux (64-bit, offering flexibility across different operating systems. - Unicode Compliance: Handles multilingual data seamlessly, supporting various character encodings such as Chinese, Cyrillic, and Hebrew. - Comprehensive Data Type Support: Supports all MySQL/MariaDB and Python data types, with options to control data type mapping between them. Primary Value and Problem Solved: The MySQL and MariaDB Python Connector addresses the need for a reliable, high-performance, and secure method to connect Python applications with MySQL and MariaDB databases. By offering direct connections without the necessity of a client library, it streamlines deployment and enhances data transmission speeds. Its support for secure communication protocols ensures data security, while compatibility across multiple platforms and comprehensive data type support make it a versatile tool for developers working in diverse environments.
dotConnect for SQL Server, formerly known as SQLDirect .NET, is an enhanced data provider for SQL Server that builds on ADO.NET technology and SqlClient to present a complete solution for developing SQL Server-based database applications. As part of the Devart database application development framework, dotConnect for SQL Server offers both high performance native connectivity to SQL Server and a number of innovative database development tools and technologies. dotConnect for SQL Server introduces new approaches for designing application architecture, boosts productivity, and leverages database application implementation.
Devart Excel Add-in for Freshdesk allows you to connect Microsoft Excel to Freshdesk, quickly and easily load data from Freshdesk to Excel, instantly refresh data in an Excel workbook from the database, edit these data, and save them back to Freshdesk. It enables you to work with Freshdesk data like with usual Excel worksheets, easily perform data cleansing and de-duplication, and apply all the Excel's powerful data processing and analysis capabilities to these data.
dotConnect for FreshBooks is an ADO.NET provider for working with FreshBooks data through the standard ADO.NET or Entity Framework interfaces. It allows you to easily integrate FreshBooks data into your .NET applications, and integrate FreshBooks services with widely used data-oriented technologies. dotConnect for FreshBooks has the same standard ADO.NET classes as other standard ADO.NET providers: FreshBooksConnection, FreshBooksCommand, FreshBooksDataAdapter, FreshBooksDataReader, FreshBooksParameter, etc. This allows you quickly get started with it and eliminates the need to study any FreshBooks data access specificities.
Devart Reviews
Lyudmila Z.
Marketing specialist at EVADAV - The Evolving Ad NetworkSalesforce to BigQuery sync
Burke A.
Cost-Effective Integration Solution
Maksym V.
Посада COO at GeonixA solid integration platform for day-to-day data workflows
About
Streamline complex data tasks and increase productivity with our expert solutions
Since its founding in 1997, Devart has been working on simplifying and enhancing data workflows for both individual professionals and teams, including enterprise-level organizations from the Fortune 100 list. Currently, Devart is one of the leading developers of data integration, backup, management, and connectivity solutions, as well as database tools for major database management systems and cloud platforms.
