SQL Server Data Access Components VS bokeh python

Compare SQL Server Data Access Components VS bokeh python and see what are their differences

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SQL Server Data Access Components

Enjoy the highest performance and unlimited possibilities when working with SQL Server

bokeh python

This Python tutorial will get you up and running with Bokeh, using examples and a real-world dataset. You'll learn how to visualize your data, customize and organize your visualizations, and add interactivity.

Landing page //
2023-04-12

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.

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2023-08-18

SQL Server Data Access Components

Website: devart.com
Pricing URL: Official SQL Server Data Access Components Pricing
$ Details: paid Free Trial $199.95 / One-off
Release Date: 2023 May

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bokeh python

Website: realpython.com
Pricing URL: -
$ Details: -
Release Date: -

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SQL Server Data Access Components features and specs

Direct access to server data. Does not require installation of other data provider layers (such as BDE and ODBC)
Interface compatible with standard data access methods, such as BDE and ADO
VCL, LCL and FMX versions of library available
Separated run-time and GUI specific parts allow you to create pure console applications such as CGI
Unicode support

bokeh python features and specs

Interactivity
Bokeh provides interactive plots and dashboards that can enhance the user experience by allowing them to explore data by zooming, panning, and hovering.
Web Integration
It generates outputs that are readily usable in web applications. Bokeh plots can be embedded in web pages, making it suitable for creating dashboards and web-based data visualization applications.
Versatility
Bokeh supports a wide variety of plots and chart types, which allows users to create complex and informative visualizations.
Pythonic Syntax
The library has an API that is intuitive for Python users, making it easier to learn and integrate into Python-based projects.
Server for Real-time Updates
Bokeh server allows for the creation of interactive, real-time streaming web applications, which is useful for applications requiring live data updates.

Possible disadvantages of bokeh python

Learning Curve
Despite its intuitive syntax, Bokeh's extensive capabilities and features can present a steeper learning curve, particularly for beginners in data visualization.
Rendering Performance
For very large datasets, Bokeh might encounter performance issues, such as slower rendering times in the browser compared to other digital visualization technologies.
Limited 3D Capabilities
Unlike some other visualization libraries, Bokeh’s support for 3D plotting is limited, which might be a constraint for users needing advanced 3D plotting features.
Complexity with Advanced Plots
While Bokeh is great for basic plots, creating highly customized or advanced visualizations may require more effort, with users potentially needing to write custom JavaScript callbacks.
Dependencies
Bokeh’s reliance on JavaScript and other underlying libraries might pose challenges in environments where managing dependencies is complex.