CSViewer Integrations

Microsoft Excel, part of Microsoft 365, transforms the way individuals and organizations work with data. It’s a powerful platform for creating dynamic spreadsheets, conducting financial analysis, and uncovering insights through AI-enhanced tools. The new Copilot in Excel uses natural language prompts to generate formulas, analyze trends, and even automate complex data modeling—no coding required. Excel’s integration with Python allows professionals to perform advanced analytics directly within their spreadsheets, bridging the gap between data science and everyday productivity. With PivotTables, charts, and conditional formatting, users can visualize data patterns and make data-driven decisions with confidence. Cloud-based real-time collaboration makes teamwork seamless, letting multiple people coauthor spreadsheets simultaneously from anywhere. Excel’s security, supported by OneDrive and Microsoft Defender, ensures your data remains protected and recoverable. Whether for budgeting, forecasting, or business intelligence, Excel remains the trusted tool for clarity, collaboration, and confidence in every calculation.

Collaborate seamlessly on online spreadsheets from any device and in real-time, making teamwork more efficient. Create a definitive reference point for your data with user-friendly sharing and simultaneous editing capabilities. Enhance your workflow by utilizing comments to assign tasks and keep discussions active. Features like Smart Fill and formula recommendations allow for quicker analysis while minimizing mistakes. Quickly gain insights by posing questions about your data using straightforward language. Sheets integrates smoothly with other beloved Google applications, streamlining your tasks. Effortlessly analyze data collected through Google Forms in Sheets, or incorporate your spreadsheet charts into Google Slides and Docs. Additionally, you can respond to comments directly within Gmail and easily showcase your spreadsheets during Google Meet presentations, making collaboration even more effective. This interconnectedness not only saves time but also enhances productivity across all your projects.

Numerous individuals rely on Excel, VBA/Python scripts, or SQL queries for preparing data, often due to a lack of awareness of superior options available. EasyMorph stands out as a dedicated tool that offers over 150 built-in actions designed for quick and visual data transformation and automation, all without the need for coding skills. By utilizing EasyMorph, you can move beyond complex scripts and unwieldy spreadsheets, significantly enhancing your productivity. This application allows you to seamlessly retrieve data from a variety of sources such as databases, spreadsheets, emails and their attachments, text files, remote folders, corporate applications like SharePoint, and web APIs, all without needing programming expertise. You can employ visual tools and queries to filter and extract precisely the information you require, eliminating the need to consult IT for assistance. Moreover, it enables you to automate routine tasks associated with files, spreadsheets, websites, and emails with no coding required, transforming tedious and repetitive actions into a simple button click. With EasyMorph, not only is the data preparation process simplified, but users can also focus on more strategic tasks instead of getting bogged down in the minutiae of data handling.

Parquet was developed to provide the benefits of efficient, compressed columnar data representation to all projects within the Hadoop ecosystem. Designed with a focus on accommodating complex nested data structures, Parquet employs the record shredding and assembly technique outlined in the Dremel paper, which we consider to be a more effective strategy than merely flattening nested namespaces. This format supports highly efficient compression and encoding methods, and various projects have shown the significant performance improvements that arise from utilizing appropriate compression and encoding strategies for their datasets. Furthermore, Parquet enables the specification of compression schemes at the column level, ensuring its adaptability for future developments in encoding technologies. It is crafted to be accessible for any user, as the Hadoop ecosystem comprises a diverse range of data processing frameworks, and we aim to remain neutral in our support for these different initiatives. Ultimately, our goal is to empower users with a flexible and robust tool that enhances their data management capabilities across various applications.