Sliq Integrations

Microsoft Excel is the industry-standard spreadsheet application that helps users organize, analyze, and visualize data with precision and power. Whether you’re managing budgets, tracking performance, or analyzing complex datasets, Excel simplifies every task with intuitive tools and intelligent automation. With Copilot, you can now ask Excel to write formulas, summarize data, or create visualizations—all powered by AI. From basic spreadsheets to advanced financial modeling, Excel adapts to your skill level and workflow. Its cloud collaboration through Microsoft 365 lets multiple users edit, share, and comment in real time from any device. With flexible templates, built-in charts, and cross-platform integration, Excel turns numbers into insights you can act on.

Google Sheets is an AI-powered online spreadsheet platform designed to help individuals and teams organize, analyze, and visualize data collaboratively. The platform includes Gemini AI features that allow users to create formulas, generate trackers, analyze datasets, and build data visualizations using simple text prompts. Google Sheets supports real-time collaboration with live co-editing, comments, version history, and customizable sharing controls that help teams work together from any location. Users can streamline workflows with smart tables, pre-built templates, Connected Sheets for BigQuery and Looker data, and integrations with third-party applications such as Salesforce, Intuit, and Asana. The platform also enhances Microsoft Excel files by adding collaborative editing, intelligent suggestions, and task management capabilities directly within spreadsheets.

When n2y subscribers began telling us everything they wanted in a comprehensive IEP software solution, we listened and got right to work. Today, Polaris is setting the new standard for effortless, effective collaboration throughout every stage of the IEP process, planning, writing, executing, and reporting. Creating accurate, data‑driven roadmaps has never been easier, saving educators weeks of time per individual student and clearly pointing the way to compliance. As an essential stand‑alone solution or a crucial part of the n2y total solution, Polaris is mapping better outcomes and brighter futures. Polaris ensures everyone on the IEP team is working completely in sync to provide a consistent, customized learning journey with students at the heart of their vision. Use accurate, up-to-date, highly visible student performance data to inform decisions. All of the included assessments, rubrics, and profiles produce truly reliable data for determining levels of academic performance.

JSON (JavaScript Object Notation) is a lightweight data-interchange format. It is easy for humans to read and write. It is easy for machines to parse and generate. It is based on a subset of the JavaScript Programming Language Standard ECMA-262 3rd Edition - December 1999. JSON is a text format that is completely language independent but uses conventions that are familiar to programmers of the C-family of languages, including C, C++, C#, Java, JavaScript, Perl, Python, and many others. These properties make JSON an ideal data-interchange language. JSON is built on two structures: 1. A collection of name/value pairs. In various languages, this is realized as an object, record, struct, dictionary, hash table, keyed list, or associative array. 2. An ordered list of values. In most languages, this is realized as an array, vector, list, or sequence. These are universal data structures. Virtually all modern programming languages support them in one form or another.

pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. Tools for reading and writing data between in-memory data structures and different formats: CSV and text files, Microsoft Excel, SQL databases, and the fast HDF5 format. Intelligent data alignment and integrated handling of missing data: gain automatic label-based alignment in computations and easily manipulate messy data into an orderly form.Aggregating or transforming data with a powerful group by engine allowing split-apply-combine operations on data sets. Time series-functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging. Even create domain-specific time offsets and join time series without losing data.

We created Parquet to make the advantages of compressed, efficient columnar data representation available to any project in the Hadoop ecosystem. Parquet is built from the ground up with complex nested data structures in mind, and uses the record shredding and assembly algorithm described in the Dremel paper. We believe this approach is superior to simple flattening of nested namespaces. Parquet is built to support very efficient compression and encoding schemes. Multiple projects have demonstrated the performance impact of applying the right compression and encoding scheme to the data. Parquet allows compression schemes to be specified on a per-column level, and is future-proofed to allow adding more encodings as they are invented and implemented. Parquet is built to be used by anyone. The Hadoop ecosystem is rich with data processing frameworks, and we are not interested in playing favorites.