FusionCharts
FusionCharts is a powerful and easy-to-use JavaScript charting library that helps developers to add interactive charts and data visualizations to their web and mobile applications. With 100+ chart types, including column, bar, line, area, pie, doughnut, scatter, bubble, and more, it's easy to create professional-looking charts that are engaging and informative. The library is completely cross-browser compatible and works seamlessly with a wide range of technologies, including Angular, React, Vue, and more.
FusionCharts product suite consists of
• FusionCharts Suite XT
• FusionTime
• FusionExport
• FusionGrid
FusionCharts offers a wide range of features that make it one of the most popular charting libraries on the market, including:
• Real-time data updates
• Dynamic updates of data using AJAX
• Drill-down and multi-level charts
• Animation and special effects
• Export to PDF, PNG, and SVG
• Responsive design
• Accessibility support
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Polars
Knowing of data wrangling habits, Polars exposes a complete Python API, including the full set of features to manipulate DataFrames using an expression language that will empower you to create readable and performant code. Polars is written in Rust, uncompromising in its choices to provide a feature-complete DataFrame API to the Rust ecosystem. Use it as a DataFrame library or as a query engine backend for your data models.
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PySpark
PySpark is an interface for Apache Spark in Python. It not only allows you to write Spark applications using Python APIs, but also provides the PySpark shell for interactively analyzing your data in a distributed environment. PySpark supports most of Spark’s features such as Spark SQL, DataFrame, Streaming, MLlib (Machine Learning) and Spark Core. Spark SQL is a Spark module for structured data processing. It provides a programming abstraction called DataFrame and can also act as distributed SQL query engine. Running on top of Spark, the streaming feature in Apache Spark enables powerful interactive and analytical applications across both streaming and historical data, while inheriting Spark’s ease of use and fault tolerance characteristics.
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OpenObserve
OpenObserve is an open source observability platform for logs, metrics, and traces that emphasizes high performance, scalability, and dramatically lower cost. It supports petabyte-scale observability thanks to features like data compression using columnar storage and the ability to use “bring your own bucket” storage (local disk, S3, GCS, Azure Blob, etc.). It is written in Rust, uses the DataFusion query engine to directly query Parquet files, and provides a stateless, horizontally scalable architecture with caching (both result and disk) to maintain speed under heavy load. It embraces open standards (OpenTelemetry compatibility, vendor-neutral APIs), so it fits into existing monitoring/logging workflows. Key modules include logs, metrics, traces, frontend monitoring, pipelines, alerts, and dashboards/visualizations.
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