GeoSpock
GeoSpock enables data fusion for the connected world with GeoSpock DB – the space-time analytics database. GeoSpock DB is a unique, cloud-native database optimised for querying for real-world use cases, able to fuse multiple sources of Internet of Things (IoT) data together to unlock its full value, whilst simultaneously reducing complexity and cost. GeoSpock DB enables efficient storage, data fusion, and rapid programmatic access to data, and allows you to run ANSI SQL queries and connect to analytics tools via JDBC/ODBC connectors. Users are able to perform analysis and share insights using familiar toolsets, with support for common BI tools (such as Tableau™, Amazon QuickSight™, and Microsoft Power BI™), and Data Science and Machine Learning environments (including Python Notebooks and Apache Spark). The database can also be integrated with internal applications and web services – with compatibility for open-source and visualisation libraries such as Kepler and Cesium.js.
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StarTree
StarTree, powered by Apache Pinot™, is a fully managed real-time analytics platform built for customer-facing applications that demand instant insights on the freshest data. Unlike traditional data warehouses or OLTP databases—optimized for back-office reporting or transactions—StarTree is engineered for real-time OLAP at true scale, meaning:
- Data Volume: query performance sustained at petabyte scale
- Ingest Rates: millions of events per second, continuously indexed for freshness
- Concurrency: thousands to millions of simultaneous users served with sub-second latency
With StarTree, businesses deliver always-fresh insights at interactive speed, enabling applications that personalize, monitor, and act in real time.
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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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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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