Open Source Mac Data Analytics Tools

DuckDB is a high-performance analytical database system. It is designed to be fast, reliable and easy to use. DuckDB provides a rich SQL dialect, with support far beyond basic SQL. DuckDB supports arbitrary and nested correlated subqueries, window functions, collations, complex types (arrays, structs), and more. For more information on the goals of DuckDB, please refer to the Why DuckDB page on our website. Processing and storing tabular datasets, e.g. from CSV or Parquet files. Interactive data analysis, e.g. Joining & aggregate multiple large tables. Concurrent large changes, to multiple large tables, e.g. appending rows, adding/removing/updating columns. Large result set transfer to client. For development, DuckDB requires CMake, Python3 and a C++11 compliant compiler. Run make in the root directory to compile the sources. For development, use make debug to build a non-optimized debug version.

Simple SQL Client for lightweight data analysis. You can share the result with gist. Supports MySQL, PostgreSQL (Amazon Redshift), SQLite3, Google BigQuery, Treasure Data, Amazon Athena. You can download and install from Web Site or Releases.

StarRocks is the next generation of real-time SQL engines for enterprise analytics. Real-time analytics is notoriously difficult. Complex data pipelines and de-normalized tables have always been a necessary evil. Processing any updates or deletes once data arrives has not been possible- until now. StarRocks solves these challenges and makes real-time analytics easy. Get amazing query performance on Star or Snowflake Schemas directly. From canceled orders to updated items, your analytics applications can easily handle them with StarRocks. From streaming data to change data capture, StarRocks meets the data ingestion demands of real-time analytics. Scale storage and computing power horizontally and support tens of thousands of concurrent users. All of your BI tools work with StarRocks through standard SQL. StarRocks provides superior performance. It is also a unified OLAP covering most data analytics scenarios.

The PalOOCa Project offers a fast, flexible and intuitive Office-based Business Intelligence solution based on Jedox. It provides an extension for OpenOffice.org Calc which allows both, read and write, access to data from within the Jedox OLAP Server via Calc. If used together with the Open Source Jedox/Palo OLAP Server it completes the Open Source MOLAP-Stack for Business Intelligence. Additionally to Jedox OLAP it is also (read-only) compatible to (almost) all OLAP servers supporting XMLA. Intended audiences are: - scientific research & teaching - financial analysis & controlling Consult the FAQ in the Wiki.

Has accumulated experience in supporting hundreds of Tencent businesses, compatible with various complex system architectures, born in operation and maintenance, and proficient in operation and maintenance. From configuration management to job execution, task scheduling and monitoring self-healing, and then through operation and maintenance big data analysis to assist operational decision-making, it covers the full-cycle assurance management of business operations in a comprehensive manner. The open PaaS has a powerful development framework and scheduling engine, as well as a complete operation and maintenance development training system, which helps the rapid transformation and upgrading of operation and maintenance. Through the Blue Whale intelligent cloud system, it can help enterprises quickly realize the automation of basic operation and maintenance services, thereby accelerating the transformation of DevOps, realizing a tool culture, and maximizing operational efficiency.

A collection of tools for working with the comparative data analysis ontology including import/export facilities for common phylogenetic file formats, and also a triple-store framework.

DbViz produces plots of data contained in MySQL databases. Data can be viewed by looking at line graphs, xy scatter plots, and bar graphs. This greatly simplifies data analysis.

Apache IoTDB (Database for Internet of Things) is an IoT native database with high performance for data management and analysis, deployable on the edge and the cloud. Due to its light-weight architecture, high performance and rich feature set together with its deep integration with Apache Hadoop, Spark and Flink, Apache IoTDB can meet the requirements of massive data storage, high-speed data ingestion and complex data analysis in the IoT industrial fields. In the scene of factories, there are tens of devices under LAN network. IoTDB can be installed on a local controller server in the factory to receive data from those devices. The local controller server (normal PC or workstation) with IoTDB can provide the ability to persist data and query data with SQL-like interface. In addition, with TsFile-Sync tool, TsFiles on the local controller can be transmitted to the data center equipped with IoTDB instance in the cloud.

LinDB is a scalable, high-performance, high-availability distributed time series database. A single server could easily support more than one million write TPS; With fundamental techniques like efficient compression storage and parallel computing, LinDB delivers highly optimized query performance. The multi-channel replication protocol supports any amount of nodes, and ensures the system's availability. Schema-free multi-dimensional data model with Metric, Tags, and Fields; The LinQL is flexible yet handy for real-time data analytics. Horizontal scalable is made simple by adding more new broker and storage nodes without too much thinking and manual operations. And the tags-based sharding strategy resolves the hotspot problem. LinDB is designed to work under a Multi-Active IDCs cloud architecture. The compute layer of LinDB, called brokers, supports efficient Multi-IDCs aggregation query.

TensorBase hopes the open source not become a copy game. TensorBase has a clear-cut opposition to fork communities, repeat wheels, or hack traffic for so-called reputations (like Github stars). After thoughts, we decided to temporarily leave the general data warehousing field. For people who want to learn how a database system can be built up, or how to apply modern Rust to the high-performance field, or embed a lightweight data analysis system into your own big one. You can still try, ask or contribute to TensorBase. The committers are still around the community. We will help you in all kinds of interesting things pursued in the project by us and maybe you. We still maintain the project to look forward to meeting more database geniuses in this world, although no new feature will be added in the near future.

Import width files into MySQL, manipulate and export back to fixed width. Useful for health data analysis or *whatever*. Includes Thompson Reuters Care Discovery 3000 data file definition. Windows, OS X, Linux Requires PHP 5.3.x and MySQL 5.1.x