B/S architecture of open source data visualization analysis platform is not a complete collection for your reference. In no particular order. Welcome to add.
kibana
Elasticsearch – dedicated data analysis search dashboard. K in the ELK Stack.
Common open source visualization solutions for logging systems.
Developed using Nodejs+AnglarJs+React, the metadata is stored in an index of ES.
Elastic maintains open source and has a vibrant, iterative community.
grafana
Developed with Golang+TypeScript+AngularJS, metadata supports mysql and Postgres.
Maintained by Grafana Labs, the community is very active and constantly iterating.
Superset
A lot of big companies use it internally.
Supported data sources include MySQL, Postgres, Vertica, Oracle, Microsoft SQL Server, SQLite, Greenplum, Firebird, MariaDB, Sybase, and IBM DB2, Exasol, MonetDB, Snowflake, Redshift, Clickhouse, Apache Kylin and more!
Developed using Python+Flask+ React +jQuery, sqLite is used by default to store metadata. Open source by Airbnb, it is now part of the Apache Incubation program, with a very active and iterative community.
Zeppelin
For Java+Angular development, metadata notebooks are stored in git repositories using local file systems by default. Open source by Apache, in continuous iteration, currently version 0.8.
Hue
Develop and access SQL, data application workbench, support intelligent SQL and task editor, Dashboard, task workflow scheduling, data browser. Hadoop ecosystem visualization tools.
SQL support: Hive, Impala, MySQL, Oracle, KSQL/Kafka SQL, Solr SQL, Presto, PostgreSQL, Redshift, BigQuery, AWS Athena, Spark SQL, Phoenix, Kylin, etc.
Tasks supported: MapReduce, Java, Pig, Sqoop, Shell, DistCp, Spark, etc.
Developed using Python+Django+jquery, metadata is stored using SQLite by default.
Hue evolved from Cloudera Desktop. Cloudera contributed Hue to the Apache Foundation Hadoop community.
CBoard
Support JDBC data source, Saiku2.x data source, Kylin1.6, Elasticsearch 1.x, 2.x, 5.x.
Developed using Java Spring+MyBatis+AngularJS+Bootstrap. The metadata uses MySQL5+/SQLServer.
Shanghai Chu company open source, recently found that the official out of the enterprise version of the charge, the community version appears to be a lot of low.
Mining
BI apps written in Python (Pandas Web interface)
OpenMining supports all databases based on ORM SQLAlchemy.
Developed using Python+Lua+AngularJs+jQuery, metadata is stored in MongoDB.
By Avelino and UP! The latest commit for the master branch, developed by Essencia, is 2016
Saiku
It provides a Schema designer, interactive reporting engine, display boards, and NoSQL linking technology. Connect to the OLAP system using REST apis.
Using Java+ Backbone +jQuery development, using JackRabbit tree metadata management.
Originally called the Pentaho analysis Tool, it was originally a front-end analysis tool wrapped in GWT (Google Web Toolkit) based on the OLAP4J library. Renamed Saiku and supported by Analytical Labs.
Metabase
Use Clojure and Node development, front-end use react framework. Metadata is stored in the H2 database by default.
The community is active and the project is constantly updated.
redash
SQL Editor + Visualization, supporting 35 data sources: Amazon Athena, Amazon DynamoDB, Amazon Redshift, Axibase Time Series Database, Cassandra, ClickHouse, CockroachDB, CSV, Databricks, DB2 by IBM, Druid, Elasticsearch, Google Analytics, Google BigQuery, Google Spreadsheets, Graphite, Greenplum, Hive, Impala, InfluxDB, JIRA, JSON, Apache Kylin, MapD, MemSQL, Microsoft SQL MySQL, Oracle, PostgreSQL, Presto, Prometheus, Python, Qubole, Rockset, Salesforce, ScyllaDB, Shell Scripts, Snowflake, SQLite, TreasureData, Vertica, Yandex AppMetrrica, Yandex Metrica.
The backend uses Python and the front-end uses Angular and React. The metadata environment uses PostgreSQL & Redis.
The project is currently active and iterating.
SqlPad
Developed using Nodejs+React, metadata is stored in Nedb.
Developed by Rick Bergfalk and in ongoing maintenance.
conclusion
These are some of the open source solutions for data visualization that I have collected or researched, and they may seem disorganized in the presence of mature, stable enterprise-level solutions, or unprofessional in the presence of large, crowded factories. But their developers provide excellent reference cases and masterworks for secondary development, giving small businesses almost free data analysis and visualization. A big thank you to these exciting projects and to all the people who contribute to open source.
Since I have not fully experienced and thoroughly investigated the above projects, the above brief introduction is for reference only and the official one shall prevail.