Steema/TeeBI
Steema/TeeBI has 152 GitHub stars and 63 forks. Star changes are not measured yet. Last checked 2026-11-14 01:53 UTC. Last push 2026-11-12.
Measured star growth
- in 24h
- Collecting history
- in 7 days
- Collecting history
- in 15 days
- Collecting history
- in 1 month
- Collecting history
Net star changes over overlapping periods, not daily totals. Missing measurements are left blank. Stars measure attention, not software quality.
About
Data-mining and Visualization components for RAD Studio
DataREADME.md
OriginalTeeBI
Telegram forums: https://t.me/steema_bi
Datamining, Visualization, Multidimensional Queries, Pivot Tables, Big data
What is TeeBI ?
A simple class to implement in-memory database complex structures, to provide:
Ultra fast speed (every column or field is a simple array)
Big-data ready, billions of cells supported
Multi-platform (Windows, Mac OSX, Android, iOS, Linux and more), for VCL, Firemonkey, Delphi and C++. 100% Pure Pascal, no dependencies, no server, just RTL.
Automatic visualizations of complex structures (charts, grids, optional TeeGrid and more)
SQL-like high-speed queries and summaries by code or "select" scripts
Multi-dimensional Excel-like pivot tables, with drag & drop editor
Geographic database (Countries, States, Provinces...) to automatically create summary geo queries, TeeChart world maps, and statistics

Transparent optional remote web server fast data access (compressed binary streams of raw arrays)
Automatic relationships between columns (master-detail), indexes and foreign keys
Import data from files, databases, objects (ORM), AI in one or few lines of code
Export data (to csv,pdf,html,xml,excel,json), comparisons, manipulations and conversions
TeeBI makes very easy to create custom dashboard no-code analytic applications for your end-users. Ready to use dialogs enable easy runtime connections to many database engines and to all kind of data sources, and drag and drop in the query dialogs allow selecting fields as measures, dimensions, parameters and filters with just mouse clicks.
Supported development environments:
- Embarcadero RAD Studio Florence 13.0, and from Studio XE4 and up (Delphi and C++)
- Lazarus / FreePascal
Getting started
Example code
uses BI.DataItem, BI.CSV, BI.Db, BI.Xml, BI.Json, BI.AI;
var Data1 : TDataItem;
Data1 := TBICSV.FromFile( 'mydata.csv' ); // also From TStrings, String etc
// other importing methods:
Data1 := TBIDB.From( SQLConnection1 ); // loads all tables in the connection in one line of code
// import all tables from a database, one line of code:
Data1 := TBIDB.Import(FDConnection1, False).ToItem;
// import data from different formats
Data1 := TBIJson.From ...
Data1 := TBIXML.From ...
// import from Artificial Intelligence AI agents, like Google Gemini:
Data1 := TBIAI.From('Give me the list of the highest 10 mountains by elevation in csv format, just the list');
// from arrays, TCollection, custom Records (via RTTI):
Data1 : TTypeProvider<TCustomer>.Create(Self, MyArrayOfCustomers).Data;
// import from components with automatic detection:
Data1 := TComponentImporter.From(Self, Memo1.Lines);
Data1 := TComponentImporter.From(Self, DataSource1);
// importing from any URL, automatic detection of content format:
Data1 := TBIURLSource.From('https://www.mysite.com/get/mydata?param=123');
// queries, including group by, sort, expressions, having, sub-select, distinct, date operators etc
Data1 := TBISQL.From( Data2, 'sum(Amount) where (Customer=123) and (Product<456) group by Country, Year');
Data1 := TBISQL.From( Data2, 'ProductName, UnitPrice where UnitPrice > select Average(UnitPrice)');
Data1 := TBISQL.From( Movies, 'top 100 offset 15000 year, length');
// visualizing
BIGrid1.Data := Data1;
BIChart1.Data := Data1;
BIGrid2.Data := Data1['Products']; // sub-tables
// importing data from a standard TeeChart Series:
Data1 := TChartData.From(Series1);
Online grids and charts from remote queries using BIWeb Server:
http://steema.cat:15015/?data=SQLite_demo&format=.jpg
Related
Deprecated
Features marked as obsolete and moved to a separate folder, but still usable:
Machine-learning (native data pass to R Language and Python Scikit)
Interactive Dashboards rendered to screen (desktop and mobile) and HTML Web pages
Explore related projects
Asabeneh/30-Days-Of-Python
The 30 Days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than 100 days. Follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw
78,711 starsgrafana/grafana
The open and composable observability and data visualization platform. Visualize metrics, logs, and traces from multiple sources like Prometheus, Loki, Elasticsearch, InfluxDB, Postgres and many more.
77,894 starsredis/redis
For developers, who are building real-time data-driven applications, Redis is the preferred, fastest, and most feature-rich cache, data structure server, and document and vector query engine.
77,490 starsapache/superset
Apache Superset is a Data Visualization and Data Exploration Platform
75,522 stars
