A New Way to Build Data Apps: Orchestrate analytics, logic, and visualization as code.

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LunivCore Framework: Business Intelligence as Code

Build dashboards, analytical reports, and entire BI workflows using Lua or the purpose-built lcore language without the overhead of traditional enterprise tools.

This monorepo houses the full LunivCore ecosystem:

• a compact C/Lua interpreter
• structured documentation
• development utilities designed to deliver a fast, extensible, and highly maintainable BI platform.

We provide a foundation for teams seeking to standardize analytics as code, accelerate experimentation, and integrate BI logic directly into their application stack.

How It Works

See It In Action

LunivCore is an open-source, engineer focused alternative to PowerBI, Jupyter Notebook, evidence.dev, Tableau, Streamlit, Shiny, and other business intelligence/reporting tool.

Instantly start answering the most important questions for your business.

This .lcore file:

# Sales Report Q1 (examples/helloworld.lcore)
text "Regional sales are strong this year in the West."

dataset Sales "Q1 Regional Sales" {
    North: 340
    South: 120
    East: 210
    West: 450
}

plot Sales as bar

view MarketShare "Market Share by Segment (%)" {
    SegmentA: 90
    SegmentB: 8
    SegmentC: 2
    text "Analysis Notes: North dominates the market."
    text "Segments B and C are negligible."
}
plot MarketShare as bar

Outputs:

╔════════════════════════════════════════════════╗
║Regional sales are strong this year in the West.║
╚════════════════════════════════════════════════╝
--- Q1 Regional Sales ---
North      | ██████████████████████████████████ (340)
South      | ████████████ (120)
East       | █████████████████████ (210)
West       | █████████████████████████████████████████████ (450)
----------------
╔════════════════════════════════════════════════╗
║          Market Share by Segment (%)           ║
╠════════════════════════════════════════════════╣
║ SegmentA : 90 │█████████████████████████████ │ ║
║ SegmentB :  8 │███░░░░░░░░░░░░░░░░░░░░░░░░░░ │ ║
║ SegmentC :  2 │█░░░░░░░░░░░░░░░░░░░░░░░░░░░░ │ ║
║  Analysis Notes: North dominates the market.   ║
║        Segments B and C are negligible.        ║
╚════════════════════════════════════════════════╝

Or run it on the Lua VM through C functions:

-- Create datasets dynamically and aggregate values
local regions = {"North", "South", "East", "West"}
local q1_values = {340, 210, 210, 450}
local q2_values = {400, 190, 220, 470}

local q1_ds = BI.new("Q1 Aggregation KPIs")
local q2_ds = BI.new("Q2 Aggregation KPIs")

-- Populate datasets using loops
for i, region in ipairs(regions) do
    q1_ds:add(region, q1_values[i])
    q2_ds:add(region, q2_values[i])
end

-- Perform basic analytics
print("Q1 Total:", q1_ds:sum())
print("Q2 Total:", q2_ds:sum())
print("Difference Q2-Q1:", q2_ds:sum() - q1_ds:sum())

-- Advanced analytics: conditional highlights
for i, region in ipairs(regions) do
    local q1_val = q1_values[i]
    local q2_val = q2_values[i]
    if q2_val > q1_val then
        BI.text(region .. " exceeded projections by " .. (q2_val - q1_val))
    else
        BI.text(region .. " lagged projections by " .. (q1_val - q2_val))
    end
end

-- Combine datasets into a single view
local market_view = {
    title = "Quarterly Market Share Comparison",
    rows = {},
    children = {}
}

for i, region in ipairs(regions) do
    table.insert(market_view.rows, {
        name = region,
        value = q2_values[i]
    })
    table.insert(market_view.children, {
        type = "text",
        value = string.format("%s Q1: %d, Q2: %d", region, q1_values[i], q2_values[i])
    })
end

BI.render_view(market_view)

-- Final summary
BI.text("All datasets processed successfully.")

Q1 Total: 1210
Q2 Total: 1280
Difference Q2-Q1: 70
╔════════════════════════════════════════════════╗
║        North exceeded projections by 60        ║
╚════════════════════════════════════════════════╝
╔════════════════════════════════════════════════╗
║         South lagged projections by 20         ║
╚════════════════════════════════════════════════╝
╔════════════════════════════════════════════════╗
║        East exceeded projections by 10         ║
╚════════════════════════════════════════════════╝
╔════════════════════════════════════════════════╗
║        West exceeded projections by 20         ║
╚════════════════════════════════════════════════╝
╔════════════════════════════════════════════════╗
║       Quarterly Market Share Comparison        ║
╠════════════════════════════════════════════════╣
║ North : 400 │██████████████████████████░░░░░ │ ║
║ South : 190 │█████████████░░░░░░░░░░░░░░░░░░ │ ║
║ East  : 220 │███████████████░░░░░░░░░░░░░░░░ │ ║
║ West  : 470 │███████████████████████████████ │ ║
║             North Q1: 340, Q2: 400             ║
║             South Q1: 210, Q2: 190             ║
║             East Q1: 210, Q2: 220              ║
║             West Q1: 450, Q2: 470              ║
╚════════════════════════════════════════════════╝
╔════════════════════════════════════════════════╗
║      All datasets processed successfully.      ║
╚════════════════════════════════════════════════╝

LunivCore generates a terminal-dashboard or website from markdown-inspired .lcore or Lua files:

• Low-level framework engineered for integration anywhere that supports C, including microcontrollers, websites, servers, and more.

• Built-in Interpreter: View the powerful lcore interpreter.

• SQL-like statements inside markdown-inspired files to run queries against your data sources
• Charts and components are rendered using these query results
• Templated pages generate many pages from a single markdown template
• Loops and If / Else statements allow control of what is displayed to users
• Coded for Performance: LunivCore is made with C, enabling high-performance.
• Production-Grade: Ships with a Lua unit testing framework.
• Portable: Built using standard C (C99) and the embeddable Lua library.
• Support: On-going, lifetime support from the passionate dev team at Luniv Technology

Benchmark Results

The following table summarizes the performance as the dataset size scaled by a factor of nearly 3,000x (from 5.5K to 11.9M rows).

Test	Dataset Count (Rows)	Executed Time (ms)	User Time (ms)	System Time (ms)	Throughput
1	5,508	1.64	0.23	1.47	approx 3.3 million rows per second
2	396,647	13.46	11.05	2.41	approx 29.5 million rows per second
3	3,173,183	88.27	78.76	9.14	approx 35.9 million rows per second
4	6,346,367	169.73	138.69	30.48	approx 37.4 million rows per second
5	11,908,037	367.70	304.11	60.99	approx 32.4 million rows per second*

Read the full report.

Why Now?

I built LunivCore after getting tired of the constraints of traditional enterprise BI platforms.

Instead of relying on a Markdown-driven system like evidence, I wanted a lower-level foundation with less external dependencies.

Something lean, predictable, and fully under my control. Using C and Lua gives me that flexibility.

This gives me the freedom to layer features on top such as Markdown rendering, web output, and anything else in a deliberate manner.

-Nick

License

LunivCore is licensed under the MIT license. See the LICENSE file for licensing information.