LunivCore, Business Intelligence As Code

The LunivCore Advantage

We built a BI tool that thinks like an engineering team.

Version Control Your Data

Reports are code. Git diff your analytics. Review changes before deployment. Audit every calculation modification.

C-Level Performance

Achieve Tens of Millions of Rows per Second throughput. The C core executes full-scan analytical queries on billion-row datasets in under 30 seconds on standard hardware, leveraging native memory efficiency.

See the Test

Deploy Anywhere

Lightweight C core (< 5MB). Embeddable in any application via C API or Lua bindings. Perfect for SaaS, Edge, and IoT.

Developer-First UX

Markdown-inspired .lcore syntax and Lua scripting for complex logic. Ship analytics faster without drag-and-drop complexity.

Zero External Dependencies

Self-contained runtime. No database lock-in. Just the core C engine and Lua runtime.

Open Source Forever

Core engine is MIT licensed. Built by and for the community.

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See It In Action: Code & Output

Analytics should be auditable, readable code. The LunivCore DSL lets engineers write reports with clean, minimal syntax.

LCore DSL

Write analytics-as-code using our new language, lcore:

lcore

dataset Sales "Q1 Revenue by Region" {
    APAC: 2850000
    Americas: 3450000
    EMEA: 1920000
}

plot Sales as bar

Output (Generated by the C Core):

┌─ Q1 Revenue by Region
├─────────────────────────────────────────────────────────────────
  APAC       │ ████████████████████████████████ (2850000)
  Americas   │ ███████████████████████████████████████ (3450000)
  EMEA       │ ██████████████████████ (1920000)
└─────────────────────────────────────────────────────────────────

lcore

  text "Exploratory Analysis With LunivCore"

  text "Process Data"

  dataset regional_sales "Journal #1 Data (Sales $)" {
      Midwest: 1124
      Northeast: 2545
      West: 728
      South: 1954
      Texas: 1720
      California: 2140
  }

  text "First, we need to get some basic statistics."

  sum regional_sales
  avg regional_sales
  min regional_sales
  max regional_sales
  count regional_sales

  text "Now let us visualize the data distribution"

  plot regional_sales as bar

Output (Generated by the C Core):

┌─ Note
  Exploratory Analysis With LunivCore
└──────────────────────────────────────────────────────────────────────────────┘

┌─ Note
  Process Data
└──────────────────────────────────────────────────────────────────────────────┘

┌─ Journal #1 Data (Sales $) Raw Data
  Midwest          │███████████████     1124
  Northeast        │███████████████████████████████████     2545
  West             │██████████      728
  South            │██████████████████████████     1954
  Texas            │███████████████████████     1720
  California       │█████████████████████████████     2140
└──────────────────────────────────────────────────────────────────────────────┘

┌─ Note
  First, we need to get some basic statistics.
└──────────────────────────────────────────────────────────────────────────────┘
Sum(regional_sales) = 10211
Avg(regional_sales) = 1701.83
Min(regional_sales) = 728
Max(regional_sales) = 2545
Count(regional_sales) = 6

┌─ Note
  Now let us visualize the data distribution
└──────────────────────────────────────────────────────────────────────────────┘

┌─ regional_sales bar
  Value Distribution
  Midwest                   │█████████████████     1124
  Northeast                 │████████████████████████████████████████     2545
  West                      │███████████      728
  South                     │██████████████████████████████     1954
  Texas                     │███████████████████████████     1720
  California                │█████████████████████████████████     2140
└──────────────────────────────────────────────────────────────────────────────┘

Lua Components

Build complex, reusable logic in Lua:

Value Card

print("\n>>> Integrated Dashboard View\n")
BI.render_view({
    title = "EXECUTIVE DASHBOARD",
    rows = {
        {name = "Total Revenue", value = 12500},
        {name = "Profit Margin", value = 3750},
        {name = "Customer Count", value = 850},
        {name = "Market Share", value = 2340},
    }
})

Output (Generated through the Lua VM)

>>> Integrated Dashboard View

  ┌─ EXECUTIVE DASHBOARD Container
    Total Revenue            : 12500
    Profit Margin            : 3750
    Customer Count           : 850
    Market Share             : 2340
  └───────────────────────────────────────────┘

Tables

print("\n>>> Performance Metrics\n")
BI.chart("table", {
    {name = "Response Time", value = 45},
    {name = "Uptime %", value = 9999},
    {name = "Users", value = 1250},
    {name = "Requests/sec", value = 320},
})

Output (Generated through the Lua VM)

>>> Performance Metrics

┌─ chart table
  ┌───────────────────────────────────────────────────────────┬───────────────┐
  │ Metric                                                    │ Value         │
  ├───────────────────────────────────────────────────────────┼───────────────┤
  │ Response Time                                             │         45    │
  │ Uptime %                                                  │       9999    │
  │ Users                                                     │       1250    │
  │ Requests/sec                                              │        320    │
  └───────────────────────────────────────────────────────────┴───────────────┘
└──────────────────────────────────────────────────────────────────────────────┘

Charts

-- Test 1: Sales Dashboard
print("\n>>> Sales Performance Dashboard\n")
local sales = BI.new("Q3 2024 Sales")
sales:add("North", 2500)
sales:add("South", 1800)
sales:add("East", 3200)
sales:add("West", 2900)
sales:chart("bar")

-- Test 2: Product Distribution
print("\n>>> Product Distribution\n")
BI.chart("pie", {
    {name = "Product A", value = 15000},
    {name = "Product B", value = 23000},
    {name = "Product C", value = 18000},
    {name = "Product D", value = 27000},
})

Output (Generated through the Lua VM)

>>> Sales Performance Dashboard

  ┌─ chart bar
    Value Distribution
    North                     │███████████████████████████████     2500
    South                     │██████████████████████     1800
    East                      │████████████████████████████████████████     3200
    West                      │████████████████████████████████████     2900
  └──────────────────────────────────────────────────────────────────────────────┘

>>> Product Distribution

  ┌─ chart pie
    Share of Total (n=83000)
    ████  18.1% │ Product A
    ██████  27.7% │ Product B
    █████  21.7% │ Product C
    ████████  32.5% │ Product D
  └───────────────────────────────────────────┘

Exploratory Analysis

Perform exploratory analysis and create data journals with Lua or lcore:

Lua

BI.text('Exploratory Analysis With LunivCore')

BI.text('Process Data')
print('→ loading data...')

local data = BI.new('Journal #1 Data (Sales $)')
data:add("Midwest", 1124)
data:add("Northeast", 2545)
data:add("West", 728)
data:add("South", 1954)
data:add("Texas", 1720)
data:add("California", 2140)

print('✓ finished loading data...\n')

BI.text('First, we need to get some basic statistics.')

print('Computing statistics...\n')
print(string.format('  Total Sales:    $%d', data:sum()))
print(string.format('  Average Sales:  $%.2f', data:avg()))
print(string.format('  Min Sales:      $%d', data:min()))
print(string.format('  Max Sales:      $%d', data:max()))
print(string.format('  Regions:        %d\n', data:count()))

BI.text('Now let us visualize the data distribution')

print('Rendering bar chart...')
data:chart('bar')

print('Rendering second chart...')
data:chart('pie')

print('Rendering table...')
data:chart('table')

BI.text('Key Findings')

print('\nAnalysis Summary:')
print(string.format('  • Top Region: California ($%d)', data:max()))
print(string.format('  • Lowest Region: West ($%d)', data:min()))
print(string.format('  • Standard Coverage: %d regions analyzed', data:count()))
print(string.format('  • Revenue Range: $%d', data:max() - data:min()))
print(string.format('  • Average per Region: $%.0f\n', data:avg()))

BI.text('Conclusion')

print('The regional sales data shows:')
print('  - Northeast leads with $2,545 in sales')
print('  - California performs well at $2,140')
print('  - West region has lowest sales at $728')
print('  - Significant variance across regions suggests targeted marketing opportunities')
print()

Output (Generated through the Lua VM)

┌─ Note
  Exploratory Analysis With LunivCore
└──────────────────────────────────────────────────────────────────────────────┘

┌─ Note
  Process Data
└──────────────────────────────────────────────────────────────────────────────┘
→ loading data...
✓ finished loading data...


┌─ Note
  First, we need to get some basic statistics.
└──────────────────────────────────────────────────────────────────────────────┘
Computing statistics...

  Total Sales:    $10211
  Average Sales:  $1701.83
  Min Sales:      $728
  Max Sales:      $2545
  Regions:        6


┌─ Note
  Now let us visualize the data distribution
└──────────────────────────────────────────────────────────────────────────────┘
Rendering bar chart...

┌─ chart bar
  Value Distribution
  Midwest                   │█████████████████     1124
  Northeast                 │████████████████████████████████████████     2545
  West                      │███████████      728
  South                     │██████████████████████████████     1954
  Texas                     │███████████████████████████     1720
  California                │█████████████████████████████████     2140
└──────────────────────────────────────────────────────────────────────────────┘
Rendering second chart...

┌─ chart pie
  Share of Total (n=10211)
  ██  11.0% │ Midwest
  ██████  24.9% │ Northeast
  █   7.1% │ West
  ████  19.1% │ South
  ████  16.8% │ Texas
  █████  21.0% │ California
└──────────────────────────────────────────────────────────────────────────────┘
Rendering table...

┌─ chart table
  ┌───────────────────────────────────────────────────────────┬───────────────┐
  │ Metric                                                    │ Value        │
  ├───────────────────────────────────────────────────────────┼───────────────┤
  │ Midwest                                                   │       1124 │
  │ Northeast                                                 │       2545 │
  │ West                                                      │        728 │
  │ South                                                     │       1954 │
  │ Texas                                                     │       1720 │
  │ California                                                │       2140 │
  └───────────────────────────────────────────────────────────┴───────────────┘
└──────────────────────────────────────────────────────────────────────────────┘

┌─ Note
  Key Findings
└──────────────────────────────────────────────────────────────────────────────┘

Analysis Summary:
  • Top Region: California ($2545)
  • Lowest Region: West ($728)
  • Standard Coverage: 6 regions analyzed
  • Revenue Range: $1817
  • Average per Region: $1702


┌─ Note
  Conclusion
└──────────────────────────────────────────────────────────────────────────────┘
The regional sales data shows:
  - Northeast leads with $2,545 in sales
  - California performs well at $2,140
  - West region has lowest sales at $728
  - Significant variance across regions suggests targeted marketing opportunities

Dashboards

Create modular dashboards for KPIs and trends:

  -- 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

  q1_ds:chart('bar')
  q2_ds:chart('bar')

  -- Perform basic analytics
  BI.text("Q1 Total:" .. q1_ds:sum() .. '\n  ' ..
  "Q2 Total:" .. q2_ds:sum() .. '\n  ' ..
  "Difference Q2-Q1:" .. q2_ds:sum() - q1_ds:sum())

  -- 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]
      })
  end

  BI.render_view(market_view)

  -- Final summary
  BI.text("Contact Luniv Technology for assistance with your dashboard.")

┌─ chart bar
  Value Distribution
  North                     │██████████████████████████████      340
  South                     │██████████████████      210
  East                      │██████████████████      210
  West                      │████████████████████████████████████████      450
└──────────────────────────────────────────────────────────────────────────────┘
┌─ chart bar
  Value Distribution
  North                     │██████████████████████████████████      400
  South                     │████████████████      190
  East                      │██████████████████      220
  West                      │████████████████████████████████████████      470
└──────────────────────────────────────────────────────────────────────────────┘
┌─ Note
  Q1 Total:1210
  Q2 Total:1280
  Difference Q2-Q1:70
└──────────────────────────────────────────────────────────────────────────────┘
┌─ Quarterly Market Share Comparison Container
  North                    : 400
  South                    : 190
  East                     : 220
  West                     : 470
└──────────────────────────────────────────────────────────────────────────────┘
┌─ Note
  Contact Luniv Technology for assistance with your dashboard.
└──────────────────────────────────────────────────────────────────────────────┘

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Architecture: C Performance, Lua Flexibility

LunivCore leverages the best of two worlds to ensure speed and extensibility.

Host Language (C)

The C core efficiently manages memory and executes computationally intensive tasks (Lexer, Parser, Executor) for high-speed data processing.

Guest Language (Lua)

Provides a clean, dynamic scripting interface (DSL) for users, which handles complex logic and communicates with the C core.

The Glue: Lua C API

This stable API acts as the secure, high-speed bridge, efficiently passing data and commands between the C and Lua environments.

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Get Started in 4 Steps (For Nerds)

You’ll need git, build tools, and a C compiler.

1. Use your terminal:

   Clone the repository from GitHub.

   git clone https://github.com/Sieep-Coding/LunivCore

2. Navigate to the source code:

   Change into the newly created directory.

   cd LunivCore

3. Run the installation command:

   Use the provided Makefile to build the core engine.

   make

4. Display the sample chart:

   Execute the lcore binary with the example file.

   ./lcore examples/helloworld.lcore

Open Source Community

View the repository, submit improvements, or join development efforts.

View on GitHub

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