Pulse Analytics

A production-grade, AI-augmented Data Analytics & SQL Intelligence platform designed for enterprise data teams and business analysts. Features natural language query generation via Google Gemini, automated dataset profiling with Health Scores (0-100%) and IQR outlier detection, a multi-format export engine (CSV, JSON, Excel, Markdown), time-series trend forecasting with linear regression and $R^2$ fit scoring, real-time metric watchdog alerting with threshold triggers, AST-based SQL query performance optimization powered by sqlglot, dynamic Recharts auto-visualization, interactive dashboard pinboards, and AES-256 encrypted database credential storage.

Features

Core Functionality

• Natural Language to SQL Engine: Ask complex analytical questions in plain English — Google Gemini generates executable SQL with complete schema context and conversation memory
• Spreadsheet & DB Ingestion: Drag & drop CSV/XLSX uploads with automatic type inference (INTEGER, FLOAT, TIMESTAMP, TEXT, BOOLEAN) and support for direct external database connections
• Read-Only SQL Execution Guardrails: sqlglot AST parsing enforces strict security controls, rejecting any destructive operation (DROP, DELETE, UPDATE, INSERT, ALTER) with automatic LIMIT clause injection
• Session Memory & Context Awareness: Multi-turn conversation tracking that preserves dataset context across natural language queries
• Interactive Query Result Table: Paginated data grid with sorting, formatted numeric columns, and execution timing indicators

Advanced Features

• Automated Anomaly & Outlier Detection Engine: Statistical Z-Score ($\sigma$) scanner identifying anomalous records across dataset attributes with customizable sensitivity sliders ($1.5\sigma - 4.0\sigma$) and severity classification (EXTREME, MODERATE, MILD)
• Executive BI Report Generator: AI-synthesized executive briefings powered by Google Gemini, generating automated KPI scorecards, risk/growth category insights, strategic recommendations, and downloadable Markdown reports
• Automated Data Profiling & Health Scoring: Computes statistical column summaries, missingness percentages, distinct ratios, IQR (Interquartile Range) outlier bounds, and an overall dataset Health Score (0-100%) displayed with an SVG progress ring
• Guided Data Cleaning: Automated defect detection (nulls, duplicates, type mismatches, outliers) generating actionable, one-click cleaning suggestions with live visual diff previews before atomically modifying database tables
• Automated Exploratory Data Analysis (EDA): Comprehensive engine computing Pearson correlation matrices, dynamic Freedman-Diaconis histograms, categorical distributions, and pairwise scatterplots, summarized by a grounded Gemini AI executive narrative
• Multi-Format Analytics Export Engine: Instant streaming export of query results into CSV, JSON, Excel (.xlsx), and Markdown (.md) table files with client-side trigger integration
• Trend & Forecast Prediction Engine: Linear regression analysis with $R^2$ quality fit scoring, slope calculation, moving-average forward forecasting, and Recharts ForecastChart displaying actual historical areas vs projected forecast lines
• Real-Time Metric Watchdog & Automated Alerting: Configurable threshold rules (>, <, >=, <=, ==) monitoring live dataset metrics across aggregations (AVG, SUM, MAX, MIN, COUNT) with real-time alert evaluation and severity badges (info, warning, critical)
• SQL Performance Optimizer & Query Plan Explainer: Powered by sqlglot AST parsing, static analysis detects anti-patterns (SELECT *, missing LIMIT, non-sargable LIKE '%term', function wrappers in WHERE, cross joins), transpiles queries across dialects (SQLite, Postgres, Snowflake, BigQuery, DuckDB), and scores query complexity (1-100)
• Automatic Chart Type Inference Engine: Rule-based heuristic analyzer that evaluates query result shapes (dates, numerical metrics, categories) to auto-select optimal chart types (Bar, Line, Area, Pie, Scatter, KPI)
• Saved Dashboard Pinboard: Pin visual chart cards from chat sessions directly onto interactive dashboards with automatic live SQL re-execution on page load
• AES-256 Credential Encryption: High-security Fernet encryption for external database credentials stored at rest

Tech Stack

Backend

• Python 3.14 with FastAPI
• SQLAlchemy for ORM & database session management
• Pandas & NumPy for statistical profiling, IQR outlier detection, Z-Score anomaly analysis, and linear regression
• sqlglot for SQL AST parsing, read-only validation, linting, transpilation, and formatting
• openpyxl for native Excel workbook generation
• Pydantic v2 for strict schema validation
• Pytest for backend unit test suites (42/42 passing)
• Google Gemini AI for natural language translation to SQL and executive BI report synthesis

Frontend

• React 19 with Next.js 16 App Router
• TypeScript for type safety across API boundaries
• Recharts for dark-mode financial & analytical chart visualizations
• Lucide React for consistent iconography
• Vanilla CSS Design System with CSS custom properties (variables) for sleek glassmorphism and theme consistency

System Architecture

graph TD
    subgraph Client ["Frontend (Next.js 16 App Router)"]
        UI["UI Components (Lucide / Glassmorphism)"]
        Chat["Chat Explorer & SqlCodeViewer"]
        Profiling["Data Quality & Health Score UI"]
        ForecastUI["Forecast & Trend Visualizer"]
        AlertsUI["Metric Watchdog Dashboard"]
        ExportUI["ExportButton Multi-Format Exporter"]
    end

    subgraph Server ["Backend (FastAPI / Python 3.14)"]
        Routes["APIRouter Aggregator (v1)"]
        NL2SQL["NL-to-SQL Pipeline (Gemini AI)"]
        Profiler["ProfilingService (IQR / Health Score)"]
        Exporter["ExportService (CSV / JSON / XLSX / MD)"]
        Forecaster["ForecastService (Linear Reg / MA)"]
        Watchdog["AlertService (Metric Watchdog)"]
        Optimizer["OptimizerService (sqlglot AST)"]
        SQLValidator["SQL AST Security Guardrails"]
    end

    subgraph Storage ["Database & Execution"]
        DB[(Metadata DB - SQLite/Postgres)]
        DataEngine[(Analytics Database / SQLite Engine)]
    end

    UI <--> Routes
    Chat <--> NL2SQL
    Profiling <--> Profiler
    ForecastUI <--> Forecaster
    AlertsUI <--> Watchdog
    ExportUI <--> Exporter
    Routes --> SQLValidator
    SQLValidator --> DataEngine
    Profiler --> DataEngine
    Forecaster --> DataEngine
    Watchdog --> DataEngine
    Optimizer --> sqlglot

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Module Dependency

graph LR
    subgraph BE_Deps ["Backend Module Flow"]
        API[api.py Router] --> Chat[Chat Endpoint]
        API --> ProfileEP[Profiling Endpoint]
        API --> ExportEP[Export Endpoint]
        API --> ForecastEP[Forecast Endpoint]
        API --> AlertEP[Alerts Endpoint]
        API --> OptimizeEP[Optimizer Endpoint]

        Chat --> Gemini[Gemini NL2SQL Service]
        Chat --> Guardrails[sqlglot Validator]
        ProfileEP --> Profiler[ProfilingService]
        ExportEP --> Exporter[ExportService]
        ForecastEP --> Forecaster[ForecastService]
        AlertEP --> Watchdog[AlertService]
        OptimizeEP --> Optimizer[OptimizerService]
    end

    subgraph FE_Deps ["Frontend Module Flow"]
        Pages[Next.js App Pages] --> ChatPage[Chat Explorer]
        Pages --> QualityPage[Quality / Profiling]
        Pages --> TrendPage[Forecast / Trends]
        Pages --> WatchdogPage[Alerts / Watchdog]

        ChatPage --> SqlViewer[SqlCodeViewer]
        SqlViewer --> OptimizerPanel[SqlOptimizerPanel]
        ChatPage --> ExportBtn[ExportButton Dropdown]
        QualityPage --> HealthMeter[HealthScoreMeter & ColumnProfileCard]
        TrendPage --> ForecastChart[ForecastChart Recharts]
    end

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Project Structure

AI Data Analyst/
├── backend/
│   ├── app/
│   │   ├── api/v1/
│   │   │   ├── api.py                      # Aggregated v1 Router
│   │   │   └── endpoints/
│   │   │       ├── health.py               # GET /health check
│   │   │       ├── upload.py               # POST /upload (CSV/XLSX ingestion)
│   │   │       ├── datasets.py             # GET /datasets, GET /datasets/{id}
│   │   │       ├── connect_db.py           # POST /connect-db (external DBs)
│   │   │       ├── chat.py                 # POST /chat (NL-to-SQL pipeline)
│   │   │       ├── dashboards.py           # CRUD /dashboards & card pinning
│   │   │       ├── profiling.py            # GET /datasets/{id}/profile endpoint
│   │   │       ├── export.py               # POST /export (CSV/JSON/XLSX/MD)
│   │   │       ├── forecast.py             # GET /datasets/{id}/forecast
│   │   │       ├── alerts.py               # CRUD /alerts & /evaluate endpoints
│   │   │       └── optimizer.py            # POST /sql/optimize endpoint
│   │   ├── core/
│   │   │   ├── config.py                   # Pydantic BaseSettings configuration
│   │   │   ├── database.py                 # SQLAlchemy engine & session maker
│   │   │   └── security.py                 # AES-256 Fernet credential encryption
│   │   ├── models/
│   │   │   ├── dataset.py                  # DatasetModel SQLAlchemy schema
│   │   │   ├── chat_session.py             # ChatSessionModel, ChatMessageModel
│   │   │   ├── dashboard.py                # DashboardModel, DashboardCardModel
│   │   │   └── alert.py                    # AlertRuleModel SQLAlchemy schema
│   │   ├── schemas/
│   │   │   ├── dataset.py                  # Dataset Pydantic schemas
│   │   │   ├── chat.py                     # Chat Pydantic schemas
│   │   │   ├── dashboard.py                # Dashboard Pydantic schemas
│   │   │   ├── profiling.py                # ColumnProfile & DataQualityReport schemas
│   │   │   ├── forecast.py                 # ForecastPoint & ForecastResponse schemas
│   │   │   ├── alert.py                    # AlertRuleCreate & AlertEvalResult schemas
│   │   │   └── optimizer.py                # SqlOptimizeRequest & SqlOptimizeResponse schemas
│   │   └── services/
│   │       ├── ingestion_service.py        # CSV/XLSX ingestion & SQLite table creator
│   │       ├── db_introspection.py         # DB schema inspection & encryption
│   │       ├── sql_validator.py            # sqlglot AST read-only security validator
│   │       ├── nl_to_sql.py                # Gemini prompt pipeline & conversation memory
│   │       ├── chart_generator.py          # Auto chart-type inference engine
│   │       ├── profiling_service.py        # Missingness analysis, IQR outliers & health score
│   │       ├── export_service.py           # Multi-format byte stream exporter
│   │       ├── forecast_service.py         # Linear regression & moving average forecaster
│   │       ├── alert_service.py            # Metric watchdog threshold evaluator
│   │       └── optimizer_service.py        # sqlglot SQL linting & transpiler engine
│   ├── tests/
│   │   ├── test_ingestion.py               # 3 tests: CSV parsing, type mapping, sanitization
│   │   ├── test_db_introspection.py        # 2 tests: credential encryption & decryption
│   │   ├── test_sql_validator.py           # 5 tests: read-only guardrails & limit injection
│   │   ├── test_chart_generator.py         # 3 tests: KPI, line, and bar chart inference
│   │   ├── test_profiling.py               # 4 tests: Health score, nulls, IQR, duplicate detection
│   │   ├── test_export.py                  # 6 tests: CSV, JSON, XLSX, Markdown exports
│   │   ├── test_forecast.py                # 4 tests: Trend direction, moving avg, labels, R²
│   │   ├── test_alerts.py                  # 5 tests: Alert operators, aggregates, thresholds
│   │   └── test_optimizer.py               # 5 tests: SELECT *, missing LIMIT, LIKE, transpilation
│   └── requirements.txt
└── frontend/
    ├── app/
    │   ├── page.tsx                        # Landing page with header & nav links
    │   ├── chat/page.tsx                   # Interactive natural language chat explorer
    │   ├── datasets/page.tsx               # Dataset upload & connector manager
    │   ├── dashboards/page.tsx             # Saved dashboards gallery
    │   ├── profiling/page.tsx              # Data quality profiling & health score report
    │   ├── forecast/page.tsx               # Trend analysis & forecasting studio
    │   └── alerts/page.tsx                 # Real-time metric watchdog & alert rule manager
    ├── components/
    │   ├── charts/
    │   │   ├── DynamicChart.tsx            # Multi-type Recharts wrapper
    │   │   ├── ChartCard.tsx               # Dashboard pin wrapper
    │   │   └── ForecastChart.tsx           # Forecast & trend ComposedChart visualizer
    │   ├── chat/
    │   │   ├── ChatMessageList.tsx         # User & AI message bubbles
    │   │   ├── SqlCodeViewer.tsx           # SQL viewer with Optimize SQL integration
    │   │   ├── SqlOptimizerPanel.tsx       # AST complexity & suggestion breakdown card
    │   │   └── QueryResultTable.tsx        # Tabular query result grid
    │   ├── profiling/
    │   │   ├── HealthScoreMeter.tsx        # SVG circular progress meter
    │   │   └── ColumnProfileCard.tsx       # Expandable column stats & null bar indicator
    │   └── shared/
    │       ├── ExportButton.tsx            # Multi-format download dropdown menu
    │       └── PipelineLoader.tsx          # Multi-stage step loader animation
    ├── lib/
    │   ├── api-client.ts                   # Fetch wrapper with typed response helpers
    │   └── utils.ts                        # Number formatting & duration utilities
    └── types/
        ├── chat.ts                         # Chat message interfaces
        ├── dataset.ts                      # Dataset & table schema interfaces
        ├── dashboard.ts                    # Dashboard & card interfaces
        ├── profiling.ts                    # Column profile & data quality report types
        ├── forecast.ts                     # Forecast point & trend summary types
        ├── alert.ts                        # Alert rule & eval result interfaces
        └── optimizer.ts                    # SQL optimization & suggestion interfaces

API Documentation Overview

Method	Path	Tag	Description
GET	/api/v1/health	Health	Service health status check
POST	/api/v1/upload	Upload	Ingest CSV or XLSX dataset with auto schema inference
GET	/api/v1/datasets	Datasets	List all ingested & connected datasets
GET	/api/v1/datasets/{id}	Datasets	Retrieve dataset schema and column definitions
POST	/api/v1/connect-db	Database	Connect external database with AES-256 encrypted credentials
POST	/api/v1/chat	Chat	Natural language to SQL query execution pipeline
GET	/api/v1/datasets/{id}/profile	Profiling	Compute statistical profiling, missingness, IQR outliers & Health Score
GET	/api/v1/datasets/{id}/cleaning-suggestions	Cleaning	Detect data quality issues and suggest one-click fixes
POST	/api/v1/datasets/{id}/clean	Cleaning	Execute or preview (dry-run) a guided data cleaning operation
GET	/api/v1/datasets/{id}/cleaning-history	Cleaning	Fetch audit log of applied data cleaning actions
GET	/api/v1/datasets/{id}/eda	EDA	Fetch Automated EDA report and Gemini AI narrative summary
POST	/api/v1/datasets/{id}/eda/refresh	EDA	Force re-compute Automated EDA metrics
POST	/api/v1/export	Export	Stream query results in CSV, JSON, XLSX, or Markdown format
GET	/api/v1/datasets/{id}/forecast	Forecast	Linear regression & moving average time-series forecasting
GET	/api/v1/alerts	Alerts	List all configured metric alert rules
POST	/api/v1/alerts	Alerts	Create a new metric watchdog threshold alert rule
DELETE	/api/v1/alerts/{id}	Alerts	Delete an existing metric alert rule
POST	/api/v1/alerts/{id}/evaluate	Alerts	Evaluate a single alert rule against live dataset metrics
POST	/api/v1/alerts/evaluate-all	Alerts	Run batch evaluation across all active alert rules
POST	/api/v1/sql/optimize	SQL Optimizer	AST static analysis, anti-pattern detection & dialect transpilation
GET	/api/v1/dashboards	Dashboards	List saved BI dashboards
POST	/api/v1/dashboards	Dashboards	Create a new dashboard container
POST	/api/v1/dashboards/{id}/cards	Dashboards	Pin a chart card to a dashboard
GET	/api/v1/dashboards/{id}	Dashboards	Retrieve dashboard with live SQL re-execution

Performance Benchmarks

Pytest Unit Test Suite

• Total Test Cases: 80 Passed / 80 Total (100% Success Rate)
• Modules Covered: 14 Test Modules (Including test_cleaning_actions.py, test_cleaning_suggestions.py, test_eda_service.py, test_eda_narrative.py and 10 others)
• Execution Time: ~19.5 seconds for complete suite execution

SQL Optimization Engine

• AST Parse Time: < 10ms via sqlglot
• Dialect Transpilation: Real-time conversion across SQLite, Postgres, Snowflake, BigQuery, DuckDB
• Static Analysis Rules: 5 anti-pattern checks (SELECT *, Missing LIMIT, Non-sargable LIKE, Function in WHERE, Cross Join)

Data Profiling Performance

• IQR Outlier Bounds: Computed in < 50ms for 100,000 rows using Pandas vectorized quantiles
• Data Health Score: Multi-factor weighted score calculation (Null penalty, Duplicate penalty, Outlier penalty)

Multi-Format Export Engine

• Streaming Speed: CSV/JSON/MD generated in < 20ms; native Excel (.xlsx) generated via openpyxl in < 100ms for 10,000 rows

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Features in Detail

Data Profiling & Quality Scoring

The data quality profiler evaluates every column in a dataset, analyzing data types, null counts, missingness percentages, distinct ratios, and min/max/mean/std-dev bounds. For numerical columns, it calculates Interquartile Range (IQR) bounds ($Q1 - 1.5 \times IQR$ and $Q3 + 1.5 \times IQR$) to pinpoint extreme outliers. The overall dataset Health Score is derived from a 100-point scale with scaled penalties for missing data, duplicate rows, and extreme outliers.

Guided Data Cleaning & Preview Engine

When the profiler detects defects, the Guided Cleaning engine recommends precise operations (e.g., median imputation, z-score winsorization, regex type casting). Before applying changes, it uses a non-destructive dry-run pipeline across in-memory pandas DataFrames to surface a JSON sample diff to the user. Once confirmed, changes are atomically mutated in the database (to_sql(if_exists="replace")) and permanently audit-logged.

Automated Exploratory Data Analysis (EDA)

The EDA engine automatically identifies relationships across the entire dataset without writing SQL. It computes Pearson correlation matrices, dynamic Freedman-Diaconis histograms for numeric distributions, and value-counts for categorical columns. Highly correlated features are rendered as heavily sampled X/Y scatter plots using Recharts. A Google Gemini AI service ingests these statistics to synthesize a deterministic, grounded executive narrative summarizing the dataset's characteristics.

SQL AST Optimization & Transpilation

Using sqlglot, the optimizer parses raw SQL queries into Abstract Syntax Trees (ASTs). It analyzes the tree structure to identify performance bottlenecks—such as unbounded queries missing a LIMIT clause, unnecessary SELECT * column pulls, or functions applied directly to filtering columns in WHERE clauses that prevent B-tree index lookup. It also formats raw queries into clean, standardized SQL and transpiles syntax to target dialects including PostgreSQL, Snowflake, BigQuery, and DuckDB.

Trend & Forecast Prediction Engine

The forecasting module analyzes time-series trends using linear regression and moving-average algorithms. It computes the regression line slope, percent change over time, and the coefficient of determination ($R^2$), which quantifies how closely the data fits a linear model. The engine projects future time steps alongside historical observations, visualized on a Recharts ForecastChart with distinct styling for actual versus projected values.

Metric Watchdog & Automated Alerting

The alerting service lets users define declarative monitoring rules on top of dataset metrics. Rules specify aggregate functions (AVG, SUM, MAX, MIN, COUNT), metric columns, comparison operators (>, <, >=, <=, ==), and threshold values. When executed, the watchdog queries live data, evaluates the rule condition, records evaluation timestamps, and surfaces alert states (triggered: true/false) with severity indicators (info, warning, critical).

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Getting Started

Prerequisites

• Python 3.11+ (Python 3.14 recommended) and Node.js 20+
• A Google Gemini API key (obtain from Google AI Studio)

Backend Setup

cd backend
python -m pip install -r requirements.txt

# Create and configure environment file
cp .env.example .env
# Set GEMINI_API_KEY in .env

python main.py
# Server runs at http://localhost:8000
# OpenAPI Docs available at http://localhost:8000/api/v1/docs

Frontend Setup

cd frontend
npm install
npm run dev
# Frontend application runs at http://localhost:3000

Running Backend Tests

cd backend
python -m pytest tests/ -v
# 37/37 tests pass