Data analytics showcase

Market Intelligence

Market data from multiple sources, aggregated automatically, analysed by AI, and surfaced in a live dashboard. Trend signals and anomalies detected before they show up in a weekly report that is already outdated.

A PostgreSQL-backed data pipeline feeds a Streamlit analytics dashboard. An AI layer runs pattern detection on the aggregated data — identifying trends, flagging anomalies, and generating daily digests. The whole system updates automatically without manual data assembly.

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Data freshness

Live

Dashboards update on each pipeline run

Signal coverage

800+

Data points tracked across configured sources

Reporting time

-90%

vs. manual weekly report assembly

Business framing

Why this mattered

The team was assembling market reports manually — pulling data from multiple sources, copying it into spreadsheets, and summarising it by hand every week. By the time the report was ready, the most time-sensitive signals were already stale. The system replaced that manual cycle with an automated pipeline that is always current and surfaces what matters without requiring anyone to go looking for it.

Observed pain

Weekly manual reports were outdated the moment they were finished.
Important market signals were missed because no one was watching the data continuously.
Time spent on data assembly left less time for actual analysis and decision-making.

Slide-like narrative, without the PowerPoint perfume

This page is built to read like a guided walkthrough: each block shows the business reason, the system move, and the operational implication. Future case pages should follow the same spine.

Slide 01

Automated data aggregation from multiple sources

Scheduled jobs pull data from configured market feeds into PostgreSQL on a regular interval. Each source has a defined schema mapping so data lands in normalised tables regardless of the source format. New data sources can be added by defining a new feed connector without touching the rest of the pipeline.

Scheduled ingestion — configurable per source, from minutes to daily
Schema normalisation — all sources land in a consistent table structure
Deduplication and gap detection built into the ingestion layer

Slide 02

AI identifies what the numbers mean

A language model runs over the aggregated data and produces structured signals: which metrics moved significantly, which movements are anomalies versus trend continuations, and which combinations of signals suggest a pattern worth attention. The AI layer adds interpretation on top of the raw numbers without replacing the analyst.

Trend detection: identifies directional movements over configurable windows
Anomaly flagging: surfaces values that deviate from expected range
Daily digest: narrative summary of the most significant signals

Slide 03

Live dashboard and automated reporting

The Streamlit dashboard renders live charts, metric tables, and the AI digest on each page load. Alerts are triggered for high-priority signals and delivered via email or messaging. The team sees a current picture of the market without having to pull, clean, or format any data manually.

Live charts update automatically on each dashboard load
Alert thresholds configurable per metric
Daily digest delivered to email or Slack on schedule

Workflow anatomy

Operationally, this is a Microsoft-native decision pipeline. Each stage is small enough to inspect, yet together they turn a mailbox into a governed queue.

01 · Ingest

Scheduled jobs pull market data

Feed connectors run on a cron schedule, fetching data from each configured source. Raw data is written to staging tables in PostgreSQL. Failed fetches are logged and retried on the next cycle without losing the previous successful pull.

02 · Transform

Pipeline normalises and aggregates data

A transformation layer reads from staging tables, applies deduplication, normalises units and formats, and writes clean data to analysis-ready tables. Rolling windows, moving averages, and comparison periods are calculated and stored as materialised views.

03 · Analyse

AI layer runs pattern detection

The AI analysis job reads from the processed tables, runs trend and anomaly detection, and generates the daily digest text. Signals above configured thresholds are written to an alerts table. The entire analysis cycle runs automatically after each pipeline completion.

04 · Surface

Dashboard and alerts delivered

The Streamlit app reads live from PostgreSQL and renders charts, metric cards, and the latest AI digest. Alert notifications are dispatched via the configured channel. The team starts each day with a current market picture already assembled.

Business impact

What changed for operations

Market monitoring shifts from reactive weekly reviews to continuous automated tracking — signals are visible the moment they appear in the data.
Analyst time redirected from data assembly to acting on signals — the pipeline replaces the manual reporting cycle entirely.
Decision-makers receive a daily digest with AI-curated highlights instead of a raw data dump that requires interpretation.

Architecture note

Routing logic in plain English

Market feeds → scheduled ingestion jobs → PostgreSQL staging → transformation pipeline → analysis tables → AI analysis job → Streamlit dashboard + alert delivery
Each pipeline stage is independently schedulable and observable — failures in one stage do not block others from running on their next cycle.
The Streamlit dashboard is stateless; all data lives in PostgreSQL, making the dashboard easy to redeploy or replace without data migration.

Microsoft stack in play

The point is not tool worship. The point is to use what the business already has, then make it behave like a coherent system instead of a collection of tabs.

PostgreSQL

The central data store. Holds raw ingested data, normalised analysis tables, materialised views for aggregations, and the alerts log.

Python data pipeline

Feed connectors, transformation jobs, and the analysis runner. Scheduled via cron. Each component is independently testable and replaceable.

Streamlit

The dashboard front-end. Reads live from PostgreSQL and renders charts, metric tables, and the AI digest. No separate web server required.

AI analysis layer (LLM)

Runs trend detection, anomaly flagging, and daily digest generation over the aggregated dataset. Output is structured for downstream consumption.

Alert delivery (email / Slack)

Sends threshold-triggered notifications and the daily digest to configured recipients. Supports per-metric alert configuration.

Reusable pattern

Data-first intelligence

This showcase represents the practical end of the data analytics spectrum: not a complex ML platform, but a well-structured pipeline that gets market data into a usable form and applies AI where it adds genuine interpretive value.

Next step

If your queue looks similar, the pattern is portable

Support inboxes, sales qualification, service requests, approvals, or mixed internal mailboxes — the same design principle applies: classify intent, route with confidence, and keep exceptions visible.

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