Real-Time SQL to Parquet Sync for Power BI
TL;DR
Incremental data sync tool for data engineers at mid-sized companies using SQL Server + Power BI that automatically syncs queries/views to Parquet files in 5–10 min (tracking changes via metadata) so they cut manual export time by 5+ hours/week and ensure fresh Power BI data.
Target Audience
Data engineers and analytics teams at mid-sized companies using SQL Server + Power BI, who need reliable real-time data exports but struggle with CDC limitations.
The Problem
Problem Context
Data engineers need to share near-real-time query results from SQL Server to downstream users (e.g., Power BI) in Parquet format. Their current workflow relies on Change Data Capture (CDC), but CDC fails for views without datetime columns, forcing manual workarounds that break incremental loading.
Pain Points
CDC doesn’t work with views lacking datetime columns, making incremental updates impossible. Manual exports waste 5+ hours/week. Downstream users (Power BI) can’t access fresh data, delaying analytics. Failed attempts include hiring consultants or duct-taping scripts together.
Impact
Delayed analytics cost teams lost revenue opportunities and wasted engineering time. Downstream users (e.g., marketers, analysts) can’t trust stale data, leading to poor decisions. The team’s credibility suffers when they can’t deliver reliable data pipelines.
Urgency
This is a blocking issue for teams relying on real-time analytics. Without a fix, the team can’t scale their data workflows or meet SLAs for downstream users. The problem worsens as data volume grows, making manual fixes unsustainable.
Target Audience
Data engineers, analytics teams, and BI developers at mid-sized companies using SQL Server + Power BI. Also affects freelance consultants and startups with similar stack constraints.
Proposed AI Solution
Solution Approach
A lightweight SaaS that automatically syncs SQL Server query results to Parquet files (or DuckDB) in near-real-time (5–10 min latency). It bypasses CDC limitations by using a custom incremental logic engine that works with views, even without datetime columns. Exports are optimized for Power BI consumption.
Key Features
- Parquet Export: Generates optimized Parquet files for Power BI with minimal storage overhead.
- Scheduling & Alerts: Lets users set sync intervals (e.g., every 5 min) and get Slack/email alerts on failures.
- Self-Hosted Option: Deployable via Docker for teams with strict data residency needs.
User Experience
Users input their SQL query (or view) and configure sync settings (e.g., 5-min intervals). The tool runs in the background, exporting fresh Parquet files to a shared folder or cloud storage. Downstream users (Power BI) import the files automatically. Failures trigger alerts, and a dashboard shows sync history.
Differentiation
Unlike CDC, this works with views and doesn’t require datetime columns. Unlike manual scripts, it’s reliable and scalable. Unlike ETL tools, it’s affordable ($50–$100/mo) and focused on this exact use case. The self-hosted option addresses data privacy concerns.
Scalability
Starts with SQL Server + Parquet, then adds connectors (e.g., Snowflake, BigQuery) and advanced features (e.g., data transformation rules). Pricing scales with usage (e.g., seats or query complexity), allowing growth from small teams to enterprises.
Expected Impact
Teams save 5+ hours/week on manual exports. Downstream users get fresh data for Power BI, improving decision-making. The tool restores trust in the data pipeline and enables scaling without hiring more engineers.