Open Source DataOps Tools Improve Test Coverage

Test Coverage is enabled by Open Source Tools like DataKitchen’s DataOps Data Quality TestGen, and Shift Down With DataKitchen’s Open Source DataOps Observability https://hubs.ly/Q03Z6_7S0 #dataquality #opensource #dataobservability #dataops #dataengineering

Test Coverage is enabled by Open Source Tools like DataKitchen’s DataOps Data Quality TestGen, and Shift Down With DataKitchen’s Open Source DataOps Observability https://hubs.ly/Q03Z6-WK0 #dataquality #opensource #dataobservability #dataops #dataengineering

New Feature in OpenAlgo v2: History API with Local Database Support The History API now supports fetching data from your local Historify database! What's New:  {   "source": "api" // default - fetch from broker   "source": "db"  // fetch from local DuckDB  } Why it matters:  - Fast - No network latency, instant queries  - No rate limits - Query as much as you need  - Works offline - No broker connection required  - Backtesting ready - Use Historify as your data warehouse Supported Timeframes:  1m, 2m, 3m, 5m, 10m, 15m, 30m, 1h, 2h, 4h, D, W, M, Q, Y How it works:  - Base data: 1m and D stored in DuckDB  - Intraday (5m, 15m, 1h, etc.) → aggregated from 1m  - Higher TF (W, M, Q, Y) → aggregated from D  This opens up custom backtesting with OpenAlgo using Historify as your local data storage!

New Feature in OpenAlgo v2: History API with Local Database Support The History API now supports fetching data from your local Historify database! What's New:  {   "source": "api" // default - fetch from broker   "source": "db"  // fetch from local DuckDB  } Why it matters:  - Fast - No network latency, instant queries  - No rate limits - Query as much as you need  - Works offline - No broker connection required  - Backtesting ready - Use Historify as your data warehouse Supported Timeframes:  1m, 2m, 3m, 5m, 10m, 15m, 30m, 1h, 2h, 4h, D, W, M, Q, Y How it works:  - Base data: 1m and D stored in DuckDB  - Intraday (5m, 15m, 1h, etc.) → aggregated from 1m  - Higher TF (W, M, Q, Y) → aggregated from D  This opens up custom backtesting with OpenAlgo using Historify as your local data storage!

🚀 dplyr vs DuckDB: Performance Depends on Data Size When working in R, we often ask: Should I stick with dplyr, or move to DuckDB for better performance? The answer isn’t either-or—it depends on how much data you’re processing. In this blog, I break down: Why dplyr is faster for small, in-memory datasets Why DuckDB + dplyr scales better for large datasets How query planning, memory management, and execution engines impact performance Practical guidance on choosing the right approach for analytics pipelines and Shiny apps. The key takeaway: 👉 Syntax stays the same, but execution strategy changes with scale. If you work with R, analytics engineering, or data-heavy applications, this comparison can help you make better performance decisions. 📖 Read the full blog: https://lnkd.in/giwrh2cu #RStats #DataEngineering #DuckDB #dplyr #RProgramming

The Data Consultant’s Growth Playbook: Accelerating Client Acquisition and Retention with DataKitchen’s Open Source TestGen https://hubs.ly/Q03_kXqM0 #dataops #dataquality #dataengineering #opensource #dataobservability

The Data Consultant’s Growth Playbook: Accelerating Client Acquisition and Retention with DataKitchen’s Open Source TestGen https://hubs.ly/Q03_kV8H0 #dataops #dataquality #dataengineering #opensource #dataobservability

The Data Consultant’s Growth Playbook: Accelerating Client Acquisition and Retention with DataKitchen’s Open Source TestGen https://hubs.ly/Q03_kRjq0 #dataops #dataquality #dataengineering #opensource #dataobservability

“SELECT ” is expensive. Sometimes painfully so. Most costly warehouse queries I’ve seen weren’t complex—they were careless. As Data Engineers, performance isn’t optional. It’s part of correctness. Three SQL practices I rely on to keep pipelines efficient: Window functions over self-joins Functions like ROW_NUMBER(), LEAD(), and LAG() are usually clearer and more efficient than joining a table to itself. Predicate pushdown Filter as early as possible. Scanning less data almost always matters more than clever logic. CTEs for clarity, not just convenience Readable transformations are easier to debug, optimize, and review—especially months later. Small choices compound quickly at scale. What’s one SQL habit that significantly improved your pipeline performance? #SQL #DataPipelines #PerformanceEngineering #DataEngineering