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# Best of OLAP — 2025

1. 1  
[](/posts/duckdb-in-100-seconds-c59vgegy8 "DuckDB in 100 Seconds")  
Video  
![Avatar of fireship](https://media.daily.dev/image/upload/s--75ndqrkr--/f_auto,t_logo/v1702882094/logos/fireship.jpg)Fireship · 1y  
DuckDB in 100 Seconds  
DuckDB is an open-source, embeddable SQL database optimized for analytical workloads through columnar storage. Unlike SQLite's row-based approach, DuckDB stores data column-wise, enabling faster aggregations, filters, and joins on large datasets. It features vectorized query execution, multi-threading, and can directly query CSV and Parquet files. The database excels at time series analysis and is already used by major companies like Meta, Google, and Airbnb.  
65  
1
2. 2  
[](/posts/introduction-to-apache-kylin-vn61dev56 "Introduction to Apache Kylin")  
Article  
![Avatar of baeldung](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/52cccfb454d946c4b7dfa5e816f9e576)Baeldung · 1y  
Introduction to Apache Kylin  
Apache Kylin is an open-source OLAP engine designed for sub-second query performance on massive datasets. Initially developed by eBay and later managed by the Apache Software Foundation, it excels in handling high concurrency and integrates seamlessly with Hadoop and data lake platforms. Key features include multidimensional modeling, optimized indexing, and support for both batch and streaming data sources. The platform can be easily explored using Docker, allowing for straightforward setup, model creation, and CUBE building via SQL and REST API.  
63
3. 3  
[](/posts/scaling-muse-how-netflix-powers-data-driven-creative-insights-at-trillion-row-scale-serm8slgm "Scaling Muse: How Netflix Powers Data-Driven Creative Insights at Trillion-Row Scale")  
Article  
![Avatar of netflix](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/netflix)Netflix TechBlog · 1y  
Scaling Muse: How Netflix Powers Data-Driven Creative Insights at Trillion-Row Scale  
Netflix evolved their Muse analytics platform to handle trillion-row datasets by implementing HyperLogLog sketches for approximate distinct counts, using Hollow for in-memory precomputed aggregates, and extensively tuning their Apache Druid cluster. The migration reduced query latencies by 50% while supporting advanced filtering capabilities for creative content insights. The team used parallel stack deployment, automated validation, and granular feature flags to ensure data accuracy during the transition.  
23

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