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In recent years, an increasing number of enterprises began to use data to power decision-making, which yields new demands for data exploration and analytics. As database technologies evolve with each passing day, a variety of online analytical processing (OLAP) engines keep popping up. These OLAP engines have distinctive advantages and are designed to suit varied needs with different tradeoffs, such as data volume, performance, or flexibility.

This article compares two popular open-source engines, Apache Druid, and StarRocks, in several aspects that may interest you the most, including data storage, pre-aggregation, computing network, ease of use, and ease of O&M. It also provides star schema benchmark (SSB) test results to help you understand which scenario favors which more.

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A lot, if not all, of data science projects, require some data visualization front-end to display the results for humans to analyze. Python seems to boast the most potent libraries, but do not lose hope if you’re a Java developer (or if you’re proficient in another language as well). In this post, I will describe how you can benefit from such a data visualization front-end without writing a single line of code.

The Use Case: Changes From Wikipedia

I infer that you are already familiar with Wikipedia. If you are not, Wikipedia is an online encyclopedia curated by the community. In their own words:

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