In , big topics ruled in the of big and analytics.

Whether the subject at hand was something relatively new, such as incorporating machine learning into working with data or monetization, or longstanding concerns, such as choosing the best possible data model or how to make the most of data science skills, readers of the IBM Big Data & Analytics Hub were hungry for knowledge this year. They voraciously read posts about those topics, as well as about working with open source frameworks such as Hadoop and Spark. They were also quite interested in how NASA uses big data in its operations.

Here are the top blog posts of the year:

10. How to build an all-purpose big data engine with Hadoop and Spark

9. Big data at NASA

8. Incorporating machine learning in the data lake for robust business results

7. 5 key attributes of effective data monetization strategy

6. 9 experts answer your top data science & machine learning questions

. Cyber security powered by AI and machine learning

4. Charting the data lake: Rethinking data models for data lakes

3. How does machine learning work?

2. Learning machine learning? Six article you don’t want to miss

1. The quant crunch: The demand for data science skills

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