Until a decade ago, the database world was all SQL, distributed, sometimes replicated, and fully consistent. Then, web and cloud applications emerged that need to deal with complex big data, and NoSQL came in to address their requirements, trading consistency for scalability and availability. NewSQL has been the latest technology in the big data management landscape, combining the scalability and availability of NoSQL with the consistency and usability of SQL. By blending capabilities only available in different kinds of database systems such as fast data ingestion and SQL queries and by providing online analytics over operational data, NewSQL opens up new opportunities in many application domains where real-time decisions are critical. NewSQL may also simplify data management, by removing the traditional separation between NoSQL and SQL (ingest data fast, query it with SQL), as well as between operational database and data warehouse / data lake (no more ETLs!). However, a hard problem is scaling out transactions in mixed operational and analytical (HTAP) workloads over big data, possibly coming from different data stores (HDFS, SQL, NoSQL). Today, only a few NewSQL systems have solved this problem. In this paper, we make the case for NewSQL, introducing their basic principles from distributed database systems and illustrating with Spanner and LeanXcale, two of the most advanced systems in terms of scalable transaction management.