The rise of dating apps in the last decade has changed the way people forge relationships, with Pew research conducted in 2022 finding that 53% of US adults under 30 had used online dating. But dating apps have caused dissatisfaction and despair among many users, as Pew found 46% of all users (and 51% of women) had a negative experience of online dating. Some dating companies have faced business struggles recently, with shares in Bumble crashing by 30% last month after a bad earnings report and Match Group this year announcing an 8% slump in paying Tinder users and cuts to 6% of its global workforce. The Guardian asked people to share why they had chosen to ditch dating apps and forge connections in other ways. I've been single for about 12 years, and was on the apps since they arrived.

The book provides a super fast, short introduction to Spark in the first chapter and then jump straight into MLlib, Spark Streaming Spark SQL, GraphX, etc. in subsequent chapters. A huge positive for this book is that it not only talks about Spark itself, but also covers using Spark with other big data technologies like Hadoop, Kafka, Titan, Neo4j, HBase, Cassandra, H2O, etc. True to the name, sure the book covers more than simple introductory Spark topics, but it concentrates on breath than depth. There is decent coverage and enough code examples for each topic, but what it lacks is depth. There is no "best practices" or "performance" or "watch out for" type discussions or any type of advanced code. The MLlib chapter covers Naive Bayes, K-Means and Artificial Neural Networks (ANN).