Unicode is a subject that trips up even experienced programmers. It’s one of those places where computer science and engineering bump hard into human diversity.
I want my big-data applications to run as fast as possible. So why do the engineers who designed Hadoop specify “commodity hardware” for Hadoop clusters? Why go out of your way to tell people to run on mediocre machines?
This is part two of an extended article. See part one here.
A full listing of Hive best practices and optimization would fill a book. All we’ll do here is skim over the topics that best indicate the spirit of Hive, and how it is used most successfully. There’s plenty of detail available in the documentation and on the Web at large. Hopefully, these quick run-downs will provide enough background and keywords for a rewarding Google search.
SQL is the lingua-franca of data big and small, but SQL is a language, not a platform—it serves as the conceptual framework for data tasks on many platforms, ranging from blog content management with MySQL, to high-frequency online transaction processing (OLTP) systems, to heavy-duty batch processing on Hadoop and other big-data platforms.