Felipe Gutierrez - Spring Cloud Data Flow: Native Cloud Orchestration Services for Microservice Applications on Modern Runtimes

Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the books product page, located at www.apress.com/9781484212400 . For more detailed information, please visit http://www.apress.com/source-code .

I would like to express all my gratitude to the Apress team: Steve Anglin for accepting my proposal, Mark Powers for keeping me on track and for his patience, and the rest of the Apress team involved in this project. Thanks to everybody for making this possible.

Thanks to my technical reviewer, Manuel Jordan, for all the details and effort in his reviews, and the entire Spring team for creating this amazing technology.

The digital universe consists of an estimated 44 zettabytes of data. A zettabyte is 1 million petabytes, or 1 billion terabytes, or 1 trillion gigabytes. In 2019, Google processed approximately 3.7 million queries, YouTube recorded 4.5 million viewed videos, and Facebook registered 1 million logins every 60 seconds. Imagine the computer power to process all these requests, data ingestion, and data manipulation. Common sense tells us that the big IT companies use a lot of hardware to preserve data. A lot of storage needs to be incorporated to prevent limits of capacity.

How does an IT company deal with challenges like data overload, rising costs, or skill gaps? In recent years, big IT companies have heavily invested in developing strategies that use enterprise data warehouses (EDW) to serve as central data systems that report, extract, transform, and load (ETL) processes from different sources. Today, both users and devices (thermostats, light bulbs, security cameras, coffee machines, doorbells, seat sensors, etc.) ingest data.

Companies such as Dell, Intel, and Clouderato name a fewwork together to create hardware and storage solutions that help other companies grow and become faster and more scalable.

When we talk about data science , a team of scientists with PhD degrees comes to mind. They probably earn big bucks, and they dont rest because companies depend on them. What is a data scientists actual educational experience?

A few years ago, computing journals revealed that Spark and Scala skyrocketed in companies that wanted to apply data science with the addition of tools such as Hadoop, Kafka, Hive, Pig, Cassandra, D3, and Tableau.

Python has become one of the main programming languages for machine learning techniques, alongside R, Scala, and Java.