Slack wanted to stay independent. But in a landscape dominated by tech giants, that proved impossible.

Author : Pjmdef21

Publish Date : 2021-01-08 14:13:45

Slack wanted to stay independent. But in a landscape dominated by tech giants, that proved impossible.

I devise 2 factors that we can use when we draw the chart and locate each book on it.
One is about a ‘Technical Conceptuality — Practicality’ that means whether it deals with general implementation concept or specific implementation(or API), and the other one is ‘Generality vs Data Contextuality’.
Here I plot the chart following the two factors:

Designing Data-Intensive Applications by Martin Kleppmann: It delivers core concepts of the data system like data model, distributed system(e.g. two-phase lock), and batch & streaming of the data processing.
(3) Rebuilding Reliable Data Pipelines Through Modern Tools by Ted Malaska: If most of your experience is out of the data things, this book would be a good choice to start to understand what’s going on in the data field. Things like stakeholders in a data environment, data pipelining, common issues(lots of them are relatively data-

Expert Hadoop Administration by Sam R. Alapati: There’s also a good Oreilly book on Hadoop, but I choose it because I actually re-read it again and again for the last 1 year whenever I need some thorough answer(What kind of configurations do I need for the server of HDFS Namenode? or Where should I check to monitor HDFS ?).
(5) Architecting Modern Data Platform by Jan Kunigk, Ian Buss, Paul Wilkinson, Lars George: A good book with fantastic graphs and images. Compared to (4), it more focuses on the external Hadoop services(Server RAM, CPU specifications, or Network Band Requirements, etc).
Main Contents of each book
Some are short, but some are demanding to start. So, I share some thoughts on what was influential in each one to

Author Jay Kreps, who is one of the developers of Kafka and Samza, says that the logs, that we usually perceive in the form of a web server like Nginx, takes the central role in the database and distributed system and it has many benefits in log-centric designs and consensus compared to other alternatives.
And, he addresses some practical examples: ‘Data Integration’, ‘Real-Time data processing’, and ‘Distributed System Design’.

It also gives you a perspective rather than ‘how to’ on Hadoop with the Lambda Architecture.
Personally, I frequently go back to this and remind myself whenever I feel conceptually-polluted.
Rebuilding Reliable Data Pipelines Through Modern Tools (~ 100 Pages) to lie wor.

This book, which is free on the Unravel site, teaches you who are the stakeholders in the data environment and what the landscape of data ETL(Extract, Transform, Load) looks like.
It uses many simple metaphors but practical enough to make you ‘feel’ how it would be like to work as a data engineer in the environment described in the book.
There’s a comprehensive book written by the same author, Ted Malaska, but I think this concise book would be sufficient for your knowledge base and then you could find your way by googling.

For the professionals who struggle with Hadoop services, it is hard to find valuable resources to solve practical problems including HDFS, Yarn, Oozie, Sqoop, etc.

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