전체 91pages 분량으로, 내용이 많은 것은 아니나 data engineering 관련 지식을 정리해 놓은 책입니다. 

도커(Docker)나 REST APIs 등 데이터 사이언티스트가 알아야할 내용도 많이 포함되어 있습니다. 데이터 엔지니어링 책이지만 데이터 사이언티스트도 최소 이정도 지식은 있어야 하지 않을까 (개인적으로)생각합니다.

저자의 github (github.com/andkret/Cookbook)에서 관련 코드도 확인할 수 있습니다.

내용은 다음과 같은데, 

책에 모든 내용이 자세히 담겨 있다기 보다는 관련 링크가 첨부된 형식입니다.

예를 들어 data science@ Uber 링크를 클릭하면, 관련 tech blog나 링크가 첨부되어 있습니다.

많은 분들께 필요한 자료인듯하여 공유합니다:)

기회가 된다면 조금씩 정리해서 공유하도록 하겠습니다.

Contents:

• Introduction
• Basic Engineering Skills
• Advanced Engineering Skills
• Hands On Course‚
• Case Studies
• Best Practices Cloud Platforms
• 130+ Data Sources Data Science
• 1001 Interview Questions
• Recommended Books and Courses

• How To Contribute
• Support What You Like
• Important Links

Full Table Of Contents:

Introduction

• What is this Cookbook
• Data Engineer vs Data Scientist
  • Data Engineer
  • Data Scientist
  • Machine Learning Workflow
  • Machine Learning Model and Data
• My Data Science Platform Blueprint
  • Connect
  • Buffer
  • Processing Framework
  • Store
  • Visualize
• Who Companies Need

Basic Engineering Skills

• Learn To Code
• Get Familiar With Git
• Agile Development
  • Why is agile so important?
  • Agile rules I learned over the years
  • Agile Frameworks
    • Scrum
    • OKR
• Software Engineering Culture
• Learn how a Computer Works
• Data Network Transmission
• Security and Privacy
  • SSL Public and Private Key Certificates
  • JSON Web Tokens
  • GDPR regulations
• Linux
  • OS Basics
  • Shell scripting
  • Cron Jobs
  • Packet Management
• Docker
  • What is Docker and How it Works
    • Don't Mess Up Your System
    • Preconfigured Images
    • Take it With You
    • Kubernetes Container Deployment
    • How to Create Start and Stop a Container
    • Docker Micro Services
    • Kubernetes
    • Why and How To Do Docker Container Orchestration
    • Userful Docker Commands
• The Cloud
  • IaaS vs PaaS vs SaaS
  • AWS Azure IBM Google IBM
  • Cloud vs On-Premises
  • Security
  • Hybrid Clouds
• Security Zone Design
  • How to secure a multi layered application
  • Cluster security with Kerberos

Advanced Engineering Skills

• Data Science Platform
  • Why a Good Data Platform Is Important
  • Big Data vs Data Science and Analytics
  • The 4 Vs of Big Data
  • Why Big Data
    • Planning is Everything
    • The Problem with ETL
    • Scaling Up
    • Scaling Out
    • When not to Do Big Data
• Hadoop Platforms
  • What is Hadoop
  • What makes Hadoop so popular
  • Hadoop Ecosystem Components
  • Hadoop is Everywhere?
  • Should You Learn Hadoop?
  • How to Select Hadoop Cluster Hardware
• Connect
  • REST APIs
    • API Design
    • Implemenation Frameworks
    • Security
  • Apache Nifi
  • Logstash
• Buffer
  • Apache Kafka
    • Why a Message Queue Tool?
    • Kafka Architecture
    • Kafka Topics
    • Kafka and Zookeeper
    • How to Produce and Consume Messages
    • Kafka Commands
  • Apache Redis Pub-Sub
  • AWS Kinesis
  • Google Cloud PubSub
• Processing Frameworks
  • Lambda and Kappa Architecture
  • Batch Processing
  • Stream Processing
    • Three Methods of Streaming
    • At Least Once
    • At Most Once
    • Exactly Once
    • Check The Tools
  • Should You do Stream or Batch Processing
  • Is ETL still relevant for Analytics?
  • MapReduce
    • How Does MapReduce Work
    • MapReduce
    • MapReduce Example
    • MapReduce Limitations
  • Apache Spark
    • What is the Difference to MapReduce?
    • How Spark Fits to Hadoop
    • Spark vs Hadoop
    • Spark and Hadoop a Perfect Fit
    • Spark on YARn
    • My Simple Rule of Thumb
    • Available Languages
    • Spark Driver Executor and SparkContext
    • Spark Batch vs Stream processing
    • How Spark uses Data From Hadoop
    • What are RDDs and How to Use Them
    • SparkSQL How and Why to Use It
    • What are Dataframes and How to Use Them
    • Machine Learning on Spark (TensorFlow)
    • MLlib
    • Spark Setup
    • Spark Resource Management
  • AWS Lambda
  • Apache Flink
  • Elasticsearch
  • Apache Drill
  • StreamSets
• Store
  • Data Warehouse vs Data Lake
  • SQL Databases
    • PostgreSQL DB
    • Database Design
    • SQL Queries
    • Stored Procedures
    • ODBC/JDBC Server Connections
  • NoSQL Stores
    • HBase KeyValue Store
    • HDFS Document Store
    • MongoDB Document Store
    • Elasticsearch Document Store
    • Hive Warehouse
    • Impala
    • Kudu
    • Apache Druid
    • InfluxDB Time Series Database
    • Greenplum MPP Database
• Visualize
  • Android and IOS
  • API Design for Mobile Apps
  • Dashboards
    • Grafana
    • Kibana
  • Webservers
    • Tomcat
    • Jetty
    • NodeRED
    • React
  • Business Intelligence Tools
    • Tableau
    • Power BI
    • Quliksense
  • Identity & Device Management
    • What Is A Digital Twin
    • Active Directory
• Machine Learning
  • How to do Machine Learning in production
  • Why machine learning in production is harder then you think
  • Models Do Not Work Forever
  • Where are The Platforms That Support Machine Learning
  • Training Parameter Management
  • How to Convince People That Machine Learning Works
  • No Rules No Physical Models
  • You Have The Data. Use It!
  • Data is Stronger Than Opinions
  • AWS Sagemaker

Hands On Course

• What We Want To Do
• Thoughts On Choosing A Development Environment
• A Look Into the Twitter API
• Ingesting Tweets with Apache Nifi
• Writing from Nifi to Apache Kafka
• Apache Zeppelin Data Processing
  • Install and Ingest Kafka Topic
  • Processing Messages with Spark & SparkSQL
  • Visualizing Data
• Switch Processing from Zeppelin to Spark

Case Studies

• Data Science @Airbnb
• Data Science @Amazon
• Data Science @Baidu
• Data Science @Blackrock
• Data Science @BMW
• Data Science @Booking.com
• Data Science @CERN
• Data Science @Disney
• Data Science @DLR
• Data Science @Drivetribe
• Data Science @Dropbox
• Data Science @Ebay
• Data Science @Expedia
• Data Science @Facebook
• Data Science @Google
• Data Science @Grammarly
• Data Science @ING Fraud
• Data Science @Instagram
• Data Science @LinkedIn
• Data Science @Lyft
• Data Science @NASA
• Data Science @Netflix
• Data Science @OLX
• Data Science @OTTO
• Data Science @Paypal
• Data Science @Pinterest
• Data Science @Salesforce
• Data Science @Siemens Mindsphere
• Data Science @Slack
• Data Science @Spotify
• Data Science @Symantec
• Data Science @Tinder
• Data Science @Twitter
• Data Science @Uber
• Data Science @Upwork
• Data Science @Woot
• Data Science @Zalando

Best Practices Cloud Platforms

• Amazon Web Services (AWS)
  • Connect
  • Buffer
  • Processing
  • Store
  • Visualize
  • Containerization
  • Best Practices
  • More Details
• Microsoft Azure
  • Connect
  • Buffer
  • Processing
  • Store
  • Visualize
  • Containerization
  • Best Practices
• Google Cloud Platform (GCP)
  • Connect
  • Buffer
  • Processing
  • Store
  • Visualize
  • Containerization
  • Best Practices

130+ Free Data Sources For Data Science

• General And Academic
• Content Marketing
• Crime
• Drugs
• Education
• Entertainment
• Environmental And Weather Data
• Financial And Economic Data
• Government And World
• Health
• Human Rights
• Labor And Employment Data
• Politics
• Retail
• Social
• Travel And Transportation
• Various Portals
• Source Articles and Blog Posts
• Free Data Sources Data Science

1001 Interview Questions

• Interview Questions

Recommended Books and Courses

• About Books and Courses
• Books
  • Languages
    • Java
    • Python
    • Scala
    • Swift
  • Data Science Tools
    • Apache Spark
    • Apache Kafka
    • Apache Hadoop
    • Apache HBase
  • Business
    • The Lean Startup
    • Zero to One
    • The Innovators Dilemma
    • Crossing the Chasm
    • Crush It!
  • Community Recommendations
    • Designing Data-Intensive Applications
• Online Courses
  • Machine Learning Stanford
  • Computer Networking
  • Spring Framework
  • IOS App Development Specialization