Architecting Modern Data Platforms 1st edition by Lars George, Paul Wilkinson, Ian Buss, Jan Kunigk ISBN 149196927X 978-1491969274

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ISBN 10: 149196927X
ISBN 13: 978-1491969274
Author: Lars George, Paul Wilkinson, Ian Buss, Jan Kunigk

There’s a lot of information about big data technologies, but splicing these technologies into an end-to-end enterprise data platform is a daunting task not widely covered. With this practical book, you’ll learn how to build big data infrastructure both on-premises and in the cloud and successfully architect a modern data platform.

Ideal for enterprise architects, IT managers, application architects, and data engineers, this book shows you how to overcome the many challenges that emerge during Hadoop projects. You’ll explore the vast landscape of tools available in the Hadoop and big data realm in a thorough technical primer before diving into:

• Infrastructure: Look at all component layers in a modern data platform, from the server to the data center, to establish a solid foundation for data in your enterprise
• Platform: Understand aspects of deployment, operation, security, high availability, and disaster recovery, along with everything you need to know to integrate your platform with the rest of your enterprise IT
• Taking Hadoop to the cloud: Learn the important architectural aspects of running a big data platform in the cloud while maintaining enterprise security and high availability

Architecting Modern Data Platforms 1st Table of contents:

1. Big Data Technology Primer

A Tour of the Landscape

Core Components

Computational Frameworks

Analytical SQL Engines

Storage Engines

Ingestion

Orchestration

Summary

I. Infrastructure

2. Clusters

Reasons for Multiple Clusters

Multiple Clusters for Resiliency

Multiple Clusters for Software Development

Multiple Clusters for Workload Isolation

Multiple Clusters for Legal Separation

Multiple Clusters and Independent Storage and Compute

Multitenancy

Requirements for Multitenancy

Sizing Clusters

Sizing by Storage

Sizing by Ingest Rate

Sizing by Workload

Cluster Growth

The Drivers of Cluster Growth

Implementing Cluster Growth

Data Replication

Replication for Software Development

Replication and Workload Isolation

Summary

3. Compute and Storage

Computer Architecture for Hadoop

Commodity Servers

Server CPUs and RAM

Nonuniform Memory Access

CPU Specifications

RAM

Commoditized Storage Meets the Enterprise

Modularity of Compute and Storage

Everything Is Java

Replication or Erasure Coding?

Alternatives

Hadoop and the Linux Storage Stack

User Space

Important System Calls

The Linux Page Cache

Short-Circuit and Zero-Copy Reads

Filesystems

Erasure Coding Versus Replication

Discussion

Guidance

Low-Level Storage

Storage Controllers

Disk Layer

Server Form Factors

Form Factor Comparison

Guidance

Workload Profiles

Cluster Configurations and Node Types

Master Nodes

Worker Nodes

Utility Nodes

Edge Nodes

Small Cluster Configurations

Medium Cluster Configurations

Large Cluster Configurations

Summary

4. Networking

How Services Use a Network

Remote Procedure Calls (RPCs)

Data Transfers

Monitoring

Backup

Consensus

Network Architectures

Small Cluster Architectures

Medium Cluster Architectures

Large Cluster Architectures

Network Integration

Reusing an Existing Network

Creating an Additional Network

Network Design Considerations

Layer 1 Recommendations

Layer 2 Recommendations

Layer 3 Recommendations

Summary

5. Organizational Challenges

Who Runs It?

Is It Infrastructure, Middleware, or an Application?

Case Study: A Typical Business Intelligence Project

The Traditional Approach

Typical Team Setup

Compartmentalization of IT

Revised Team Setup for Hadoop in the Enterprise

Solution Overview with Hadoop

New Team Setup

Split Responsibilities

Do I Need DevOps?

Do I Need a Center of Excellence/Competence?

Summary

6. Datacenter Considerations

Why Does It Matter ?

Basic Datacenter Concepts

Cooling

Power

Network

Rack Awareness and Rack Failures

Failure Domain Alignment

Space and Racking Constraints

Ingest and Intercluster Connectivity

Software

Hardware

Replacements and Repair

Operational Procedures

Typical Pitfalls

Networking

Cluster Spanning

Summary

II. Platform

7. Provisioning Clusters

Operating Systems

OS Choices

OS Configuration for Hadoop

Automated Configuration Example

Service Databases

Required Databases

Database Integration Options

Database Considerations

Hadoop Deployment

Hadoop Distributions

Installation Choices

Distribution Architecture

Installation Process

Summary

8. Platform Validation

Testing Methodology

Useful Tools

Hardware Validation

CPU

Disks

Network

Hadoop Validation

HDFS Validation

General Validation

Validating Other Components

Operations Validation

Summary

9. Security

In-Flight Encryption

TLS Encryption

SASL Quality of Protection

Enabling in-Flight Encryption

Authentication

Kerberos

LDAP Authentication

Delegation Tokens

Impersonation

Authorization

Group Resolution

Superusers and Supergroups

Hadoop Service Level Authorization

Centralized Security Management

HDFS

YARN

ZooKeeper

Hive

Impala

HBase

Solr

Kudu

Oozie

Hue

Kafka

Sentry

At-Rest Encryption

Volume Encryption with Cloudera Navigator Encrypt and Key Trustee Server

HDFS Transparent Data Encryption

Encrypting Temporary Files

Summary

10. Integration with Identity Management Providers

Integration Areas

Integration Scenarios

Scenario 1: Writing a File to HDFS

Scenario 2: Submitting a Hive Query

Scenario 3: Running a Spark Job

Integration Providers

LDAP Integration

Background

LDAP Security

Load Balancing

Application Integration

Linux Integration

Kerberos Integration

Kerberos Clients

KDC Integration

Certificate Management

Signing Certificates

Converting Certificates

Wildcard Certificates

Automation

Summary

11. Accessing and Interacting with Clusters

Access Mechanisms

Programmatic Access

Command-Line Access

Web UIs

Access Topologies

Interaction Patterns

Proxy Access

Load Balancing

Edge Node Interactions

Access Security

Administration Gateways

Workbenches

Hue

Notebooks

Landing Zones

Summary

12. High Availability

High Availability Defined

Lateral/Service HA

Vertical/Systemic HA

Measuring Availability

Percentages

Percentiles

Operating for HA

Monitoring

Playbooks and Postmortems

HA Building Blocks

Quorums

Load Balancing

Database HA

Ancillary Services

General Considerations

Separation of Master and Worker Processes

Separation of Identical Service Roles

Master Servers in Separate Failure Domains

Balanced Master Configurations

Optimized Server Configurations

High Availability of Cluster Services

ZooKeeper

HDFS

YARN

HBase

KMS

Hive

Impala

Solr

Kafka

Oozie

Hue

Other Services

Autoconfiguration

Summary

13. Backup and Disaster Recovery

Context

Many Distributed Systems

Policies and Objectives

Failure Scenarios

Suitable Data Sources

Strategies

Data Types

Consistency

Validation

Summary

Data Replication

HBase

Cluster Management Tools

Kafka

Summary

Hadoop Cluster Backups

Subsystems

Case Study: Automating Backups with Oozie

Restore

Summary

III. Taking Hadoop to the Cloud

14. Basics of Virtualization for Hadoop

Compute Virtualization

Virtual Machine Distribution

Anti-Affinity Groups

Storage Virtualization

Virtualizing Local Storage

SANs

Object Storage and Network-Attached Storage

Network Virtualization

Cluster Life Cycle Models

Summary

15. Solutions for Private Clouds

OpenStack

Automation and Integration

Life Cycle and Storage

Isolation

Summary

OpenShift

Automation

Life Cycle and Storage

Isolation

Summary

VMware and Pivotal Cloud Foundry

Do It Yourself?

Automation

Isolation

Life Cycle Model

Summary

Object Storage for Private Clouds

EMC Isilon

Ceph

Summary

16. Solutions in the Public Cloud

Key Things to Know

Cloud Providers

AWS

Microsoft Azure

Google Cloud Platform

Implementing Clusters

Instances

Storage and Life Cycle Models

Network Architecture

High Availability

Summary

17. Automated Provisioning

Long-Lived Clusters

Configuration and Templating

Deployment Phases

Vendor Solutions

One-Click Deployments

Homegrown Automation

Hooking Into a Provisioning Life Cycle

Scaling Up and Down

Deploying with Security

Transient Clusters

Sharing Metadata Services

Summary

18. Security in the Cloud

Assessing the Risk

Risk Model

Environmental Risks

Deployment Risks

Identity Provider Options for Hadoop

Option A: Cloud-Only Self-Contained ID Services

Option B: Cloud-Only Shared ID Services

Option C: On-Premises ID Services

Object Storage Security and Hadoop

Identity and Access Management

Amazon Simple Storage Service

GCP Cloud Storage

Microsoft Azure

Auditing

Encryption for Data at Rest

Requirements for Key Material

Options for Encryption in the Cloud

On-Premises Key Persistence

Encryption via the Cloud Provider

Encryption Feature and Interoperability Summary

Recommendations and Summary for Cloud Encryption

Encrypting Data in Flight in the Cloud

Perimeter Controls and Firewalling

GCP

AWS

Azure

Summary

A. Backup Onboarding Checklist

Backup Onboarding Checklist

Backup

Services

Cloudera Manager

HDFS

HBase

Hive/Impala

Sqoop

Oozie

Hue

Sentry

Index

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Tags: Lars George, Paul Wilkinson, Ian Buss, Jan Kunigk, Architecting Modern