Building Data Integration Solutions. Unifying Data for Enhanced Decision Making - Helion

ebook

Autor: Jay Borthen
ISBN: 9781098173029
stron: 274, Format: ebook
Data wydania: 2024-10-15
Księgarnia: Helion

Cena książki: 29,90 zł (poprzednio: 249,17 zł)
Oszczędzasz: 88% (-219,27 zł)

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Are you struggling to manage and make sense of the vast streams of data flowing into your organization? In today's data-driven world, the ability to effectively unify and organize disparate data sources is not just an advantage—it's a necessity. The challenge lies in navigating the complexities of data diversity, volume, and regulatory demands, which can overwhelm even the most seasoned data professionals.

In this essential book, Jay Borthen offers a comprehensive guide to understanding the art of data integration. This book dives deep into the processes and strategies necessary for creating effective data pipelines that ensure consistency, accuracy, and accessibility of your data. Whether you're a novice looking to understand the basics or an experienced professional aiming to refine your skills, Borthen's insights and practical advice, grounded in real-world case studies, will empower you to transform your organization's data handling capabilities. You will:

Understand various data integration solutions and how different technologies can be employed
Gain insights into the relationship between data integration and the overall data lifecycle
Learn to effectively design, set up, and manage data integration components within pipelines
Acquire the knowledge to configure pipelines, perform data migrations, transformations, and more

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Spis treści

Building Data Integration Solutions. Unifying Data for Enhanced Decision Making eBook -- spis treści

Preface
- Overview of the Book Structure and What Readers Can Expect to Learn
- Conventions Used in This Book
- Using Code Examples
- OReilly Online Learning
- How to Contact Us
- Acknowledgments
I. Foundations of Data Integration
1. Introduction to Data Integration
- Data Integration and Data Management
- Defining Data Integration
- Why Data Integration Is Important
- The Evolution of Data Integration
- Data Integration Use Cases and Case Studies
  - Healthcare
  - Tax Administration
  - Immigration and Border Control
- Conclusion
2. Key Concepts in Data Integration
- Data Properties
  - Data Types
  - Data Structure Types
  - Metadata
  - Data Orientation
  - Encodings
  - File Formats
    - CSV/TXT
    - JSON
    - Parquet
    - Avro
    - Protocol Buffers
    - ORC
    - HDF5
  - Data Context
- Data Stores
  - Types of Storage
    - File-based storage
    - Block storage
    - Object storage
    - In-memory storage
    - Virtual storage
  - Data Models and Management Systems
    - Relational databases
    - Non-relational databases
      - Column-oriented databases
      - Document stores and key-value storage
      - Graph databases
      - Object-oriented databases
      - Vector databases
      - Multimodel databases
    - Data warehouses
    - Data lakes and data lakehouses
  - Hybrid and Multicloud Storage
- Data Movement and Transformation
  - Connectors and Connections
  - Migration
  - Ingestion
  - Replication
  - Batches, Streams, and Events
    - Batches
    - Streams
    - Events
  - Pipelines
  - Conditioning
  - Change Data Capture
- Integration Management
  - Data Services
  - Data Orchestration
- Conclusion
3. Data Integration Challenges
- Organizational Issues
- Technical Challenges
  - Data Quality
  - Data Processing
    - Hardware
    - Software
- Security and Compliance
- Conclusion
4. Models, Architectures, Methods, and Patterns
- Models
  - Conceptual Data Integration Models
  - Logical Data Integration Models
  - Physical Data Integration Methods
- Architectures
  - Hub-and-Spoke
  - Point-to-Point
  - Enterprise Service Bus
  - Federation
- Methods
- Patterns
  - Ingestion Patterns
    - Extract, transform, load
    - Extract, load, transform
  - Data Consolidation Pattern
  - Data Replication and Propagation Pattern
  - Data Virtualization Pattern
  - Event-Driven Integration Pattern
- Conclusion
II. Tools, Technologies, and Frameworks
5. Data Integration Tool Options
- Open Source Versus Commercial Solutions
  - Advantages of Open Source Solutions
  - Advantages of Commercial Solutions
- Programming Languages Versus Low-Code/No-Code Platforms
- Cloud Versus On-Premises Architectures
  - On-Premises Considerations
  - Cloud Service Providers
    - Amazon Web Services
    - Google Cloud Platform
    - IBM Cloud
    - Microsoft Azure
    - Oracle Cloud Platform
- Distributed Versus Centralized Data Systems
- In-Memory Processing
- Security and Compliance
- Conclusion
6. Data Stores and Management Systems
- Relational Databases
  - IBM Db2
  - Microsoft SQL Server
  - MySQL and MariaDB
  - Oracle Database
  - PostgreSQL
  - SQLite
  - Sybase and SAP
- Non-Relational Databases
  - Document Stores and Key-Value Storage
    - Amazon DynamoDB and DocumentDB
    - Apache Ignite
    - MongoDB
    - Redis
  - Graph Databases
    - Amazon Neptune
    - Neo4j
    - TigerGraph
  - Vector Databases
    - LanceDB
    - Pinecone
    - Weaviate
  - Wide-Column Databases
    - Apache Cassandra and HBase
    - AWS Keyspaces
    - Azure Cosmos DB
    - Google Bigtable
- Data Warehouses
  - Amazon Redshift
  - Apache Doris, Druid, Hadoop, and Hive
  - Cloudera Data Warehouse
  - IBM Db2 Warehouse
  - Snowflake
- Data Lakes and Lakehouses
  - Amazon Simple Storage Service
  - Apache Hudi and Iceberg
  - Azure Blob Storage
  - Delta Lake
  - Google Cloud Storage
  - IBM Cloud Storage Services
- Conclusion
7. Data Ingestion and Streaming Tools
- Apache Beam, Flink, Spark, and Storm
- Apache NiFi
- AWS Glue and Amazon Kinesis
- Azure Event Hubs
- Confluent and Kafka
- Conclusion
8. Comprehensive Integration Suites
- AWS Glue, Amazon Elastic MapReduce, and Amazon Q
- Azure Data Factory
- Databricks
- Fivetran
- IBM DataStage and App Connect
- IICS and PowerCenter
- Microsoft SQL Server Integration Services
- MuleSoft
- Oracle Data Integrator and GoldenGate
- Pentaho
- Qlik, Talend, and Stitch
- TIBCO
- Conclusion
III. Introducing the Example Data Integration Solution
9. Introducing the Example Solution
- Objectives
- Initial State
- Planned Architecture
- Conclusion
10. Implementing a Batch Solution
- Setting Up Qlik Replicate
  - Setting Up a Windows Server EC2 Instance for Qlik Replicate
  - Installing and Downloading Qlik Replicate
  - Setting Up Endpoint Connections
- Setting Up Databricks
  - Setting Up Databricks in AWS
  - Connecting Databricks
- Conclusion
11. Implementing a Streaming Solution
- Raspberry Pi and Sensor Setup
  - Bill of Materials
  - Sensor Configuration
- Creating a Confluent Cloud Cluster
- Creating a Local Python Environment
- Cluster Settings
- Creating a Topic
- Configuring a Client
- Creating the Python Producer and Consumer Applications
- Setting Up a Connector
- Conclusion
A. Setting Up the Data Integration Solution Example
- Ubuntu Linux and PostgreSQL
  - Setting Up Ubuntu AWS EC2 Instances
  - Creating a PostgreSQL Database in the Rocky Linux AWS EC2 Instance
- A Final Thought on Sensor Devices
B. References
Key Terms Glossary
Acronyms Glossary
Index