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Spis treści

Apache Iceberg: The Definitive Guide eBook -- spis treści

• Foreword by Gerrit Kazmaier
• Foreword by Raghu Ramakrishnan
• Foreword by Rick Sears
• Preface
  • About This Book
  • Why We Wrote This Book
  • What You Will Find Inside
  • How to Use This Book
  • Feedback and Questions
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• I. Fundamentals of Apache Iceberg
• 1. Introduction to Apache Iceberg
  • How Did We Get Here? A Brief History
    • Foundational Components of a System Designed for OLAP Workloads
      • Storage
      • File format
      • Table format
      • Storage engine
      • Catalog
      • Compute engine
    • Bringing It All Together
  • The Data Warehouse
    • A Brief History
    • Pros and Cons of a Data Warehouse
  • The Data Lake
    • A Brief History
    • Pros and Cons of a Data Lake
  • Should I Run Analytics on a Data Lake or a Data Warehouse?
  • The Data Lakehouse
  • What Is a Table Format?
  • Hive: The Original Table Format
  • Modern Data Lake Table Formats
  • What Is Apache Iceberg?
    • How Apache Iceberg Came to Be
    • The Apache Iceberg Architecture
    • Key Features of Apache Iceberg
      • ACID transactions
      • Partition evolution
      • Hidden partitioning
      • Row-level table operations
      • Time travel
      • Version rollback
      • Schema evolution
  • Conclusion
• 2. The Architecture of Apache Iceberg
  • The Data Layer
    • Datafiles
    • Delete Files
      • Positional delete files
      • Equality delete files
  • The Metadata Layer
    • Manifest Files
    • Manifest Lists
    • Metadata Files
    • Puffin Files
  • The Catalog
  • Conclusion
• 3. Lifecycle of Write and Read Queries
  • Writing Queries in Apache Iceberg
    • Create the Table
      • Send the query to the engine
      • Write the metadata file
      • Update the catalog file to commit changes
    • Insert the Query
      • Send the query to the engine
      • Check the catalog
      • Write the datafiles and metadata files
      • Update the catalog file to commit changes
    • Merge Query
      • Send the query to the engine
      • Check the catalog
      • Write datafiles and metadata files
      • Update the catalog file to commit changes
  • Reading Queries in Apache Iceberg
    • The SELECT Query
      • Send the query to the engine
      • Check the catalog
      • Get information from the metadata file
      • Get information from the manifest list
      • Get information from the manifest file
    • The Time-Travel Query
      • Send the query to the engine
      • Check the catalog
      • Get information from the metadata file
      • Get information from the manifest list
      • Get information from the manifest file
  • Conclusion
• 4. Optimizing the Performance of Iceberg Tables
  • Compaction
  • Hands-on with Compaction
    • Compaction Strategies
    • Automating Compaction
  • Sorting
  • Z-order
  • Partitioning
    • Hidden Partitioning
    • Partition Evolution
    • Other Partitioning Considerations
  • Copy-on-Write Versus Merge-on-Read
    • Copy-on-Write
    • Merge-on-Read
    • Configuring COW and MOR
  • Other Considerations
    • Metrics Collection
    • Rewriting Manifests
    • Optimizing Storage
    • Write Distribution Mode
    • Object Storage Considerations
    • Datafile Bloom Filters
  • Conclusion
• 5. Iceberg Catalogs
  • Requirements of an Iceberg Catalog
  • Catalog Comparison
    • The Hadoop Catalog
      • Pros and cons of the Hadoop catalog
      • Hadoop catalog use cases
      • Configuring Spark to use the Hadoop catalog
    • The Hive Catalog
      • Pros and cons of the Hive catalog
      • Hive catalog use cases
      • Configuring Spark to use the Hive catalog
    • The AWS Glue Catalog
      • Pros and cons of the AWS Glue catalog
      • AWS Glue catalog use cases
      • Configuring Spark to use the AWS Glue catalog
    • The Nessie Catalog
      • Pros and cons of the Nessie catalog
      • Nessie catalog use cases
      • Configuring Spark to use the Project Nessie catalog
    • The REST Catalog
      • Pros and cons of the REST catalog
      • REST catalog use cases
      • Configuring Spark to use the REST catalog
    • The JDBC Catalog
      • Pros and cons of the JDBC catalog
      • JDBC catalog use cases
      • Configuring Spark to use the JDBC catalog
    • Other Catalogs
  • Catalog Migration
    • Using the Apache Iceberg Catalog Migration CLI
    • Using an Engine
      • register_table()
      • snapshot()
  • Conclusion
• II. Hands-on with Apache Iceberg
• 6. Apache Spark
  • Configuration
    • Configuring Apache Iceberg and Spark
      • Configuring via the CLI
      • Configuring via Python code (PySpark)
    • Configuring the Catalogs
      • Using org.apache.iceberg.spark.SparkCatalog
      • Using org.apache.iceberg.spark.SparkSessionCatalog
      • Using a custom catalog
    • Starting Spark with All the Configurations (AWS Glue Example)
  • Data Definition Language Operations
    • CREATE TABLE
      • Create a table with partitions
      • Use the CREATE TABLEAS SELECT statement
    • ALTER TABLE
      • Rename a table
      • Set table properties
      • Add a column
      • Rename a column
      • Modify a column
      • Drop a column
    • Alter a Table with Icebergs Spark SQL Extensions
      • Add/drop/replace a partition
      • Set the write order
      • Set the write distribution
      • Set/drop identifier fields
    • DROP TABLE
  • Reading Data
    • The Select All Query
    • The Filter Rows Query
    • Aggregation Queries
      • Count the records
      • Find the average
      • Sum the values
      • Find the maximum
    • Using Window Functions
  • Writing Data
    • INSERT INTO
    • MERGE INTO
    • INSERT OVERWRITE
      • Static overwrite
      • Dynamic overwrite
    • DELETE FROM
    • UPDATE
  • Iceberg Table Maintenance Procedures
    • Expire Snapshots
    • Rewrite Datafiles
    • Rewrite Manifests
    • Remove Orphan Files
  • Conclusion
• 7. Dremios SQL Query Engine
  • Configuration
  • Data Definition Language Operations
    • CREATE TABLE
      • CREATE TABLEAS SELECT
      • CREATE TABLE with partitioning and sorting
      • CREATE TABLE with row access and column masking
    • ALTER TABLE
      • ADD COLUMNS
      • MODIFY COLUMN
      • ALTER COLUMN
      • DROP COLUMN
    • DROP TABLE
  • Reading Data
    • Using the SELECT Query
    • Filtering Rows
    • Using Aggregated Queries
      • Count records
      • Find the average
      • Sum the value
      • Find the maximum
    • Using Window Functions
  • Writing Data
    • INSERT INTO
    • COPY INTO
    • MERGE INTO
    • DELETE
    • UPDATE
  • Iceberg Table Maintenance
    • Expire Snapshots
    • Rewrite Datafiles
    • Rewrite Manifests
  • Conclusion
• 8. AWS Glue
  • Configuration
    • Creating a Glue Database
    • Configuring the Glue ETL Job
      • Configure the data source
      • Basic properties
      • Advanced properties
  • Create a Table Using the Glue Data Catalog
    • Read the Table
    • Insert the Data
  • Conclusion
• 9. Apache Flink
  • Configuration
    • Prerequisites
    • Start the Flink Cluster and Flink SQL Client
  • Data Definition Language Operations
    • CREATE CATALOG
      • The Hadoop catalog
      • The Hive catalog
      • Custom catalogs
    • CREATE DATABASE
    • CREATE TABLE
      • CREATE TABLEPARTITIONED BY
      • CREATE TABLELIKE
    • ALTER TABLE
    • DROP TABLE
  • Reading Data
    • Flink SQL Batch Read
    • Flink SQL Streaming Read
    • Metadata Table
      • History
      • Metadata logs
      • Snapshots
  • Writing Data
    • INSERT INTO
    • INSERT OVERWRITE
    • UPSERT
  • Flink DataFrame and Table API with Apache Iceberg Tables
    • Prerequisites
    • Configuring the Flink Job
    • Starting the Cluster and Building the Package
    • Running the Job
  • Conclusion
• III. Apache Iceberg in Practice
• 10. Apache Iceberg in Production
  • Apache Iceberg Metadata Tables
    • The history Metadata Table
    • The metadata_log_entries Metadata Table
    • The snapshots Metadata Table
    • The files Metadata Table
    • The manifests Metadata Table
    • The partitions Metadata Table
    • The all_data_files Metadata Table
    • The all_manifests Metadata Table
    • The refs Metadata Table
    • The entries Metadata Table
    • Using the Metadata Tables in Conjunction
      • Get data on all the files added in a snapshot
      • Get a detailed overview of the lifecycle of a particular datafile
      • Track the evolution of the table by partition across snapshots
      • Monitor files associated with a particular branch
      • Find file differences between two branches of a table
      • Find the growth in storage by the latest snapshot of each branch
  • Isolation of Changes with Branches
    • Table Branching and Tagging
      • Table branching
      • Table tagging
    • Catalog Branching and Tagging
      • Catalog branching
      • Catalog tagging
  • Multitable Transactions
  • Rolling Back Changes
    • Rolling Back at the Table Level
      • rollback_to_snapshot
      • rollback_to_timestamp
      • set_current_snapshot
      • cherrypick_snapshot
    • Rolling Back at the Catalog Level
  • Conclusion
• 11. Streaming with Apache Iceberg
  • Streaming with Spark
    • Streaming into Iceberg with Spark
    • Streaming from Iceberg with Spark
  • Streaming with Flink
    • Streaming into Iceberg with Flink
      • Flink for stream reading
      • Flink for stream writing
    • Example of Streaming into Iceberg with Flink
  • Streaming with Kafka Connect
    • The Iceberg Kafka Sink
      • Configuring the Apache Iceberg Kafka sink
      • Setting up Kafka Connect with Apache Iceberg
  • Streaming with AWS
  • Conclusion
• 12. Governance and Security
  • Securing Datafiles
    • Securing Files: Best Practices
    • Hadoop Distributed File System
      • Access control lists
      • Encryption
      • Permissions
    • Amazon Simple Storage Service
      • Encryption
        • SSE-S3 (SSE with S3-managed keys)
        • SSE-KMS (SSE with the AWS Key Management Service)
        • SSE-C (SSE with customer-provided keys)
      • Bucket policies
      • Identity and Access Management
      • Object ACLs
    • Azure Data Lake Storage
      • ADLS encryption
      • Role-Based Access Control
      • ACLs
    • Google Cloud Storage
      • Encryption at rest and in transit
      • Identity and access management
      • Bucket policies
      • Object ACLs
  • Securing and Governing at the Semantic Layer
    • Semantic Layer Best Practices
    • Dremio
      • Data lineage of virtual datasets
      • Built-in wiki for documentation
      • Role-, column-, and row-based access rules
        • Role-based access control
        • Column-based access control
        • Row-based column access
    • Trino
  • Securing and Governing at the Catalog Level
    • Nessie
    • Tabular
    • AWS Glue and Lake Formation
      • Define data categories with tags
      • Define data access policies with TBAC
      • Apply policies to datasets
      • Monitor and audit access
      • Review and revise policies as needed
      • Leverage integration with other AWS services
  • Additional Security and Governance Considerations
  • Conclusion
• 13. Migrating to Apache Iceberg
  • Migration Considerations
    • Three-Step In-Place Migration Plan
    • Four-Phase Shadow Migration Plan
  • Migrating Hive Tables to Apache Iceberg
    • The Snapshot Procedure
    • The Migrate Procedure
  • Migrating Delta Lake to Apache Iceberg
  • Migrating Apache Hudi to Apache Iceberg
  • Migrating Individual Files to Apache Iceberg
    • Using the add_files Procedure
    • Migrating from Delta Lake or Apache Hudi Without Preserving History
  • Migrating from Anywhere by Rewriting Data
    • Migrating Data to a New Iceberg Table
    • Migrating Data into an Existing Iceberg Table
      • The COPY INTO command
      • The INSERT INTO SELECT command
  • Conclusion
• 14. Real-World Use Cases of Apache Iceberg
  • Ensuring High-Quality Data with Write-Audit-Publish in Apache Iceberg
    • WAP Using Icebergs Branching Feature
      • Create a branch
      • Write the data
      • Audit the data
        • NULL values
        • Duplicate records
        • Date consistency
        • Applying fixes
      • Publish the changes
  • Running BI Workloads on the Data Lake
    • Land the Raw Data into the Data Lake
    • Curate Virtual Data Marts/Data Products
    • Create a Reflection to Accelerate Our Dashboard
    • Connect Our View to Our BI Tool
    • Benefits of Running BI Workloads on the Data Lake
  • Implementing Change Data Capture with Apache Iceberg
    • Create Apache Iceberg Tables
    • Apply Updates from Operational Systems
    • Create the Change Log View to Capture Changes
    • Merge Changed Data in the Aggregated Table
  • Conclusion
• Index