Apache Iceberg

Apache Iceberg

• Apache Iceberg is an open-source table format designed to handle large-scale analytic datasets efficiently. It was initially developed by Netflix to address limitations in Hive, particularly for incremental processing and streaming data.
• Iceberg provides features like ACID compliance, schema evolution, hidden partitioning, and time travel, making it a robust solution for managing complex data workflows
• An open table format for analytical datasets
• Brings simplicity of SQL tables to data lakes and DWH with reliability andconsistency of Cloud

Interoperability and OpenStandards

• Interoperability across compute engines
• Vendor neutral
• Broad ecosystem support

Expressive SQL Support

• Provides advanced SQL capabilities for querying large datasets efficiently.
• Handles complex data types,aggregations, filtering andgrouping effectively
• ACID Guarantee Robust ACID support to ensure data integrity and reliability in data lakes.

Schema Evolution

• Modify schema (add, drop,rename columns) without table rewrites or losing historical data.

• Schema evolution refers to the ability to modify a table’s schema (like adding, renaming, or deleting columns) without disrupting existing data or queries.
• Significance: Businesses often adapt their data models over time. For example, an e-commerce company might decide to add a new field like discount_code to their orders table or rename an outdated field.
• How Apache Iceberg Helps: Iceberg supports schema evolution by tracking changes in the metadata layer. These schema updates are non-destructive, meaning you don’t need to rewrite all existing data.

Facilitate DML (Data Manipulation Language)

• A robust table format should enable standard DML operations like INSERT, UPSERT (MERGE), and DELETE. These are critical for modifying data within a table while ensuring consistency and accuracy.

Significance:

• How Apache Iceberg Helps: Iceberg supports these DML operations efficiently through its ACID-compliant framework. For example, using SQL commands like:

Time Travel

• Allows users to access historical data by querying previous versions of a table, based on snapshots or timestamps.

ACID Guarantees

• ACID (Atomicity, Consistency, Isolation, Durability) guarantees ensure that DML operations are executed safely without introducing data corruption or inconsistencies.
• Significance: For example, processing financial transactions in a banking application requires ACID properties to ensure that no double withdrawals or corrupt records occur.
• How Apache Iceberg Helps: Iceberg guarantees safe execution of DML by managing transactional metadata and providing snapshot isolation. If an INSERT operation fails, the data isn’t partially written—ensuring a clean and reliable state

• Storage Engine: Manages data storage and retrieval, ensuring efficient access and indexing.
• Compute Engine: Handles data processing tasks like transformation, querying, and analysis (e.g., Apache Spark, DuckDB, Trino).
• Catalog: Organizes metadata and provides a unified view of data, helping with management and discovery (e.g., Unity Catalog, AWS Glue, Apache Polaris).
• Table Format: Defines how data is structured within tables, supporting features like schema evolution and partitioning (e.g., Apache Iceberg, Delta Lake).
• File Format: Specifies the format for storing files, optimizing for efficient data storage and retrieval (e.g., Parquet, Avro).
• Storage: Refers to physical storage systems that provide scalable and durable data storage (e.g., S3, GCS, ADLS).

Unlocking Efficiency and Scalability with Apache Iceberg: Key Benefits for Modern Data Management

1. Fewer Copies = Less Drift

• Technical Explanation: Iceberg is ACID-compliant, which allows it to handle updates, deletes, and inserts without creating additional copies of data. It leverages snapshot isolation to ensure that concurrent operations do not interfere with each other. Instead of duplicating datasets for schema changes or transformations, Iceberg updates its metadata layer to accommodate these changes.
• Key Mechanism: Schema Evolution—Iceberg supports schema changes like adding, renaming, or dropping columns without needing to rewrite data files. This prevents the need to create new datasets during changes.
• Example: When adding a "discount" column to an orders table, Iceberg modifies the metadata layer without touching the actual data files, avoiding redundant copies.

2. Faster Queries = Fast Insights

• Technical Explanation: Iceberg optimizes query execution by utilizing hidden partitioning and file skipping. Traditional systems require explicit partitioning definitions, but Iceberg automates this by storing partition metadata in its manifest files. Query engines use this metadata to prune irrelevant files, drastically reducing the data scanned.

Key Mechanism:

• Example:

3. Data Snapshots = Mistakes Hurt Less

• Technical Explanation: Iceberg employs a snapshot-based architecture to enable time travel and rollback. Each snapshot represents the state of a table at a specific point in time, including the metadata and list of data files.

Key Mechanism:

• Example: After an accidental deletion of rows, the query:

4. Affordable Architecture = Business Value

• Technical Explanation: Iceberg reduces the need for expensive ETL jobs by supporting direct reads and writes on cloud object stores like S3. Its columnar file formats (e.g., Parquet) and metadata optimizations allow efficient analytics without preprocessing.

Key Mechanism:

• Example: A retailer analyzing sales data can directly query Iceberg tables stored in S3 without daily ETL jobs, reducing infrastructure costs.

5. Open Architecture = Future Ready

• Technical Explanation: Iceberg’s open architecture ensures compatibility with multiple storage formats (e.g., Parquet, ORC, Avro) and query engines (e.g., Spark, Flink, Trino). It adheres to a format-agnostic design, ensuring flexibility and scalability.

Key Mechanism:

• Example: A company using Spark for batch processing can seamlessly adopt Flink for real-time analytics without disrupting the existing Iceberg-based pipeline.

Decoding Hive Table Structure: Metastore, Partitions, and Data Organization

Hive Metastore

• The diagram visually explains how Hive tables are structured in a Metastore, with a focus on partitioning and file organization.

• At the top of the diagram, the Hive Metastore is shown as the central component for managing metadata of Hive tables. It stores essential information about tables, partitions, and data locations.
• In Action: The Metastore serves as a catalog, keeping track of the table db1.table1 and its hierarchical structure.

2. Table Location: /db1/table1

• The path /db1/table1 represents the root directory where the table's data is stored.
• In Hive, tables are organized as a directory containing subdirectories (partitions) and data files. This logical organization aligns with the physical layout on the filesystem (e.g., HDFS).

3. Partitions: /k1=A and /k1=B

• The table is partitioned by the key k1, which is represented by two partitions:
• Purpose of Partitions: Partitions divide data into smaller, manageable chunks, improving query efficiency. Instead of scanning the entire table, queries can focus on specific partitions.

4. Sub-partitions: /k2=1, /k2=2

• Each partition (e.g., /k1=A) further branches into sub-partitions based on the second partition key k2.
• These nested partitions help organize data hierarchically, improving query performance by enabling file pruning.

5. Data Files

• At the deepest level of the hierarchy, data files are stored within each sub-partition. These files contain the actual rows of data for the table.
• Hive Metastore keeps metadata about these files, like their locations and sizes, which query engines use to optimize query execution.

Advantages:

• Partitioning improves query performance by reducing the amount of data scanned.
• Hive Metastore centralizes metadata management, making it easier to query large datasets.

Understanding Hive Metastore

Hive Metastore (HMS) is a critical component in modern data processing frameworks. It serves as a centralized metadata store for managing information about Hive tables, partitions, and their physical locations. Here are its main features explained theoretically:

1. Partitioning and Bucketing

Partitioning: Organizes data into smaller, logical segments (e.g., by date, region) to avoid scanning the full table during queries.

Bucketing: Further divides data within partitions by hashing on specific columns (e.g., user_id), facilitating efficient joins and aggregations.

• These techniques reduce data scanned during queries, improving efficiency.

• The query scans only the partition /log_date=2025-03-16 and the bucket containing user_id=12345. Avoids a full table scan, speeding up query execution.

2. File Format Agnostic Querying in Hive Metastore

What It Means: File format agnostic querying allows users to query datasets stored in different file formats (e.g., Parquet, ORC, Avro) without requiring any changes to the SQL query or the application logic. The Hive Metastore (HMS) abstracts the underlying file format and provides a unified interface for querying data.

• This means users can focus on writing business queries without worrying about how the data is stored physically. The Hive Metastore handles all the details about where the data is stored, its file format, and other metadata.

How It Works

Metadata Storage: The Hive Metastore contains metadata about each table, including:

Query Abstraction: When a user runs a SQL query, the query engine (e.g., Hive, Spark, Trino) fetches the necessary metadata from the Hive Metastore and reads the data regardless of its file format. The engine optimizes the query based on file characteristics stored in the Metastore.

Compatibility: Hive Metastore makes it possible to mix and match file formats within the same data platform. You can have one table in Parquet, another in ORC, and yet another in Avro.

You don’t need to specify that the sales_data table is stored in Parquet format. The Hive Metastore handles the abstraction, allowing you to focus on the query logic.

Users don't need to learn the specifics of each file format. Queries are simple and consistent.

Different file formats can coexist in the same data ecosystem.

3. Atomic Operations for Directories

In distributed systems, "atomic" means an operation is all-or-nothing—it either completes fully or has no effect at all. Atomic operations for directories ensure that updates or replacements in the file system are handled as single, complete transactions, avoiding partial updates or inconsistent states.

This feature is crucial for large-scale data systems where multiple queries or writes might be happening concurrently. It guarantees that any reader or writer accessing the data will always see a consistent view of the directory.

updates the partition metadata to point to the new directory. The operation is atomic—either the directory switch completes fully, or the old directory remains unchanged

Scenario: Partial Update During Partition Replacement

• If the system crashes while executing this command, the operation doesn’t partially write metadata or corrupt the dataset.

Success: The new partition is fully written and registered in the Metastore.

Failure: The old partition remains unchanged, avoiding any disruption to ongoing queries.

Use Case: You want to refresh the data for a specific partition in a table—let’s say /region=North—with updated records. The process involves:

1. Generating a new directory (/new_data/region=North) containing the updated data.
2. Swapping the old directory (/region=North) with the new one in a single atomic operation.

Reference:

https://iceberg.apache.org/docs/nightly/