How Apache Doris and Iceberg Are Redefining the Way Data Lakes Work

|

Data lakes have become a central component of modern data architectures, enabling organizations to store vast amounts of structured, semi-structured, and unstructured data. However, managing, querying, and ensuring the reliability of data lakes pose significant challenges. Apache Doris and Apache Iceberg are two open-source projects that are redefining how data lakes work by enhancing performance, manageability, and analytical capabilities.

Understanding Apache Doris

Apache Doris is a high-performance, real-time analytical database that is designed for interactive queries and high-throughput analytical workloads. It is built upon the foundation of the StarRocks and Palo projects and provides seamless integration with data lakes and warehouses.

Key Features of Apache Doris

  • High Concurrency and Low Latency: Doris can process large analytical queries with minimal latency, making it ideal for business intelligence (BI) applications.
  • Support for Multi-Table Joins: It optimizes complex SQL queries, allowing efficient multi-table joins for better insights.
  • Unified Batch and Streaming Processing: Doris supports both batch and real-time data ingestion, making it suitable for dynamic analytics.
  • Seamless Integration with Data Lakes: It works well with Apache Iceberg, Apache Hive, and other data lake formats.

Understanding Apache Iceberg

Apache Iceberg is an open-source table format designed for large-scale data lakes. It provides transactional consistency and schema evolution while improving the performance of queries over massive datasets.

Key Features of Apache Iceberg

  • Schema Evolution: Iceberg allows tables to be updated dynamically without requiring costly data rewrites.
  • Hidden Partitioning: Unlike traditional partitioning, Iceberg optimizes query performance automatically without exposing partitioning logic to users.
  • ACID Transactions: It brings full ACID compliance to data lakes, ensuring consistency and reliability.
  • Time Travel: Users can query past versions of a dataset, making it easy to perform historical data analysis.

How Apache Doris and Iceberg Improve Data Lakes

1. Enhancing Query Performance

One of the most critical limitations of traditional data lakes is query performance. Apache Doris, when combined with Iceberg, enables high-speed analytics on vast datasets.

 

Example: Running a Query on an Iceberg Table with Apache Doris

CREATE EXTERNAL TABLE iceberg_orders (
    order_id BIGINT,
    customer_id BIGINT,
    order_date TIMESTAMP,
    total_amount DECIMAL(10,2)
)
ENGINE=iceberg
PROPERTIES (
    "database" = "iceberg_db",
    "table" = "orders",
    "catalog" = "iceberg_catalog"
);

SELECT customer_id, SUM(total_amount) AS total_spent 
FROM iceberg_orders 
WHERE order_date > '2024-01-01' 
GROUP BY customer_id;

This query benefits from Doris’s query optimizer while leveraging Iceberg’s efficient storage format.

2. Enabling Real-Time Data Ingestion

Both Apache Doris and Iceberg support real-time data ingestion, making it possible to analyze data as it arrives.

Example: Streaming Data into Apache Iceberg

from pyiceberg.catalog import load_catalog
from pyiceberg.schema import Schema
from pyiceberg.types import LongType, StringType, TimestampType, DecimalType

# Load the Iceberg catalog
catalog = load_catalog("iceberg_catalog")

# Define schema for the table
schema = Schema(
    LongType().assign_name("order_id"),
    LongType().assign_name("customer_id"),
    TimestampType().assign_name("order_date"),
    DecimalType(10,2).assign_name("total_amount")
)

# Create an Iceberg table
catalog.create_table("iceberg_db.orders", schema=schema)

This code creates an Iceberg table where real-time streaming data can be ingested and later queried using Apache Doris.

3. Enforcing ACID Compliance in Data Lakes

Traditional data lakes struggle with maintaining data consistency, especially in concurrent environments. Apache Iceberg’s ACID transactions solve this problem, ensuring reliability.

Example: Performing an Atomic Insert with Iceberg

from pyiceberg.catalog import load_catalog
from pyiceberg.transaction import Transaction

# Load the Iceberg catalog
catalog = load_catalog("iceberg_catalog")

# Start a transaction
transaction = Transaction(catalog.load_table("iceberg_db.orders"))

# Insert new data atomically
transaction.new_append().append({
    "order_id": 1001,
    "customer_id": 2001,
    "order_date": "2025-03-23T10:00:00",
    "total_amount": 150.75
}).commit()

This ensures that data is written safely and consistently into the data lake.

4. Schema Evolution Without Downtime

One of Iceberg’s strongest features is schema evolution, allowing organizations to adapt their data models without downtime.

Example: Adding a New Column to an Existing Table

ALTER TABLE iceberg_orders ADD COLUMN payment_method STRING;

This operation does not require rewriting existing data, unlike traditional data lake storage formats.

5. Time Travel and Historical Analysis

With Apache Iceberg, users can query historical versions of their datasets.

Example: Querying a Past Snapshot

SELECT * FROM iceberg_orders TIMESTAMP AS OF '2024-02-15 10:00:00';

This makes it easy to track changes, debug issues, or perform retrospective analysis.

Conclusion

Apache Doris and Apache Iceberg are revolutionizing data lake architecture by addressing some of the most pressing challenges associated with performance, scalability, and reliability. Doris enhances the query execution speed, providing a high-performance analytical engine, while Iceberg ensures the integrity, manageability, and flexibility of large-scale data storage.

By combining the strengths of both technologies, organizations can build highly efficient, scalable, and secure data lake environments that cater to diverse business intelligence, reporting, and analytics needs. The ability to process real-time data, support ACID transactions, and enable schema evolution without disrupting existing operations provides a significant advantage over traditional data lake implementations. Furthermore, Iceberg’s time travel capabilities allow users to access historical data snapshots, ensuring full traceability and auditability.

As businesses increasingly rely on data-driven decision-making, the synergy between Apache Doris and Iceberg presents a future-proof solution for handling large-scale data workloads. Whether for real-time analytics, historical data exploration, or enterprise-wide data governance, leveraging these tools together creates a seamless and optimized data lake experience.

Ultimately, the adoption of Apache Doris and Iceberg can significantly reduce query latency, improve data consistency, and simplify data lake management, making them essential components in the modern data ecosystem. Organizations looking to enhance their data lakes should strongly consider implementing these technologies to achieve the next level of efficiency and insight generation.