How Iceberg and AIStor Power the Modern Multi-Engine Data Lakehouse

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The modern data landscape is defined by explosive growth in data volume, diversity, and velocity. Organizations no longer rely on a single analytics engine or storage system; instead, they operate across multiple query engines, machine learning frameworks, and real-time analytics platforms. To support this complexity, the concept of the data lakehouse has emerged—combining the flexibility of data lakes with the reliability and performance of data warehouses.

At the heart of this modern lakehouse architecture are two critical technologies: Apache Iceberg, an open table format that brings transactional consistency and schema evolution to object storage, and AIStor, a high-performance, S3-compatible object storage platform optimized for analytics and AI workloads. Together, Iceberg and AIStor form a powerful foundation for a multi-engine data lakehouse, enabling consistent, scalable, and high-performance analytics across diverse workloads.

This article explores how Iceberg and AIStor work together, why they are well-suited for multi-engine environments, and how developers and data engineers can implement them using practical coding examples.

Understanding the Challenges of Modern Data Architectures

Traditional data architectures suffer from several well-known limitations. Data warehouses are expensive and rigid, while data lakes lack transactional guarantees, governance, and performance optimizations. As organizations adopt multiple engines—such as Spark for batch analytics, Trino for interactive SQL, and machine learning frameworks for model training—data consistency and interoperability become major challenges.

Common problems include:

  • Data corruption due to concurrent writes
  • Inconsistent table schemas across engines
  • Expensive data duplication for different tools
  • Poor performance when querying large datasets
  • Difficulty managing historical data versions

A modern lakehouse must solve these issues while remaining cloud-native, scalable, and cost-efficient. This is where Iceberg and AIStor excel.

Apache Iceberg: The Foundation of Open Table Management

Apache Iceberg is an open table format designed specifically for large-scale analytics on object storage. Unlike traditional Hive-style tables, Iceberg introduces a metadata-driven architecture that tracks data files, schemas, partitions, and snapshots in a robust and reliable way.

Key features of Iceberg include:

  • ACID transactions on object storage
  • Schema evolution without rewriting data
  • Time travel and snapshot isolation
  • Hidden partitioning for better query optimization
  • Multi-engine compatibility

Iceberg tables are engine-agnostic, meaning the same table can be accessed simultaneously by Spark, Trino, Flink, and other query engines without locking or data duplication.

AIStor: High-Performance Object Storage for Analytics and AI

AIStor is an enterprise-grade object storage system designed for data-intensive workloads. It provides S3 compatibility while delivering high throughput, low latency, and advanced performance optimizations tailored for analytics and machine learning.

AIStor plays a critical role in the lakehouse by serving as the physical storage layer for Iceberg tables. Its strengths include:

  • Massive parallel I/O for large-scale analytics
  • Optimized performance for small and large objects
  • Strong consistency guarantees
  • Scalability across on-premises and cloud environments
  • Seamless integration with data processing engines

By combining Iceberg’s logical table management with AIStor’s optimized storage layer, organizations gain a lakehouse that performs reliably at scale.

Why Iceberg and AIStor Are Ideal for Multi-Engine Lakehouses

A multi-engine lakehouse allows different engines to access the same data concurrently. For example:

  • Spark for ETL and batch analytics
  • Trino for ad-hoc SQL queries
  • Flink for streaming ingestion
  • Python-based frameworks for machine learning

Iceberg ensures consistent metadata and transactional integrity, while AIStor ensures high-performance object access across all engines.

This combination eliminates the need for engine-specific data silos and reduces operational complexity. All engines read and write the same Iceberg tables stored in AIStor, using open standards.

Architecture Overview of an Iceberg + AIStor Lakehouse

At a high level, the architecture consists of:

  • Storage Layer: AIStor object storage
  • Table Format: Apache Iceberg
  • Compute Engines: Spark, Trino, Flink, and ML frameworks
  • Metadata Management: Iceberg metadata files and catalogs

The separation of storage and compute allows independent scaling. Storage capacity and performance can grow without impacting compute clusters, and vice versa.

Creating an Iceberg Table on AIStor Using Spark

Below is an example of creating an Iceberg table using Apache Spark, with AIStor as the S3-compatible storage backend.

from pyspark.sql import SparkSession

spark = SparkSession.builder \
    .appName("IcebergWithAIStor") \
    .config("spark.sql.catalog.lakehouse", "org.apache.iceberg.spark.SparkCatalog") \
    .config("spark.sql.catalog.lakehouse.type", "hadoop") \
    .config("spark.sql.catalog.lakehouse.warehouse", "s3a://lakehouse-warehouse/") \
    .config("spark.hadoop.fs.s3a.endpoint", "http://aistor-endpoint:9000") \
    .config("spark.hadoop.fs.s3a.access.key", "ACCESS_KEY") \
    .config("spark.hadoop.fs.s3a.secret.key", "SECRET_KEY") \
    .getOrCreate()

spark.sql("""
CREATE TABLE lakehouse.sales (
    order_id BIGINT,
    customer_id BIGINT,
    amount DOUBLE,
    order_date DATE
)
USING iceberg
PARTITIONED BY (days(order_date))
""")

This code demonstrates how Iceberg metadata is stored alongside data files in AIStor, enabling transactional table creation on object storage.

Writing and Updating Data with ACID Guarantees

One of Iceberg’s most powerful features is its support for atomic writes and updates. The following example shows how to insert and update data safely.

spark.sql("""
INSERT INTO lakehouse.sales VALUES
(1001, 501, 250.75, DATE '2024-01-10'),
(1002, 502, 180.40, DATE '2024-01-11')
""")

spark.sql("""
UPDATE lakehouse.sales
SET amount = 300.00
WHERE order_id = 1001
""")

Even though the data resides in object storage, Iceberg ensures consistency by committing metadata changes atomically, while AIStor ensures durable and high-speed object writes.

Querying the Same Iceberg Table from Trino

A major advantage of Iceberg is its ability to be queried by multiple engines without data duplication. The same table created in Spark can be queried using Trino.

SELECT customer_id, SUM(amount) AS total_spent
FROM lakehouse.sales
GROUP BY customer_id
ORDER BY total_spent DESC;

Trino reads Iceberg metadata directly and retrieves data files from AIStor, benefiting from predicate pushdown and partition pruning without relying on Hive-style directory structures.

Supporting Streaming and Incremental Data Ingestion

Iceberg also supports streaming workloads when paired with engines like Flink. Streaming jobs can append data incrementally while batch engines query the same tables.

INSERT INTO lakehouse.sales
SELECT
    order_id,
    customer_id,
    amount,
    order_date
FROM streaming_orders;

This unified approach allows real-time ingestion and batch analytics to coexist on the same dataset, a key requirement of modern lakehouses.

Time Travel and Data Versioning for Analytics and AI

Iceberg’s snapshot-based design enables time travel queries, which are particularly valuable for auditing, debugging, and machine learning reproducibility.

SELECT *
FROM lakehouse.sales
FOR SYSTEM_TIME AS OF TIMESTAMP '2024-01-10 00:00:00';

AIStor’s durable object storage ensures that historical data files remain accessible, while Iceberg manages the logical snapshots efficiently.

Performance Optimization with AIStor and Iceberg

Performance in a lakehouse depends on both metadata efficiency and storage throughput. Iceberg optimizes query planning through metadata pruning, while AIStor delivers high I/O performance through parallel access and optimized object handling.

Key performance benefits include:

  • Reduced metadata scans
  • Faster query planning
  • High-throughput reads for large scans
  • Efficient small-file handling through compaction

Together, they enable analytics workloads that rival traditional data warehouses in performance.

Governance, Schema Evolution, and Data Reliability

Iceberg’s schema evolution capabilities allow teams to add, rename, or remove columns without rewriting data or breaking queries.

ALTER TABLE lakehouse.sales ADD COLUMN discount DOUBLE;

This flexibility is critical in fast-moving organizations where data models evolve rapidly. AIStor ensures that underlying objects remain consistent and protected, forming a reliable foundation for governance and compliance.

Enabling AI and Machine Learning Workloads

Machine learning pipelines benefit greatly from Iceberg’s consistency and AIStor’s performance. Training data can be read reliably, and feature datasets can be versioned using snapshots.

This allows data scientists to:

  • Reproduce experiments
  • Track feature changes over time
  • Train models on consistent datasets
  • Share data across teams and tools

The result is a unified data platform that supports both analytics and AI without compromise.

Conclusion

The modern multi-engine data lakehouse is no longer a theoretical concept—it is a practical necessity for organizations operating at scale. As data workloads diversify across batch processing, interactive analytics, streaming ingestion, and AI-driven modeling, the underlying data platform must provide consistency, performance, and openness.

Apache Iceberg delivers the transactional guarantees, metadata intelligence, and engine interoperability required to manage data reliably on object storage. AIStor complements this by providing a high-performance, scalable, and durable storage layer optimized for analytics and AI workloads. Together, they eliminate the historical trade-offs between flexibility and reliability.

By unifying multiple engines around a single, open table format stored in a high-performance object store, Iceberg and AIStor enable organizations to reduce complexity, eliminate data silos, and future-proof their data architectures. They empower teams to innovate faster, analyze data more efficiently, and build intelligent systems with confidence.

As the data ecosystem continues to evolve, the combination of Iceberg and AIStor stands out as a foundational pillar for the next generation of lakehouse platforms—open, scalable, and ready for the demands of modern data-driven enterprises.