In today’s data-driven economy, the race to getting timely insights from various data sources comes with its challenges.  Business leaders face rising warehouse costs, siloed analytics, and mounting pressure to support AI/ML while still maintaining flexibility, governance, and speed.

But what if your cloud object storage like Amazon S3 could deliver the reliability of a data warehouse, the flexibility of open formats, and the efficiency of serverless computing?

This is the transformation enabled by Apache Iceberg; a modern open table format that brings ACID transactions, time travel, schema evolution, and real-time analytics to the cloud. When paired with Apache Spark and AWS-native services like Glue, Athena, EMR, and Lake Formation, Iceberg turns your data lake into a low-cost, scalable lakehouse.

In this article, we’ll be exploring:

• How Iceberg solves legacy data lake challenges such as immutability, governance, and performance
• Why companies are getting shy of popular data warehouses due to high cost and vendor lock-in
• How this singular technology provides net-positive gains for AI/ML teams as a result of an analytics-ready datalake
• A cloud-native blueprint for building a resilient, cost-efficient lakehouse on AWS

Overcoming Legacy Lake Pitfalls with Apache Iceberg

Imagine your S3 buckets as the heart of your analytics platform, not just a passive archive. Traditional data lakes often devolve into tangled file jungles, uncontrolled writes, schema drift, This makes it a nightmare for any meaningful analytics source, thus the need for a different storage tailed for analytics workloads - a data warehouse which basically increase time-to-insight for a data lifecycle. Apache Iceberg replaces that chaos with a single source of truth on object storage.

With Iceberg’s lightweight metadata files, every write is atomic and safe. Data engineers and analysts can add or rename columns without breaking downstream reports, and roll back to any point-in-time snapshot with a simple SQL clause. On AWS, those tables live in the Glue Data Catalog, so Spark jobs on EMR, ad-hoc SQL in Athena, and Glue ETL pipelines all share a consistent view of the data. For example, partitions can be defined once by order date and region for an e-commerce events table, with Iceberg handling file placement, partition pruning automatically. Analysts query only what they need, and wait times shrink from minutes to seconds.

Rethinking Data Warehouses: Cost & Vendor lock-In

Many organisations are growing wary of mounting warehouse bills and the grip of proprietary formats. Snowflake and Redshift may promise performance, but they lock you into their own storage and compute layers and their pricing models can balloon as data volumes or concurrency grow.

By contrast, an Iceberg-powered lakehouse on the cloud such as AWS decouples storage (S3) from compute (EMR, Athena, Glue). One only has to  pay for S3 and per-query scans, this separates the fixed costs of warehouse clusters. And because Iceberg is an open specification, you retain the freedom and flexibility to switch query engines and more importantly cloud platforms without rewriting table definitions.

Empowering AI/ML Teams with an Analytics-Ready Lake

For data science teams, latency and data drift are common debilitating factors. Iceberg’s time-travel capabilities allows AI/ML teams to pin a model training run to the exact snapshot of data used in production two weeks ago. This is ideal for reproducing results and diagnosing data or model drift. Change data capture flows into merge-on-read tables can feed real-time feature stores, and scheduled compaction jobs keep those delta files from slowing downstream queries. This is a huge net-positive gain for AI/ML workloads.

This unified lakehouse approach means BI dashboards, Spark jobs, and SageMaker notebooks all draw from the same, governed dataset. This eliminates inefficient activities such as copies or wrestling with version mismatches. The result is faster iteration, fewer surprises in model performance, and a streamlined path from raw data to insights.

Blueprint for a Resilient, Cost-Efficient Lakehouse on AWS

Start by configuring AWS Glue as the Iceberg catalog and applying Lake Formation policies to lock down access at the table or column level. Ingest historic data and batch sources with Spark on EMR or Glue ETL, choosing copy-on-write for predictable nightly loads. In parallel, route real-time events such as  order updates, clickstreams into AWS services like Glue Streaming or Flink into merge-on-read tables, then compact them on a schedule to optimise read performance.

Automate snapshot cleanup with Athena’s VACUUM command to remove stale versions and free storage. Analysts are able to use  Athena’s serverless SQL while data engineers tune Spark jobs for efficient partition pruning and minimal small-file overhead. Finally, AI/ML teams can easily use  SageMaker or Redshift Spectrum for specialised workloads, or embrace federated queries if you need warehouse-style concurrency.

A Cloud-Agnostic Path Forward

Although this blueprint focuses on AWS, Iceberg’s open metadata and file-based structure work equally well on Google Cloud (Dataproc + BigQuery Omni) or Azure (Synapse Spark + serverless SQL pools). Pipelines and table definitions move seamlessly from one platform to another. This means no vendor lock-in not just at the warehouse/query engine layer, but at the cloud level.

As organisations seek to modernise their data estate, the lakehouse approach powered by Apache Iceberg stands out: it addresses the governance, performance, and flexibility gaps of legacy lakes, slashes storage and compute costs, and unifies analytics and machine-learning workflows.

At Deimos, our cloud-native data specialists turn these patterns into production-ready solutions. We’ll help you:

• Define effective partitioning and metadata strategies
• Migrate from Hive, Redshift, or Snowflake into an Iceberg-first architecture
• Automate security, governance, and snapshot lifecycle management
• Optimise Spark, Athena, and Glue for peak performance and minimal spend

Whether you’re piloting your first lakehouse or scaling to petabytes, Deimos makes the transition seamless so your data platform becomes a resilient, cost-smart engine for tomorrow’s insights. To request a complimentary assessment, click here.

Glossary of Terms

• Apache Iceberg: An open table format designed for huge analytic datasets, providing ACID transactions, schema evolution, time travel, and efficient metadata management.
• Data Lakehouse: A modern data architecture that combines the low cost and scale of data lakes with the performance and reliability of data warehouses.
• ACID Transactions: A set of properties (Atomicity, Consistency, Isolation, Durability) that ensure reliable database transactions.
• Time Travel: The ability to query data as it existed at a previous point in time—helpful for debugging and reproducibility.
• Merge-on-Read / Copy-on-Write: Table update strategies. Merge-on-Read delays compaction for faster writes. Copy-on-Write rewrites files during updates for read efficiency.
• Partition Pruning: A performance technique that skips irrelevant data segments during query execution.
• Glue / Athena / EMR / Lake Formation:
  
  • AWS Glue: A serverless data integration service.
  • Amazon Athena: An interactive SQL query engine for S3.
  • Amazon EMR: Managed big data processing with Spark, Hive, etc.
  • AWS Lake Formation: A service to set up and secure data lakes easily.
    

Common Questions

Q. Do I need to completely migrate from my data warehouse to adopt Iceberg?

A: Not at all. You can start by offloading archival or infrequently queried data to Iceberg on S3, and gradually build hybrid workflows using Redshift Spectrum or federated queries.

Q. Can I use Iceberg without Spark or EMR?

A: Yes. Apache Iceberg supports multiple engines. On AWS, you can use it via Athena (SQL), Glue ETL (Python/Scala), or even integrate with Flink for streaming workloads.

Q. How does Iceberg ensure data reliability?

A: Iceberg supports atomic commits, snapshot isolation, and rollback, so queries always see a consistent view of the data even with concurrent writes. Iceberg has the full ACID properties of a traditional database that makes it reliable. 

Q. What makes Iceberg better than Delta Lake or Hudi?

A: Iceberg excels in multi-engine support, scale, and schema and partition evolution. Unlike Delta Lake (which was historically Spark-first), Iceberg has broader support across query engines and is designed to handle massive datasets efficiently. Also, it is the only open table format that does not follow the traditional hive file structure system, which eliminates the drawbacks of hive file structure regarding scalability and concurrency.

Q. How does Iceberg help AI/ML teams specifically?

A: Iceberg’s time travel lets you pin model training to specific data snapshots, ensuring reproducibility. It also supports real-time feature pipelines via merge-on-read tables and enables unified access across ML and BI tools.

Q. Is there a lock-in risk with Iceberg?

A: No. Iceberg is open-source and works across AWS, GCP, and Azure. Its format is not tied to any specific compute engine or cloud vendor, offering true portability.

Q. How is data governance handled in an Iceberg-based lakehouse?

A: AWS Lake Formation and Glue can enforce column- or table-level access controls, while Iceberg’s metadata ensures consistency and version control across teams.

Q. What file formats does Iceberg support?

A: Iceberg supports popular datalake formats such as  Parquet, ORC, and Avro, This provides flexibility in choosing the format that best fits your use case.

Q. Is Iceberg suitable for streaming data?

A: Iceberg can support streaming workloads. Combining Iceberg with streaming technologies like Flink and Kafka, Iceberg enables incremental writes and real-time analytics. In fact Confluent - a major Kafka cloud vendor has specialised supports for Iceberg called Tableflow.