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Write Once. Read Anywhere. Scale AI Faster. Streamline ETL processes
Acumen Vega Iceberg
The Challenge: Data Silos Are Slowing AI & ML Innovation
Today’s enterprises rely on multiple data platforms — BigQuery, Redshift, Snowflake, SQL Server, and more—spread across hybrid and multi-cloud environments.
- 🔹 Siloed, unstructured data makes integration complex and costly.
- 🔹 Traditional ETL pipelines are slow, expensive, and redundant.
- 🔹 AI & ML teams struggle to access high-quality training data at scale.
The result? Companies waste time and resources on data movement instead of AI innovation.
The Solution: Acumen Vega Iceberg
Acumen Vega Iceberg automates data access and sharing by converting all legacy and multi-platform data into Apache Iceberg format.
Once ingested, BigLake and BigQuery can seamlessly read and process this data without transformation, duplication, or ETL overhead.
✅ Write Once, Read Anywhere – Unified Iceberg format ensures all tools can ingest data natively.
✅ Instant AI-Ready Data – Structured & unstructured data is accessible on-demand.
✅ 100x Faster ML Model Training – No conversion delays, AI at hyperspeed.
✅ Petabyte-Scale Performance – Designed for massive, enterprise-wide datasets.
✅ Eliminate Costly Data Duplication – Store once, access across platforms.
✅ Seamless Multi-Cloud Data Sharing – No vendor lock-in.
Why Choose Acumen Vega Iceberg?
🔹 Optimized for Google Cloud – Direct integration with BigQuery, BigLake, and GKE.
🔹 Unmatched AI Readiness – Enables multi-billion parameter foundation models effortlessly.
🔹 Data-Centric AI Workflows – Eliminates complex ETL, accelerates ML and analytics.
🔹 ACID-Compliant & Interoperable – Versioning, time travel, and fine-grained access control ensure data integrity.
Seamless Integration Across Platforms
Acumen Vega Iceberg automatically converts data into Iceberg format, ensuring smooth interoperability between on-prem and cloud environments.
🔹 Native BigQuery & BigLake Support – No extra setup required.
🔹 Multi-Source Compatibility – Works with SQL Server, Postgres, Snowflake, Redshift, and more.
🔹 Scalable on Google Kubernetes Engine (GKE) – Deploy effortlessly, scale instantly.
🔹 AI-Powered Data Flow – One-time or continuous, automatic data ingestion.
Key Use Cases
🔹 AI & ML Acceleration – Reduce ETL, eliminate data redundancy, and fast-track model training.
🔹 Continuous Data Flow – Automated, uninterrupted data ingestion at petabyte scale.
🔹 Advanced Data Governance – Auditing, lineage tracking, and granular access control.
🔹 Enterprise Data Interoperability – Seamless sharing across cloud and on-prem systems.
Why Now? The Future is Iceberg
💡 All major cloud platforms (Google Cloud, AWS, Azure, Snowflake, Databricks) now support Iceberg for data sharing.
💡 Enterprises are moving away from loading data into Snowflake or BigQuery—instead, they store in Iceberg and consume data where needed.
💡 Legacy data migration is a challenge—Acumen Vega Iceberg solves it by converting data into Iceberg format, enabling BigLake to leverage it instantly.
💡 Acumen Vega Iceberg is the missing link between legacy data systems and modern AI-driven cloud architectures.
Get Started with Acumen Vega Iceberg Today
👉 Unlock the full potential of your AI & ML initiatives.
👉 Eliminate costly ETL & data duplication.
👉 Seamlessly integrate across Google Cloud & beyond.
👉 Request a Demo Now
Clients Say
Tom Green
Director
Acumen Velocity transformed our cloud migration process. Their expert guidance and tailored solutions enabled us to streamline operations and significantly reduce costs. Highly recommend
Emma Roberts
Manager
Thanks to Acumen Velocity's comprehensive data analytics services, we gained valuable insights that boosted our decision-making process and drove our growth. A game-changer for our business
Frequently Asked Questions
What is Apache Iceberg, and why should I convert my data to it?
Apache Iceberg is an open table format for huge analytic datasets. It offers features like schema evolution, ACID compliance, and time travel. Converting your data to Iceberg improves interoperability, performance, and simplifies data engineering workflows across platforms like BigQuery, Spark, Trino, and more.
Which legacy and multi-platform data sources can be converted to Apache Iceberg?
Most structured data formats like CSV, JSON, Avro, Parquet, and ORC stored in systems like Hive, HDFS, relational databases, or cloud storage (GCS, S3) can be converted into Iceberg format using supported tools and connectors.
Does converting to Iceberg require a full ETL pipeline?
No. Solutions like BigLake or automated ingestion tools can convert data to Iceberg without full ETL processes. They eliminate the need for data duplication or transformation by converting in place or during ingestion.
How does Apache Iceberg handle schema evolution during conversion?
Iceberg natively supports schema evolution, allowing you to add, rename, or drop columns without rewriting existing data. During conversion, the schema is preserved and managed in metadata files.
Can BigQuery and BigLake read Iceberg tables directly after conversion?
Yes. Once data is converted into Iceberg format and stored in supported cloud storage (e.g., GCS), BigQuery and BigLake can directly query Iceberg tables without requiring additional transformation or duplication.
Is it possible to keep the converted Iceberg tables in sync with the original data sources?
Yes. Incremental ingestion or change data capture (CDC) mechanisms can be used to keep Iceberg tables synchronized with legacy systems.
What are the storage and performance benefits of using Apache Iceberg?
Iceberg optimizes query performance through metadata pruning, partition elimination, and manifest files. It also provides efficient storage by supporting columnar formats and large-scale data management.
How is metadata managed in Iceberg, and why is it important during conversion?
Iceberg uses a layered metadata architecture (snapshots, manifests, manifest lists) that enables time travel, rollback, and audit trails. During conversion, accurate metadata capture is critical for full Iceberg compatibility.
What are the key considerations before starting a data conversion to Iceberg?
You should evaluate: (a) Source data formats and size (b) Compatibility with target query engines (e.g., BigQuery) (c) Need for schema evolution or time travel (d) Update and delete requirements (e) Cost and performance implications of cloud storage and compute
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