Apache Iceberg Consulting Services
Apache Iceberg has become the open standard for analytical data storage, solving the key limitations of Hive tables and proprietary data warehouse formats. ACID transactions, schema evolution, time travel, partition evolution, and efficient metadata management make Iceberg the foundation of modern data lakehouse architectures.
BigData Boutique helps engineering teams design and implement Apache Iceberg data lakehouses on AWS, GCP, and Azure. Our consultants have deep experience with Iceberg on Spark, Flink, Trino, Athena, Databricks, and Dremio—from greenfield architecture design through migration from Hive and Delta Lake.
Trusted By
13+yrs
Data Engineering Expertise
5x+
Typical Storage Efficiency Gain
0
Data Loss Migrations
Apache Iceberg Consulting Services
Our Iceberg consulting services span the full lifecycle of data lakehouse implementations—from architecture design through ongoing optimization and support.
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Iceberg data lakehouse architecture design including catalog selection, partition strategy, and multi-engine access patterns
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Migration from Hive tables, Delta Lake, and legacy data warehouses to Apache Iceberg with zero data loss and full validation
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CDC pipeline design with Debezium, Flink, and Kafka to keep Iceberg tables synchronized with operational databases in real time
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Iceberg table optimization: compaction scheduling, partition evolution, metadata pruning, and file sizing for query performance
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Multi-engine query federation enabling Spark, Flink, Trino, Athena, and Databricks to read and write the same Iceberg tables
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Data governance and cataloging with Apache Polaris, AWS Glue, Unity Catalog, or Nessie for access control and lineage tracking
Why Apache Iceberg for Your Data Lakehouse
Apache Iceberg solves the fundamental problems that made Hive tables unsuitable for modern analytical workloads. Iceberg provides ACID transactions (no more partial reads during writes), schema evolution without rewriting data, partition evolution without migrating existing files, and time travel for point-in-time queries and rollback. These capabilities make Iceberg the foundation for production-grade data lakehouses.
Iceberg's table format is engine-agnostic and open. The same Iceberg tables can be read and written by Spark, Flink, Trino, Presto, Athena, Databricks, Snowflake, and DuckDB. This multi-engine access pattern eliminates data silos and makes it possible to use the best tool for each workload without copying data between systems.
With the rise of Apache Polaris and Gravitino as open catalog standards, and growing support from every major cloud provider, Iceberg is the safe long-term choice for organizations building data infrastructure that will last. Our consultants help you design Iceberg architectures that are performant, governable, and cost-effective from day one.
We Can Help You
Increase
Query Performance
Storage Efficiency
Data Freshness
Engine Flexibility
Reduce
Infrastructure Costs
Data Duplication
Pipeline Complexity
Vendor Lock-In
Iceberg Table Management & Maintenance
Iceberg tables accumulate snapshot metadata and small files over time. Without proper maintenance, query performance degrades and storage costs increase. We implement automated maintenance pipelines including snapshot expiration, orphan file cleanup, and data file compaction.
We also design partitioning strategies that balance write performance with query pruning effectiveness, and implement partition evolution when access patterns change—without rewriting historical data.
Real-Time Iceberg with Flink & CDC
One of the most powerful Iceberg use cases is CDC-driven real-time analytics. Using Debezium to capture database changes, Kafka for transport, and Apache Flink to write to Iceberg tables, we build pipelines that make operational data available for analytics with sub-minute latency.
Flink's native Iceberg sink supports row-level delete, update, and insert operations, making it possible to maintain an Iceberg table that mirrors your operational database in near-real-time. Combined with Iceberg's ACID guarantees, this enables consistent analytical queries without snapshot isolation issues.
FAQ
Apache Iceberg is an open table format for large-scale analytical data on cloud object storage. Unlike Hive, Iceberg provides ACID transactions, schema evolution without rewriting data, partition evolution, time travel queries, and efficient metadata management that scales to billions of files. Iceberg solves all major production pain points of Hive tables.
Both are excellent choices for modern data lakehouses. Iceberg has broader multi-engine support (Spark, Flink, Trino, Presto, Athena, Databricks, Snowflake, DuckDB) and is the open standard with vendor-neutral governance. Delta Lake has deep Databricks integration and simpler operational tooling for Databricks-centric workloads. We help you evaluate based on your engine mix, cloud provider, and governance requirements.
Catalog choice depends on your environment. AWS Glue Data Catalog is the simplest option for AWS deployments. Databricks Unity Catalog is best for Databricks-centric workloads. Apache Polaris (the Snowflake-donated open catalog) is emerging as the open standard. Nessie provides Git-like branching for Iceberg tables. We recommend the right catalog based on your requirements.
We use Iceberg's in-place table migration capability to convert existing Hive tables to Iceberg format without data rewriting. For large tables, we implement a parallel migration with snapshot validation. We handle all metadata migration, partition mapping, and downstream query compatibility testing.
Yes. Apache Flink has a native Iceberg connector that supports streaming writes with exactly-once semantics. We design Kafka-to-Iceberg pipelines using Flink for sub-minute data freshness with ACID guarantees on the Iceberg side.
Key maintenance tasks include: snapshot expiration (removing old snapshots and their data files), orphan file cleanup, data file compaction (combining small files into larger ones for query efficiency), and statistics refresh for query planning. We implement automated maintenance pipelines using Spark or Flink that run these tasks on appropriate schedules.
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