The pipeline layer for Apache Iceberg

Iceberg defines table state and evolution. Etleap provides the pipeline layer that keeps those tables fed, coordinated, and correct in production.

See Iceberg pipelines in action

Why Iceberg is a strong data foundation

Iceberg is increasingly used as a foundation for modern data architectures. Its consistent table semantics, shared access model, and support for safe evolution make it well suited for long-lived data systems powering analytics, data products, and AI.

Open and vendor-neutral

Built on open standards, Iceberg is not tied to a single engine, cloud, or vendor.

Shared data access

Multiple engines can read and write the same tables without copying or syncing data.

Schema and table evolution

Schemas and partitions can evolve safely over time without breaking existing data products.

Consistent table semantics

The same table behavior and guarantees apply across all supported query engines.

What Iceberg doesn’t handle

Iceberg defines how tables are stored and how they evolve. It does not manage ingestion, coordinate transformations, or operate tables over time. Those responsibilities belong to a pipeline layer that keeps Iceberg tables up to date and reliable in production.

The pipeline layer around Iceberg

A pipeline layer turns Iceberg from a table format into a running data system. It provides the continuous flow of data, synchronization with transformations, and automated upkeep that makes Iceberg usable day to day.

Etleap provides this pipeline layer around Iceberg. It makes the operational loop continuous and dependable, so teams can rely on Iceberg as a single data foundation rather than a collection of disconnected jobs.

Fully managed pipelines for Iceberg

Etleap runs and maintains the pipelines that keep Iceberg tables fast, correct, and ready for downstream use. From ingestion through modeling to table maintenance, Etleap handles the operational work so teams don’t have to build and operate their own pipeline platform.

High-performance ingestion
Achieve low-latency ingestion with a sub-5-second delay, even at volumes of millions of rows per minute. Real-time metrics in the UI ensure your data flows reliably and on time.
Automatic storage maintenance
Key maintenance tasks like compaction and snapshot expiry are fully automated to keep your Iceberg lakehouse optimized, reducing storage costs and improving query performance.
Flexible schema management
Seamlessly adapt to schema changes in Iceberg, with metadata such as load time and source sequence numbers added automatically to keep data structured and query-ready.

Transformation via data wrangling

Etleap can be deployed inside AWS VPCs for policy/regulatory compliance and data security, and at the same time provide the user experience of a cloud-native SaaS application.

Change names and types

The raw data doesn’t always have the right column names and types. Change them to what you want them to be.

Parse any data format

So many data formats, so little time to write and maintain parsing logic. Parse JSON, CSV, Parquet, XML, etc. without writing a line of code.

Filter rows and columns

Some data is sensitive, some data is irrelevant. Filter it out before it lands in your lake or warehouse.

Fully-managed Iceberg pipelines

With Etleap, Iceberg pipelines are fully managed, ensuring your data lake operates with the efficiency of a data warehouse. From high-performance ingestion to automated maintenance and flexible schema evolution, Etleap actively manages your tables so you can focus on analyzing your data, not maintaining it.

High-performance ingestion

Achieve low-latency ingestion with a sub-5-second delay, even at volumes of millions of rows per minute. Real-time metrics in the UI ensure your data flows reliably and on time.

Automatic storage maintenance

Key maintenance tasks like compaction and snapshot expiry are fully automated to keep your Iceberg lakehouse optimized, reducing storage costs and improving query performance.

Flexible schema management

Seamlessly adapt to schema changes in Iceberg, with metadata such as load time and source sequence numbers added automatically to keep data structured and query-ready.