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About
Etleap is a fully integrated pipeline platform built around Apache Iceberg, designed to give data teams everything they need to run production-grade data pipelines at scale. Rather than cobbling together disparate tools, Etleap provides a single, cohesive layer that handles the four core capabilities required for Iceberg in production: Ingest, Transform, Coordinate, and Operate. With Etleap, teams can continuously load operational data from 100+ sources—including relational databases like SQL Server and MySQL, non-relational databases, SaaS apps, and more—directly into Iceberg tables. The Transform capability lets engineers shape and model data using built-in wrangling tools and dbt Core within the same pipeline, eliminating context switching. The Coordinate layer ensures ingestion and transformation run from a shared data state, preventing conflicts and maintaining consistency. Finally, automated Operate features keep Iceberg tables healthy through scheduled maintenance, compaction, and monitoring. Trusted by leading enterprises like Moderna, Morningstar, PagerDuty, and Bill.com, Etleap has demonstrated its ability to reduce ETL costs by 50%, boost ETL staffing efficiency by 400%, and compress time-to-build by over two years. It is purpose-built for data engineers, analytics teams, and platform architects who want to adopt Iceberg as their data foundation without the operational overhead of managing a fragmented stack.
Key Features
- Continuous Data Ingestion: Load operational data from 100+ sources—including SQL, NoSQL, and SaaS—continuously into Apache Iceberg tables with auto-scaling infrastructure.
- Integrated Transformation with dbt Core: Shape and model data using built-in wrangling tools and dbt Core within the same pipeline, removing the need for separate transformation tooling.
- Pipeline Coordination: Run ingestion and transformation jobs from a shared data state to ensure consistency and prevent conflicts across the pipeline.
- Automated Iceberg Table Maintenance: Keep Iceberg tables healthy with automated operations including compaction, snapshot expiry, and monitoring to ensure long-term reliability.
- 100+ Pre-built Source Integrations: Connect to a wide range of data sources out of the box, with the option to build custom integrations for unique analytics needs.
Use Cases
- Building a centralized analytics data platform on Apache Iceberg by continuously ingesting data from 100+ operational sources.
- Replacing fragmented ETL tooling with a single integrated pipeline layer for ingestion, transformation, and table maintenance.
- Enabling data-driven investment decisions by centralizing and accelerating access to complex financial data sources.
- Supporting rapid enterprise analytics adoption—going from no central data access to full cross-team availability within weeks.
- Scaling data infrastructure to handle terabytes of daily ingestion with auto-scaling compute clusters and minimal operational overhead.
Pros
- Unified Pipeline Layer: Combines ingestion, transformation, coordination, and operations in one platform, eliminating the need to stitch together multiple tools.
- Proven Enterprise Scale: Trusted by companies like Moderna and Morningstar, with demonstrated outcomes like 50% ETL cost reduction and 400% staffing efficiency gains.
- Fast Time-to-Value: Customers have gone from zero to full enterprise analytics adoption in under six weeks, with small teams managing large-scale data stacks.
- Auto-scaling Infrastructure: Built on auto-scaling EMR clusters, Etleap handles terabytes of data without teams worrying about capacity planning.
Cons
- Enterprise-Focused Pricing: Etleap targets mid-to-large enterprises with a demo-first sales model, making it less accessible for small teams or individual developers on a budget.
- Apache Iceberg Dependency: The platform is purpose-built for Iceberg; teams not using or planning to adopt Iceberg as their table format may not fully benefit.
- Limited Self-serve Transparency: Pricing and full feature details require a demo booking, which adds friction for teams doing early-stage evaluations.
Frequently Asked Questions
What is Apache Iceberg and why does it need a pipeline layer?
Apache Iceberg is an open table format that defines how data is stored and evolves in a data lakehouse. However, running it in production requires a pipeline layer to continuously feed tables, coordinate jobs, and maintain table health—which is exactly what Etleap provides.
What data sources does Etleap support?
Etleap supports 100+ data sources including relational databases (SQL Server, MySQL, PostgreSQL), non-relational databases, and SaaS applications. It also supports custom integrations for specialized analytics needs.
Does Etleap support dbt for transformations?
Yes. Etleap integrates dbt Core natively within its pipeline, allowing data engineers to write SQL-based transformation models alongside ingestion jobs in the same unified workflow.
How quickly can a team get up and running with Etleap?
According to customer testimonials, teams have gone from initial kickoff to production deployment in as little as six weeks, even with complex multi-source data environments.
Is Etleap suitable for small teams?
Etleap is designed to maximize efficiency—some customers manage their entire analytics stack with just a two-person team. However, its pricing model is enterprise-oriented, so it's best suited for companies with significant data infrastructure needs.
