7 Best Tools to Ingest Data into Apache Iceberg

TL;DR

Apache Iceberg is an advanced open table format that enables efficient data storage and analytics at scale. This article highlights 7 essential tools for seamless data ingestion into Iceberg, ensuring real-time insights and reliable data pipelines

Apache Iceberg has transformed how organizations handle large-scale data, offering features like ACID transactions, schema evolution, and time travel. It allows businesses to build robust data lakehouses that unify structured and unstructured data for analytics and machine learning.

To fully leverage Iceberg’s capabilities, effective data ingestion is crucial. Whether it’s real-time streaming, batch processing, or change data capture (CDC), choosing the right ingestion tool can ensure data consistency, performance, and ease of use.

This article explores 7 top tools for ingesting data into Apache Iceberg. From real-time data integration platforms to scalable batch processing engines, these solutions cater to a range of use cases and organizational needs, making it easier to harness the full power of your data lakehouse.

Top 7 Tools to Ingest Data into Apache Iceberg for a Scalable Data Lakehouse

Ingesting data into Apache Iceberg is important for building a strong and efficient data system. Here are 7 tools that help make the process easier and more effective.

1. Estuary Flow

Estuary Flow is a real-time data integration platform designed to simplify the process of building and managing data pipelines. It enables organizations to efficiently collect, process, and deliver data across various systems and applications. Estuary Flow supports data integration with Apache Iceberg, making it easier to ingest and organize data into Iceberg tables, ensuring compatibility with modern data lakehouse architectures.

Key Features:

• Real-Time Data Ingestion: Estuary Flow allows for the continuous collection of data from multiple sources, ensuring that information is always up-to-date.
• Change Data Capture (CDC): The platform supports CDC, enabling the detection and capture of data changes in real-time, which is crucial for maintaining data consistency across systems.
• Schema Evolution: Estuary Flow manages changes in data schemas automatically, allowing for flexibility as data structures evolve over time.
• Scalability: Built to handle large volumes of data, Estuary Flow scales seamlessly to accommodate growing data needs without compromising performance.
• Integration with Apache Iceberg: Estuary integrates with Apache Iceberg, facilitating efficient data storage and analytics within a data lakehouse architecture.

Related Articles on Using Estuary Flow to Ingest Data into Apache Iceberg:

• Steps to Load Data Into Iceberg with Estuary Flow
• Load Data From Redshift to Iceberg
• Load Data From BigQuery to Iceberg
• Load Data From Kafka to Iceberg
• Load Data from Postgres to Iceberg

2. Dremio

Dremio is a data lakehouse platform that simplifies data management and analytics. It offers an enterprise data catalog for Apache Iceberg, providing features like data versioning and governance. Dremio's SQL query engine delivers high-performance queries, and its unified analytics support self-service across various data sources.

3. Apache Spark

Apache Spark is a unified analytics engine for large-scale data processing. It integrates with Apache Iceberg, allowing users to perform batch and streaming data processing with ease. Spark's DataFrame API enables complex transformations and actions on Iceberg tables, supporting operations like reading, writing, and managing table metadata. 

4. Apache Flink

Apache Flink is a framework and distributed processing engine for stateful computations over data streams. It integrates with Apache Iceberg to provide real-time data ingestion and processing capabilities. Flink's support for event-time processing and exactly-once state consistency ensures accurate and reliable data pipelines when working with Iceberg tables.

5. Kafka Connect

Kafka Connect is a framework for connecting Apache Kafka with external systems, including databases and data lakes. It facilitates the ingestion of streaming data into Apache Iceberg tables by capturing real-time data changes and delivering them to Iceberg-managed storage. This integration supports building robust, real-time analytics pipelines. 

6. Upsolver

Upsolver is a cloud-native data integration platform designed for high-scale workloads. It simplifies the ingestion and transformation of streaming data into Apache Iceberg tables. In January 2025, Upsolver was acquired by Qlik, a global leader in data integration, data quality, analytics, and AI. This acquisition enhances Qlik's ability to provide real-time data streaming and Iceberg optimization solutions.

7. Fivetran

Fivetran is an automated data movement platform that offers connectors to various data sources, enabling seamless data replication into destinations like Apache Iceberg. It ensures data consistency and reliability by providing fully managed pipelines that adapt to schema changes and support real-time data synchronization.

Conclusion

Ingesting data into Apache Iceberg is a critical step in building an efficient and scalable data lakehouse. Among the tools available, each offers unique features to cater to various data ingestion needs, from real-time streaming to batch processing and schema evolution.

While other tools like Apache Spark, Kafka Connect, and Fivetran offer robust features, Estuary Flow’s ability to simplify real-time data pipelines and provide flexibility for evolving data needs makes it a powerful solution. Its focus on performance and ease of use ensures that organizations can achieve efficient data ingestion and management with minimal complexity.

Take control of your data pipelines today! Register for Estuary Flow and start free. Experience real-time data integration with Apache Iceberg, designed to fit your needs effortlessly.

FAQs

1. What is Apache Iceberg, and why is it important?

Apache Iceberg is an open table format designed for large-scale data storage and analytics. It supports features like ACID transactions, schema evolution, and time travel, making it ideal for building modern data lakehouses. Iceberg helps organizations unify structured and unstructured data, ensuring consistent, scalable, and high-performing analytics.

2. How do I choose the right tool for ingesting data into Apache Iceberg?

Estuary Flow is the ideal choice for ingesting data into Apache Iceberg, offering real-time data ingestion, built-in ETL capabilities, and support for change data capture (CDC). Its ease of use and scalability make it perfect for handling dynamic and evolving workflows.

3. Can I integrate real-time data streams into Apache Iceberg?

Yes, several tools support real-time data streaming into Iceberg. For example, Kafka Connect and Estuary Flow are designed to handle real-time data ingestion with support for change data capture (CDC) and continuous updates. These tools ensure your Iceberg tables stay up-to-date with minimal latency.