How to Stream Kinesis Data to Apache Iceberg Without Complex ETL

Your real-time events shouldn't wait in line. But when you stream Kinesis data to Apache Iceberg using traditional ETL pipelines, they do, buffering in micro-batches for 5-15 minutes before finally landing in S3 tables. And when those jobs finally write, Iceberg's optimistic concurrency control often rejects parallel commits, forcing retries and leaving you with partial data.

The cost problem is just as frustrating. Legacy ETL vendors bill by the gigabyte, which means high-throughput streams that grow faster than revenue can spike integration costs 3-5x in a single quarter.

There's a better way. Managed platforms eliminate checkpoint headaches, automatically absorb schema drift, and replace volume-based pricing with predictable costs, letting you focus on insights instead of infrastructure.

TL;DR: Stream Kinesis Data to Apache Iceberg at a Glance

• Traditional Spark or Glue streaming pipelines add 5–15 minutes of latency, create commit conflicts in Iceberg, and require constant tuning.
• Managed Kinesis-to-Iceberg connectors stream data with exactly-once delivery, automatic schema evolution, and atomic Iceberg commits.
• Capacity-based pricing avoids surprise cost spikes when stream volume grows 5–10×.
• Most teams can go from weeks of custom engineering to a production-ready pipeline in hours, without running Spark clusters or managing checkpoints.

What Are the Common Approaches to Streaming Kinesis Data to Iceberg?

Moving records from Amazon Kinesis into Apache Iceberg requires choosing between custom-built solutions and managed services. Custom approaches provide full control but require 2-4 weeks of initial engineering work plus continuous maintenance. Managed platforms reduce setup to hours while automating schema evolution, checkpointing, and compaction.

1. Custom Spark Structured Streaming

Deploy Spark on EMR, Databricks, or Kubernetes and write a streaming job that reads shards, transforms records, and commits micro-batches to Iceberg. Initial setup takes 2-4 weeks to implement checkpoint recovery, schema evolution, and file compaction. Concurrency conflicts from optimistic commits and S3 transaction-per-second limits require additional tuning.

2. AWS Glue Streaming Jobs

Glue's visual editor lets you point a streaming job at a Kinesis source and an Iceberg sink, inferring the schema automatically. The service is billed by DPU hours. You still configure IAM roles, CloudWatch logging, and network policies. Control over Iceberg write parameters is limited to what Glue exposes.

3. Kafka Connect with an Iceberg sink

Some teams place Kafka in the middle, replicating Kinesis into a Kafka topic and using Kafka Connect to write into Iceberg. This adds latency and infrastructure. The approach makes sense when Kafka is already central to your data platform.

4. Managed data integration platforms

Platforms like Airbyte ship a no-code Kinesis-to-Iceberg connector backed by 600+ other connectors. Configure sources and destinations in minutes. The service handles schema drift, checkpoints, and compaction automatically. Capacity-based pricing means a 5-10x surge in data doesn't cause proportional cost increases.

What Does a Simplified Kinesis to Iceberg Architecture Look Like?

You need three components:

• Amazon Kinesis Data Streams capturing events
• A managed connector that buffers and transforms those events
• Apache Iceberg tables on Amazon S3

Kinesis delivers high-throughput writes while preserving order within each shard. A managed connector, or a serverless service like Amazon Data Firehose, grabs records, handles schema detection, batches them into Parquet, and commits them using atomic transactions. Iceberg validates each commit before it becomes visible, giving you consistent reads across Athena, Trino, or Spark.

The connector layer handles production essentials:

• Exactly-once delivery to eliminate duplicates or gaps
• Automatic schema evolution so new fields never break queries
• Configurable flush intervals (1-5 minutes) to balance latency with file sizes
• Built-in retries and dead-letter queues to prevent data loss from transient issues

How to Configure a Kinesis Source for Streaming Replication?

Reliable replication requires pulling every record exactly once, keeping lag predictable, and avoiding throttling.

1. Authentication and access setup

Create an IAM role that the connector assumes. Grant kinesis:GetRecords, kinesis:DescribeStream, and kinesis:ListShards for read-only ingestion. For cross-account ingestion, set up a trust relationship. Enable CloudTrail for auditable records.

2. Stream configuration options

Shards define throughput. Choose the iterator that matches your use case: TRIM_HORIZON replays the entire stream for historical backfill, LATEST starts with new events only.

Tune batch size (100-10,000 records) to balance throughput against memory. Polling every 1-5 seconds keeps latency low without hammering the API. For DIY pipelines you need a DynamoDB table to persist checkpoints; managed platforms handle that automatically.

3. Data format handling

JSON is easy to debug, but Avro or Protobuf provide stronger contracts and safer evolution. Define rules for flattening nested objects so the columnar layout stays tidy. Extract an event-time field for partitioning so queries prune efficiently.

source: kinesis
aws_region: us-east-1
stream_name: orders-events
shard_iterator_type: LATEST
batch_size: 5000
polling_interval_seconds: 2
format:
  type: json
  timestamp_field: event_timestamp

How to Set Up Apache Iceberg As a Streaming Destination?

Three configuration areas determine whether Iceberg handles live traffic: catalog setup, table properties tuned for high-frequency writes, and S3 storage patterns.

1. Catalog integration

Glue Data Catalog stores metadata serverlessly and surfaces tables to Athena and Redshift Spectrum with zero additional infrastructure. Hive Metastore works for multi-cloud deployments but requires running that service independently.

2. Table configuration for streaming workloads

Pick a partition key that spreads writes and matches your most common query filter—hourly or daily event_timestamp partitions work well for log or IoT data. Set write.distribution-mode to hash so concurrent writers distribute files evenly. Target 128-512 MB Parquet files. Schedule nightly compaction if streaming micro-batches create many small files.

3. Storage optimization

Prefix fan-out (embedding the partition path under multiple top-level prefixes) keeps request-per-second rates below AWS throttle thresholds. Set a snapshot retention policy that trims historical metadata after 7-30 days.

What Operational Challenges Does This Approach Eliminate?

Managed connectors eliminate three burdens: schema drift handling, checkpoint management, and cost unpredictability.

Schema evolution without pipeline failures

Managed connectors compare incoming messages against the current table schema in real-time. When they detect an additive change like a new column, they issue an ADD COLUMN operation and continue ingesting. If a change would break compatibility, you receive an alert before data is written. Custom Spark jobs often require pausing ingestion during schema updates.

Checkpointing and exactly-once delivery

Managed services own checkpoint state instead of requiring DynamoDB tables or S3 flag files. They coordinate offsets with transactions so each record is written once, even when network failures force retries. Clear dashboards show iterator age and record throughput.

Cost predictability at scale

Self-managed Spark streaming clusters bill for every minute clusters sit idle, forcing overprovisioning for traffic spikes. Capacity-based pricing changes that: you pay for compute slices you reserve, not terabytes flowing through. Streams can expand 5-10x without triggering budget requests.

How Does This Compare to Building a Custom Pipeline?

You face a clear trade-off: spend weeks coding and maintaining a bespoke pipeline, or deploy a managed connector in hours.

What are the best practices for production Kinesis to Iceberg streaming?

1. Right-Size Your Sync Frequency

Flushing every 1-5 minutes delivers fresh data for most operational analytics while avoiding metadata bloat. Push the interval lower only when dashboards genuinely require it.

2. Implement Monitoring and Alerting

Track these signals:

• Iterator age: rising age means consumers are falling behind
• Records per second: sharp drops suggest shard throttling
• File count per snapshot: growing counts indicate too-frequent commits
• Failed commit rate: small upticks can precede rollbacks

Set alerts when iterator age exceeds an hour or commit failures spike.

3. Plan for schema governance

A pipeline that auto-detects additive changes and blocks breaking ones prevents silent downstream failures. Platforms that sync schema changes directly into the catalog remove ad-hoc Spark jobs every time the business adds a column.

4. Optimize for query patterns

Partition keys should mirror common filters such as timestamp, region, customer_id—so queries prune files instead of scanning the lake. Schedule compaction after peak hours to merge small Parquet files.

Do You Need to Build and Maintain All This Yourself?

Not anymore. With Airbyte connectors, streaming Amazon Kinesis data into Apache Iceberg does not have to mean micro-batch delays, fragile Spark jobs, or surprise cost spikes. Airbyte handles the hard parts for you: schema drift, checkpointing, and concurrent writes that commonly break DIY pipelines. Instead of babysitting custom streaming code, you get a managed connector that keeps Iceberg tables fresh and queryable in minutes. Your team spends less time fixing pipelines and more time using the data that already arrived.

Start streaming Kinesis data to Iceberg in minutes with our free tier and see how easy managed connectors can be. Try Airbyte free.

Need enterprise-grade streaming at scale? Our team can help you design a solution that fits your throughput and compliance requirements. Talk to sales.

Frequently Asked Questions

What's the typical latency when streaming from Kinesis to Iceberg?

With managed connectors, expect 1-5 minute latency depending on your flush interval configuration. Custom Spark jobs can achieve sub-minute latency but require significantly more tuning and maintenance. The flush interval represents the primary latency lever. Shorter intervals mean fresher data but more S3 commits and metadata overhead.

How do I handle schema changes in my Kinesis stream without breaking the pipeline?

Managed platforms automatically detect additive schema changes (new columns) and propagate them to your Iceberg table without intervention. For breaking changes like type modifications or column removals, platforms alert you before writing, preventing silent data corruption. Custom pipelines typically require manual intervention and pipeline restarts for any schema change.

What happens if my streaming job fails mid-batch?

With exactly-once semantics, failed batches don't result in duplicate or missing data. Managed connectors maintain checkpoint state and coordinate offsets with Iceberg transactions, so recovery resumes from the last successful commit. Custom implementations need explicit checkpoint tables in DynamoDB or S3 flag files, plus retry logic that handles partial failures.

How does capacity-based pricing compare to volume-based pricing for high-throughput streams?

Capacity-based pricing charges for reserved compute resources regardless of data volume. For streams exceeding billions of events monthly, this model typically costs 2-5x less than volume-based alternatives. A 5-10x traffic spike doesn't proportionally increase your bill, you pay the same until you need additional compute capacity.