📥 How to Not Break Your Data Pipeline: A Guide to Stream Ingestion Done Right

Modern data systems depend heavily on reliable message and stream ingestion. Whether you’re dealing with user events, telemetry data, or application logs, it’s critical to design ingestion systems that are robust, scalable, and fault-tolerant. In this post, we explore the core challenges and best practices around stream ingestion.

🧬 Schema Evolution

Schema evolution is inevitable — APIs change, third-party sources evolve, and internal schemas grow. Even a small change, like adding a field or renaming one, can break downstream consumers if not managed properly.

🔧 Strategies to Manage Schema Evolution

Use a Schema Registry 🗂️ – Tools like Apache Avro, Confluent Schema Registry, or Google Pub/Sub schema registry help version your schemas and enforce compatibility.
Implement Dead-Letter Queues (DLQ) ☠️ – Route unprocessable messages due to schema mismatches to a DLQ for later inspection.
Stakeholder Communication 🗣️ – Establish a process to get notified when upstream schemas are about to change. Schema contracts or interface agreements can help.

⏰ Late-Arriving Data

Events don’t always arrive when expected. Network issues, retries, or batch upstream systems can delay event arrival.

🛠 How to Handle:

Define a grace period or watermark. For example, allow a 5-minute delay window in Flink or Spark.
Implement cutoff logic: Discard or reroute events older than a certain time.
Track and report late arrivals for operational visibility.

🔁 Ordering and Multiple Delivery

Stream ingestion systems like Kafka or Pub/Sub do not guarantee strict ordering across partitions, and many support at-least-once delivery, meaning:

Messages can arrive out of order.
Messages may be duplicated.

✅ Solutions:

Use idempotent consumers that can handle reprocessing the same message.
Implement deduplication logic based on event IDs or timestamps.
Use partitioning keys wisely to preserve intra-partition order.

🔄 Replay Capability

Replay gives you the power to rewind your event stream to a specific point in time. Essential for:

Debugging & testing
Reprocessing after failures or bugs

Available In:

✅ Kafka
✅ AWS Kinesis
✅ Google Pub/Sub

Not Available In:

❌ RabbitMQ
❌ Redis Streams (by default)

Log-based systems provide better historical traceability than memory-based ones.

⏳ Time to Live (TTL)

TTL defines how long unprocessed messages remain in the system before they’re discarded.

💡 Tips:

Too short → Messages may expire before being processed.
Too long → Risk of backlog buildup and memory pressure.

Set TTL based on:

Peak processing delays
Retry strategy
Resource availability

📏 Message Size Limits

Your stream system will have maximum allowed message sizes. Exceeding them results in errors.

System	Default Max Size
Kafka	1 MB (configurable to >20 MB)
Kinesis	1 MB
Pub/Sub	10 MB
RabbitMQ	Limited by memory and protocol, generally ~128MB

Chunk large messages or store payloads in object storage with pointers if necessary.

⚠️ Error Handling & Dead-Letter Queues

Not every event is ingestible. Errors may include:

Schema mismatches
Malformed payloads
Application-level logic errors

🔄 What to Do:

Route failures to a DLQ 🔁
Monitor the DLQ for patterns (bad producers, app bugs, etc.)
Build tools to reprocess from DLQ once the issue is resolved

DLQs are a safety net that prevent broken messages from clogging your system.

🌍 Multi-Region Ingestion

Streaming in distributed environments across regions helps with:

Redundancy and failover
Lower latency (consume close to where data is generated)
Cost savings on inter-region bandwidth

🧭 Design Considerations:

Use edge streaming services or local brokers
Replicate data asynchronously to centralized lakes
Be mindful of event ordering across regions

📌 Final Thoughts

A well-designed ingestion pipeline balances durability, latency, and scalability. Choosing the right streaming infrastructure, enforcing schema governance, preparing for replayability, and building observability into the system are all critical.

💬 Have thoughts or strategies you’ve used in your ingestion system? Hit me up on LinkedIn!