Snowflake Sequences Gone? Here’s How to Survive Without Breaking Your Data Pipelines

If you’ve been working with Snowflake Sequences, you know they’re the go-to tool for generating unique IDs in an ordered fashion. Clean, simple, reliable. Until… they aren’t.

Imagine waking up one fine morning, running your ETL pipeline, and suddenly realizing: your sequence is gone. Maybe the object was dropped during a cleanup, maybe a migration missed it, maybe someone thought, “oh, we don’t need this” — and poof.

Now the million-dollar question: How do you keep things running without breaking downstream systems?

Let’s break this down.

---

❓ Why are Sequences Important in Snowflake?

• They guarantee uniqueness across rows, perfect for surrogate keys.
• They avoid collisions in distributed systems where multiple warehouses are writing at once.
• They don’t need external coordination like you’d have with generating IDs in application code.

But the catch? Sequences are not transactional. Once a number is generated, it’s gone — even if your transaction rolls back. So you’re already trading off some predictability for performance.

---

🧩 What Happens if You Lose Access?

Losing sequences isn’t always catastrophic — but it can break:

• Primary keys in fact/dimension tables.
• CDC (Change Data Capture) pipelines that depend on ordered IDs.
• Joins between transactional and analytical datasets that expect a consistent surrogate key.

So, what’s Plan B?

---

🔧 Alternatives to Snowflake Sequences

Here are the battle-tested options you can roll out when sequences aren’t available:

1. UUIDs (Universally Unique Identifiers)

• Use UUID_STRING() in Snowflake.
• Pros: Virtually guaranteed uniqueness, distributed-safe.
• Cons: Long, messy, not human-friendly, slightly more storage.

👉 Best for global uniqueness when you don’t care about order.

---

2. Hash-Based Keys

• Use MD5(CONCAT(col1, col2, …)) or SHA1.
• Pros: Deterministic, easy to regenerate.
• Cons: Collisions are possible (though rare), not sequential.

👉 Best for idempotent pipelines where you can recompute IDs reliably.

---

3. Snowflake Task + Metadata Table

• Create a table to store the “current max ID.”
• A task/job increments and updates it transactionally.
• Pros: Controlled, sequential, human-readable IDs.
• Cons: Adds overhead, risk of contention at scale.

👉 Best when business logic demands sequence-like IDs.

---

4. Hybrid Key Design

• Combine timestamp + warehouse identifier + random suffix.
• Example: 20250818123045-WH01-XYZ
• Pros: Time-ordered, unique, distributed-safe.
• Cons: Requires standardization across teams.

👉 Best for ordered ingestion scenarios (CDC, IoT streams, logs).

---

⚠️ Lessons from Snowflake Docs & Real-World Use

Snowflake itself acknowledges sequences aren’t guaranteed for gap-free ordering (source: Snowflake Documentation). That’s because they’re designed for performance first.

So the key mindset shift:

Don’t design your data pipelines to depend on sequences alone.

Think of them as helpers, not as the single source of truth for uniqueness.

---

✅ Practical Recommendations

1. Audit dependencies: Run queries to find all tables/ETL jobs tied to sequences.
2. Choose the fallback strategy: UUIDs for simplicity, hash keys for determinism, hybrid for ordered events.
3. Standardize across teams: Publish a “Key Generation Guide” so no one’s reinventing the wheel.
4. Document the risks: Let business stakeholders know that gaps or format changes in IDs might appear.

---

🏁 Closing Thought

Losing a Snowflake Sequence might feel like chaos, but it’s actually an opportunity. Why? Because it forces us to build resilient ID strategies that survive migrations, multi-cloud deployments, and even accidental drops.

Or as the saying goes — “Don’t put all your uniqueness in one sequence.” 😉