Common Technical Errors in Databricks Pipelines & How to Handle Them

Building data pipelines on Databricks can significantly accelerate data engineering workflows—but even the most robust platforms come with their share of technical challenges. Here are some frequent databricks pipeline errors you will encounter, and tips to troubleshoot them effectively.

🔁 1. Schema Evolution Errors

Error: “Detected schema change in Delta table…”

Cause: When a new column is added or data type changes in the source, Delta tables throw an error if schema evolution is not enabled.

Fix:
✅ Use .option("mergeSchema", "true") for writes
✅ Enable Auto Merge if using Delta Live Tables
✅ Implement version control on schema changes

🧱 2. Concurrent Write Conflicts

Error: “ConcurrentModificationException” or “ConcurrentAppendException”

Cause: Multiple jobs writing to the same Delta table simultaneously.

Fix:
✅ Use OPTIMISTIC CONCURRENCY control (Delta handles some of this natively)
✅ Implement job-level locking using metadata flags or control tables
✅ Avoid overwrites unless isolated

🗃️ 3. Partition Overload or Skew

Error: Jobs hang or fail due to skewed partitions or OOM (Out Of Memory)

Cause: Uneven data distribution or too many small partitions

Fix:
✅ Use .repartition() or .coalesce() wisely
✅ Monitor skew using Spark UI
✅ Optimize partitioning strategy

🔐 4. Access Control & Credential Errors

Error: “Access Denied”, “Credentials not found”

Cause: Misconfigured IAM roles, secrets, or token expirations

Fix:
✅ Store secrets securely in Databricks Secrets
✅ Use Unity Catalog or SCIM provisioning for fine-grained access
✅ Validate AWS/Azure/GCP role permissions

⚠️ 5. Intermittent Row Loss in JDBC Reads

Error: No exception, but missing data

Cause: Partitioned JDBC reads not handling Oracle/SQLServer edge cases properly (e.g., fetchSize + cursor limits)

Fix:
✅ Reduce parallelism (fewer partitions)
✅ Tune fetchSize
✅ Validate row counts with checksum comparisons
✅ Use intermediate staging (e.g., unload to cloud storage)

🔁 Bonus: Delta Merge Failures

Error: “Target table schema is different from source”

Fix:
✅ Ensure column order and names match
✅ Use merge() with explicit column mapping
✅ Add schema enforcement in upstream transformations

💡 Pro Tips:

Always enable auto-logging and job retries in production pipelines
Use Databricks Repos + CI/CD for better version control
Monitor jobs using Databricks Job UI + Alerts
Leverage Unity Catalog for governance

🧠 Every databricks pipeline error is a learning opportunity. Mastering these helps you build resilient, scalable, and production-ready pipelines.

Srini

Data Engineer with deep AI and Generative AI expertise, crafting high-performance data pipelines in PySpark, Databricks, and SQL. Skilled in Python, AWS, and Linux—building scalable, cloud-native solutions for smart applications.