In my previous blog, we explored how to build a dynamic and robust data ingestion pipeline using Azure Data Factory. The feedback from the community was overwhelming, and it sparked some amazing discussions! Thanks to the curiosity and questions from my peers, I’m excited to share this second installment, diving deeper into the advanced challenges and solutions for optimizing API-driven data pipelines.

If you haven’t read the first post, no worries — this one stands alone! However, if you're curious about schema alignment, dealing with duplicate data, and the foundational aspects of building a smarter data pipeline, feel free to check it out here.

🔍 Key Lessons From Real-World Scenarios

After publishing the first post, I received insightful questions on topics ranging from schema alignment to managing API timeouts for massive datasets. In this blog, I’ll share the answers and some bonus learnings we uncovered along the way.

🛠️ 1. Schema Alignment Simplified

One major hurdle in data pipelines is ensuring schema alignment across systems. Here’s how we tackled it:

• Fetching the Schema: The API we used provided a dedicated endpoint to retrieve schema details. By validating this schema in SQL Server Management Studio (SSMS), we ensured that the table structures, data types, and constraints matched the database requirements.

• Handling Timeouts: Schema updates for large datasets often resulted in timeouts. To address this, we temporarily scaled up database resources, which significantly reduced the time required to save schema changes. Once the updates were complete, resources were scaled back to their original configuration to avoid unnecessary costs.

🧹 2. Automating Duplicate Handling

Data duplication is a common issue, especially when ingesting large datasets. While manual identification and deletion are possible, automation is key for efficiency. Here’s the SQL query we used:

WITH DuplicateRecords AS (
    SELECT recordId,
           ROW_NUMBER() OVER (PARTITION BY recordId ORDER BY recordId) AS row_num
    FROM my_table
)
DELETE FROM my_table
WHERE recordId IN (
    SELECT recordId
    FROM DuplicateRecords
    WHERE row_num > 1
);

💡 Pro Tip:

While this query works for one-time cleanup, you can integrate it into a stored procedure or pipeline step for ongoing automation. This ensures data remains clean without manual intervention.

⚙️ 3. Optimizing Throughput Without Over-Scaling Hardware

Ingesting large datasets efficiently can be tricky, especially when hardware scaling only yields marginal improvements. In our case, the initial approach involved upscaling resources, but the gains were not significant. We reverted to the original plan and focused on the following strategies:

• Batch Processing: Restructure the pipeline to handle larger chunks of data in fewer API calls, thereby reducing network overhead.

• Parallelization: Execute multiple data ingestion operations in parallel to utilize existing hardware more effectively.

• Switching Tools: Replace the web activity with more efficient tools, such as Azure Function Apps or Logic Apps, which can handle larger payloads per request.

• Data Compression: If the API supports compression (e.g., GZIP), use it to reduce payload size and processing time.

By combining these techniques, we improved throughput without scaling hardware, achieving a cost-effective and efficient solution.

⏳ 4. Tackling API Timeouts for Long-Running Data Retrieval

Timeouts are a common challenge when working with APIs, especially during large-scale data ingestion. To address this:

• API Documentation Review: We thoroughly reviewed the API's documentation to understand its timeout settings and limitations. However, it did not provide explicit solutions for handling prolonged data retrieval.

• Handling Specific Timeout Scenarios: During one operation involving a particularly large dataset, a timeout occurred after processing a significant portion of the records. To manage this, we implemented the following strategies:

  • Dynamic Pagination: Breaking the data retrieval into smaller, paginated chunks to keep requests within the timeout limits.
  • Incremental Fetching: Adjusting request sizes dynamically based on observed timeout patterns to avoid failures.
  • Retry Mechanism: Adding retry logic in the pipeline for automatic recovery from temporary failures.

These adjustments helped stabilize the pipeline, allowing for reliable ingestion even in the face of API limitations.

🎯 Key Takeaways

Building and optimizing data pipelines is a continuous learning process. The strategies shared here were shaped by real-world challenges, experimentation, and collaboration with my peers.

Have you faced similar challenges? Got better ideas or insights? I’d love to hear your thoughts and experiences in the comments below!

📢 Let’s Continue the Conversation

If you haven’t read my first blog yet, check it out for insights into building a dynamic data pipeline. And if you enjoyed this post, don’t forget to like, share, and leave your feedback!

🚀 Together, let’s master the art of building smarter pipelines!