Optimizing Azure Analytics data lake network performance for ETL

Cloud Platforms Microsoft Azure
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1

Our ETL pipelines are experiencing slow data transfer rates when loading large datasets into Azure Data Lake Storage Gen2. We’re moving approximately 2TB of data daily from on-premises systems, and transfers are taking 8-10 hours instead of the expected 3-4 hours.

We’re currently using VPN Gateway for connectivity and haven’t implemented data partitioning strategies. I’m exploring whether ExpressRoute would significantly improve throughput, and how to optimize our data lake structure for better network performance. Looking for experiences with similar data volumes and any monitoring approaches that helped identify bottlenecks.

2

One additional consideration - if you implement ExpressRoute, enable Private Endpoints for your Data Lake Storage account. This ensures traffic stays on the Microsoft backbone network and doesn’t traverse the internet. We measured 15-20% additional performance improvement with Private Endpoints compared to public endpoints over ExpressRoute. The combination of proper partitioning, ExpressRoute, and Private Endpoints should get you well under 3 hours for 2TB transfers.

3

VPN Gateway is definitely your bottleneck at 2TB daily. The maximum throughput is around 1.25 Gbps even with VpnGw3 SKU, which translates to roughly 6-7 hours for 2TB under ideal conditions. ExpressRoute would give you dedicated bandwidth (1-10 Gbps) with consistent latency. We saw 70% reduction in transfer times after switching from VPN to ExpressRoute 5Gbps circuit.

4

I’ve seen this pattern many times with hybrid ETL workloads. Here’s a comprehensive approach addressing data partitioning, ExpressRoute benefits, and monitoring:

Data Partitioning Strategy: Your 20-50GB files are killing parallel performance. Restructure your source data:

  • Split files into 512MB-1GB chunks (optimal for Data Factory parallelism)
  • Organize by date/category partitions (year/month/day hierarchy)
  • Use Parquet or ORC format instead of CSV for better compression and transfer efficiency
  • This alone can improve transfer times by 40-50% even over existing VPN

ExpressRoute Migration: With 2TB daily, ExpressRoute is justified. Compare your options:

  • ExpressRoute 1Gbps circuit: ~4-5 hours transfer time (predictable, no internet congestion)
  • ExpressRoute 5Gbps circuit: ~1 hour transfer time (ideal for your volume)
  • Cost is $1000-3000/month but eliminates VPN bottleneck and provides consistent latency
  • Enable ExpressRoute FastPath to bypass the virtual network gateway for even better performance

Monitoring Implementation: Set up comprehensive monitoring to validate improvements:

  • Azure Monitor metrics: Track ADLS Gen2 ingress rate (bytes/sec), transaction count, and success rate
  • Data Factory monitoring: DIU utilization (should be 80%+ during transfers), copy duration per file, and throughput (MB/s)
  • Network metrics: VPN Gateway bandwidth utilization, packet loss, and latency
  • Create Log Analytics dashboard correlating all three metric sources to identify bottlenecks
  • Set up alerts when transfer rates drop below baseline thresholds

Implement file splitting first (quick win), then monitor to validate if ExpressRoute investment is needed. Most customers see ExpressRoute pay for itself within 6 months through reduced transfer times and improved data freshness for downstream analytics.

5

We’re using Data Factory with copy activities. The source data is currently in large monolithic files (20-50GB each). Would splitting these files on-premises before transfer really make that much difference? Also, what monitoring metrics should I focus on to identify whether the issue is network throughput or data lake write performance?

6

Absolutely split those files. Data Factory copy activity parallelism is limited by file count, not file size. With 20-50GB files, you’re likely running sequential copies. Target 500MB-1GB files to maximize parallel DIU (Data Integration Units) usage. For monitoring, track these metrics in Azure Monitor: ADLS ingress bandwidth, Data Factory DIU utilization, and network metrics on your VPN Gateway. Set up Log Analytics to correlate transfer times with network saturation patterns.