A Step-by-Step Guide for Troubleshooting Fivetran Sync Delays

Running into Fivetran sync delays? Whether your syncs are failing, stalling, or lagging, you’re not alone. These issues usually occur when data volumes spike, destination limits are hit, or dependent pipelines break upstream.

To resolve them, you’ll need to address API limit restrictions, reduce transformation dependencies, and address latency issues. In this article, we’ll cover these fixes and walk through a step-by-step guide to troubleshooting Fivetran sync delays.

Understanding How Fivetran Syncs Work

Fivetran connectors are pre-built components that link your sources and destinations. When a connector is first set up, it runs a historical (initial syncs) to extract all data from core source tables. This creates matching schemas in the destination that convert unsupported data types as needed.

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Once the initial sync completes, the connector switches to incremental sync to update only new or changed data at regular intervals.

Sync frequency depends on your connector type, subscription tier, and any custom intervals you’ve configured. Fivetran also uses adaptive syncing, which scales frequency down when the data is quiet and up when changes spike.

That said, sync delays aren’t always about sync intervals. Network latency, API response times, and destination load times can all contribute.If you’re new to the platform, it’s also worth reviewing the common Fivetran pitfalls before signing up.

5 Common Causes of Fivetran Sync Delays

When you come across delays in your Fivetran data sync pipelines, these are the potential root causes:

1. Huge source changes 

If the source contains numerous data updates and schema changes, sync time increases because there is more data and changes to process and move to the destination.

You can check the Fivetran platform connector logs to identify significant changes in the source. You can also query the destination to see if there were many recent updates made. That helps explain why a sync took longer.

If the amount of data to move increases, sync time naturally increases too. 

2. Latency 

Since Fivetran runs in the cloud, network issues can affect sync speed. So, check the network delays for potential latency issues. Latency can also come from regional differences. For example, if your source is in the US and the destination is in the EU, syncing between regions may introduce delays.

3. API rate limits

Some sources have API rate limits. That means the number of times we can call the API in a given time is restricted to a certain limit. When that limit is reached, Fivetran waits for the API quotas to reset. So the sync process is delayed until the source API allows more requests.

4. Transformation dependencies

Transformation steps run during the sync can introduce delays between the source and destination. Even though Fivetran follows ELT (transformations are applied after the data lands in the destination), the data becomes available downstream only after those dbt models finish running. So complex transformations can cause data access delays for downstream applications.

How to Diagnose Sync Delay Issues?

Continuously monitoring the Fivetran platform connector and logs helps you identify data sync delays. Here’s how you can do it:

Using Fivetran’s sync dashboard

The Fivetran dashboard tracks the status of your data pipelines, configurations, and performance. Navigate to the specific connector in the dashboard and check for suspicious activity, such as increased data volume or schema changes.

You can also check the sync status in the connector tab. If the status shows “Delayed,” check the alerts page for error messages.

Fivetran logs

Fivetran includes a platform connector that delivers logs and account metadata for debugging. It automatically creates a schema in your destination/warehouse and stores sync event details. You can analyze this information to spot errors. 

Fivetran usually retains logs for one week. If you want to access logs for longer, connect to an external log service like AWS CloudWatch, Azure Monitor, Datadog, or Google Cloud Monitoring. You can analyze these logs to identify delay patterns.

Connector runtime

For syncs to run on time, two things need to happen: they must start at the correct interval and complete within the expected runtime. If either fails, sync cycles could result in delays. 

If you’re using the Fivetran Platform Connector, query the `sync_history` table to extract start time, end time, and status details, and calculate the runtime. If it’s increasing, it may indicate a growing data volume. 

Source issues

The issue isn’t always with Fivetran, source issues like slow queries, exceeded API quotas, or network latency can also cause delays. To catch these, use a cloud monitoring tool that tracks and logs key metrics, so you can analyze them and identity the root cause.

Fixing the Delay: Step-by-Step Troubleshooting Guide

Troubleshooting Fivetran sync delays is easier and more effective when you follow this step-by-step practical guide.

Step 1: Check source health 

No matter how well Fivetran is set up, it can’t pull data if the source system is down. Check if the source is under heavy load, undergoing maintenance, or has lost network access. 

To track the source database health issues like memory usage, network failures, or timeouts, use cloud monitoring tools. These tools continuously monitor system performance and alert you to outages.

Step 2: Address API limit issues

When a connector hits API rate limits, syncs pause and cause delays. In that case, upgrade to a premium tier if available. Sources like Salesforce or Google Ads offer higher API limits on premium plans. Or reduce the sync frequency to stay within limits. 

Also, check the connection with the source API and reauthenticate if access tokens have expired.

Step 3: Adjust sync intervals

If sync intervals are too often (like every 15 mins), change it to less aggressive intervals (like every 30 minutes). This is especially useful when the runtime increases due to increased data volumes because longer runtimes can overlap with the next scheduled sync and cause failures.

If you’re running a historical (initial) sync, reduce the volume of data by shortening the sync window. For example, pull data from the last 2 years instead of 5.

Step 4: Manage large tables and transformations

Massive source tables with poor maintenance can slow down Fivetran syncs. Though Fivetran uses incremental updates, it depends on partitioning, primary keys, or update timestamps. Without these, incremental syncs become difficult.

In that case, break down large tables into small, manageable pieces. Smaller batches run faster and reduce timeout issues. 

Sometimes, data isn’t available downstream even after Fivetran sync. It could be due to dbt models in the middleware. When there are many dependencies, heavy joins, or complex aggregations, it can delay data availability.

To improve performance, use materialized views, simplify joins, and reduce unnecessary model dependencies.

Step 5: Seek Fivetran support

If you’ve tried all of the above and still face delays, contact the Fivetran support team. Share relevant logs, your findings, and any recent changes made to your setup. Providing clear context helps them understand the issue quickly and work with you to resolve it efficiently.

A Better Solution: Real-time Data Integration with Hevo 

If Fivetran sync delays are recurring, the root cause may be architectural.

Hevo is a fully managed, no-code ELT platform built to make data movement simple, reliable, and transparent, without the engineering overhead or ongoing maintenance that attends Fivetran.

Here’s what sets Hevo apart:

• Simple to set up: Connects 150+ sources to your data warehouse in minutes through a guided, no-code interface.
• Real-time streaming over batch: Collects data as generated using an event-driven architecture powered by Apache Kafka. When volumes spike, Kafka buffers and balances the load automatically.
• Built to handle failure: Auto-healing pipelines and intelligent retries keep data flowing reliably even when sources go down. Automatic schema handling adjusts to API or structural changes without breaking your workflows.
• Full visibility: Unified dashboards, detailed logs, and data lineage views give you end-to-end transparency across every pipeline.
• Predictable costs as you scale: Hevo’s event-based pricing starting at $239/month has no hidden fees or surprise overages. Unlike the Fivetran pricing model, your spend stays consistent as data volumes grow.

Customers also compare different ELT tools and make the switch to Hevo for its real-time data access and predictable, event-based pricing. This can cut the total cost of ownership by up to 40%.

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If you’re evaluating your options, read our in-depth Fivetran review or explore the Fivetran migration checklist to understand what switching involves.

When you’re ready, our guide on migrating from Fivetran walks you through the process step by step.Want to explore Hevo further? Book a free demo with an expert now!

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