Real-Time Data Sync: 4 Questions We Get All the Time

We work closely with teams building real-time systems, migrating databases, or bridging heterogeneous data platforms. Along the way, we hear a lot of recurring questions. So we figured—why not write them down?

This is Part 1 of a practical Q&A series on real-time data sync. In this post, I'd like to share thoughts on the following questions:

• How should I choose between official and third-party tools?

• Can my project rely on “real-time” sync latency?

• What does real-time data sync mean to my project?

• How do I keep pipeline stability and data integrity over time?

How should I choose between official and third-party tools?​

Mature database vendors typically provide their own tools for data migration or cold/hot backup, like Oracle GoldenGate or MySQL's built-in dump utilities.

Official tools often deliver:

• The best possible performance for the migration and sync of that database.

• Compatibility with obscure engine-specific features.

• Support for special cases that third-party tools often cannot (e.g., Oracle GoldenGate parsing Redo logs).

But they also tend to:

• Offer limited or no support for other databases.

• Be less flexible for niche or custom workflows.

• Lock you in, making data exit harder than data entry.

Third-party tools shine when:

• You're syncing across platforms (e.g. MySQL > Kafka/Iceberg/Elasticsearch).

• You need advanced features like filtering and transformation.

• The official tool simply doesn't support your use case.

In short:

• If it’s homogeneous migration or backup, use the official tool.

• If it’s heterogeneous sync or anything custom, go third-party tool.

Can my project rely on “real-time” sync latency?​

In short: any data sync process that doesn't guarantee distributed transaction consistency comes with some latency risk. Even distributed transactions come at a cost—usually via redundant replication and sacrificing write performance or availability.

Latency typically falls into two categories: fault-induced latency and business-induced latency.

Fault-induced Latency:

• Issues with the sync tool itself, such as memory limits or bugs.

• Source/target database failures—data can't be pulled or written properly.

• Constraint conflicts on the target side, leading to write errors.

• Incomplete schema on the target side causing insert failures.

Business-induced Latency:

• Bulk data imports or data corrections on the source side.

• Traffic spikes during business peaks exceeding the tool’s processing capacity.

You can reduce the chances of delays (via task tuning, schema change rule setting, and database resource planning), but you’ll never fully eliminate them. So the real question becomes:

Do you have a fallback plan (e.g. graceful degradation) when latency hits?

That would significantly mitigate the risks brought by high latency.

What does real-time data sync mean to my project?​

Two words: incremental + real-time.

Unlike traditional batch-based ETL, a good real-time sync tool:

• Captures only what changes, saving massive bandwidth.

• Delivers changes within seconds, enabling use cases like fraud detection or live analytics.

• Preserves deletes and DDLs, whereas traditional ETL often relies on external metadata services.

Think of it like this: You don’t want to re-copy 1 billion rows every night when only 100 changed. Real-time sync gives you the speed and precision needed to power fast, reliable data products.

And with modern architectures—where one DB handles transactions, another serves queries, and a third powers ML—real-time sync is the glue holding it all together.

How do I keep pipeline stability and data integrity over time?​

Most stability issues come from three factors: schema changes, traffic pattern shifts, and network environment issues. Mitigating or planning for these risks greatly improves stability.

Schema Changes:

• Incompatibilities between schema change methods (e.g., native DDL, online tools like pt-osc or gh-ost) and the sync tool’s capabilities.

• Uncoordinated changes to target schemas may cause errors or schema misalign.

• Changes on the target side (e.g., schema changes or writes) may conflict with sync logic, causing the inconsistency between the source and target shcema or constraint conflicts.

Traffic Shifts:

• Business surges causing unexpected peak loads that outstrip the sync tool’s capacity, leading to memory exhaustion or lag.

• Ops activities like mass data corrections causing large data volumes and sync bottlenecks.

Network Environment:

• Missing database whitelisting for sync nodes. Sync tasks may fail due to connection issues.

• High latency in cross-region setups causing read/write problems.

You can reduce these risks significantly via change control setting, load testing during peak traffic, and pre-launch resource validation.

For data loss issues, they are typically resulted from:

• Mismatched parallelism strategy causing write disorder.

• Conflicting writes on the target side.

• Excessive latency not handled in time, causing source-side logs to be purged before sync.

How to fight back:

• Parallelism strategy mismatch often occurs due to cascading updates or reuse of primary key. You may need to fall back to table-level sync granularity and verify and correct data to ensure data consistency.

• Target-side writes should be prevented via access control and database usage standardization.

• Excessive latency must be caught via robust alerting. Also, extend log retention (ideally 24+ hours) on the source database.

With these measures in place, you can significantly enhance sync stability and data reliability—laying a solid foundation for data-driven business operations.