/
.webp)
Data connectors bridge business applications, but not all deliver true real-time synchronization. Understanding the difference between batch processing, near-real-time polling, and genuine sub-second sync helps teams select connectors that match operational requirements. The choice between these approaches impacts inventory accuracy, customer experience, and engineering maintenance overhead.
Most legacy integration platforms process data in scheduled batches ranging from 5 to 60 minutes. Tools like Fivetran, Airbyte, and traditional ETL systems excel at analytics workloads but introduce operational delays that prove problematic for transactional systems.
Batch connectors work well for reporting dashboards and data warehouses where 15-minute staleness remains acceptable. Data engineers schedule jobs to run hourly or daily, extracting complete datasets and loading them into target systems. This approach minimizes API calls and simplifies error recovery through complete reprocessing.
Jobs typically execute in three phases: extract data from source, transform it to match target schema, and load into destination. Each phase runs sequentially, creating processing windows that range from minutes to hours depending on data volume.
However, batch intervals create synchronization windows where inventory counts diverge, customer records show outdated status, and order updates lag behind actual operations. Research shows that 100ms latency reduces e-commerce revenue by 1 percent. Batch processing measured in minutes creates 900-second exposure windows during peak traffic, leading to overselling and customer abandonment rates approaching 40 percent after 3-second delays.
Retail organizations lose approximately $1.77 trillion annually to inventory distortion caused by synchronization delays. When online stores display incorrect stock levels, customers purchase unavailable items, generating cancellations that damage brand reputation and increase support costs.
Batch processing remains appropriate for analytics pipelines, historical reporting, and data warehouse loads where staleness does not impact operations. Organizations building business intelligence dashboards can tolerate hourly refreshes without operational risk.
Some platforms claim real-time capabilities through continuous API polling. These connectors query source systems every 30-60 seconds, checking for changes and propagating updates when detected. This approach reduces latency compared to batch processing but introduces new operational challenges.
Polling introduces 30-90 second average latency while consuming substantial API quota. NetSuite limits Tier 1 accounts to 15 concurrent threads, and Shopify enforces 2 requests per second on standard plans. Continuous polling exhausts these quotas quickly, triggering rate limit errors that cascade across workflows.
Organizations spending $250,000 annually on four Mulesoft vCores often implement polling workflows to achieve near-real-time sync. However, these deployments hit NetSuite's 15-thread limit during month-end close operations, causing 429 errors that halt critical financial processes. Upgrading to SuiteCloud Plus costs approximately $12,000 annually per 10 additional threads, creating step-function cost increases.
Polling also creates incomplete synchronization. If records change multiple times between polling intervals, intermediate states disappear. Financial calculations based on transient values become incorrect, and audit trails miss critical state transitions.
When inventory quantities fluctuate rapidly during flash sales, polling-based systems miss peak values, leading to overselling scenarios that batch systems avoid through complete dataset replacement. A product might drop to zero inventory, trigger backorder workflows, then restock within a 30-second polling window, leaving the connector unaware that stockouts occurred.
Polling requires maintaining connection pools, implementing exponential backoff for rate limit handling, and managing state tracking across polling cycles. Engineering teams spend significant time tuning polling intervals to balance latency against API consumption, often reaching suboptimal compromises that satisfy neither requirement.
Change Data Capture represents the gold standard for real-time synchronization. CDC monitors databases and applications at the transaction log level, detecting every modification as it occurs without querying tables or consuming API calls.
CDC-based connectors propagate changes in sub-second latency, typically 100-500 milliseconds from source commit to target update. This eliminates synchronization windows entirely, ensuring data consistency across operational systems without the staleness of batch processing or incomplete coverage of polling.
CDC operates by reading database transaction logs, also called write-ahead logs or binary logs depending on the database system. PostgreSQL uses WAL, MySQL uses binlog, and MongoDB uses the oplog. By consuming these logs, CDC tools detect changes without impacting source system performance or consuming application-level API quotas.
When a record updates in Salesforce, CDC captures the modification within milliseconds and streams it to target systems. If the same record updates again before the first sync completes, CDC queues both changes in order, ensuring targets receive all state transitions rather than just the final state that polling would capture.
This complete history proves critical for audit trails, compliance reporting, and financial reconciliation where intermediate states carry legal significance. Healthcare organizations using CDC maintain complete patient record histories for HIPAA compliance, while financial institutions track all transaction state changes for regulatory audits.
Platforms implementing CDC include Stacksync for operational sync, Debezium for Kafka-based streaming, and specialized connectors for specific databases. These tools capture inserts, updates, and deletes at the field level, maintaining complete audit trails and referential integrity across distributed systems.
Stacksync simplifies CDC implementation by providing pre-built connectors that handle transaction log consumption, schema evolution, and error recovery automatically. Organizations avoid the complexity of managing Kafka clusters and custom Debezium configurations while gaining the same sub-second latency benefits.
Real-time synchronization requires bi-directional data flow. Changes must propagate regardless of where they originate, with automatic conflict resolution when simultaneous updates occur in different systems.
Most traditional connectors only support one-way synchronization, requiring separate workflows for reverse sync. This doubles complexity and increases failure points. Engineers must maintain two configurations, debug two error paths, and monitor two sets of logs when issues arise.
Each direction requires independent schema mappings, transformation logic, and error handling. When field names differ between systems, engineers configure mappings twice. When business rules change, both workflows need updates.
True bi-directional platforms like Stacksync handle bidirectional flow in a single configuration, with built-in conflict detection and resolution strategies. When the same record updates simultaneously in Salesforce and NetSuite, the platform detects the conflict, applies predefined resolution rules, and logs the decision for audit purposes.
Conflict resolution strategies include last-write-wins, source-priority rules, and field-level merging. Last-write-wins uses timestamps to select the most recent change. Source-priority designates one system as authoritative for specific fields. Field-level merging allows different fields to update independently, preventing overwrites when changes affect separate attributes.
This approach compares timestamps and selects the most recent modification. Simple to implement but risks overwriting valid changes when system clocks drift or network delays create ordering ambiguity.
Organizations designate authoritative systems for specific data types. Salesforce might own customer contact information while NetSuite controls financial data. Conflicts resolve by selecting values from the authoritative source, ensuring data quality through controlled ownership.
The most sophisticated approach allows independent field updates. When Salesforce updates email address while NetSuite modifies billing terms, both changes apply without conflict. This granular resolution maximizes data freshness while preventing unintended overwrites.
Real-time capabilities matter only when connectors support required systems. Evaluate connector ecosystems for CRMs like Salesforce and HubSpot, ERPs including NetSuite and SAP, databases such as PostgreSQL and MySQL, and warehouses like Snowflake and BigQuery.
Stacksync offers over 200 pre-built connectors spanning operational databases, business applications, and data warehouses. All connectors support bi-directional real-time sync through CDC, eliminating API polling overhead and rate limit constraints.
The connector library includes specialized integrations for complex systems like NetSuite that require understanding of record associations, custom objects, and subsidiary structures. SAP connectors handle BAPI calls and IDoc processing. Salesforce connectors support both standard and custom objects with field-level tracking.
Database connectors implement CDC through native replication protocols. PostgreSQL connectors use logical replication slots, MySQL connectors consume binlog events, and MongoDB connectors tail the oplog. This approach provides complete change capture without requiring custom triggers or application modifications.
Each database requires different technical approaches. PostgreSQL logical replication preserves transaction ordering and handles DDL changes. MySQL binlog parsing supports row-based, statement-based, and mixed formats. MongoDB oplog tailing captures document changes with millisecond timestamps.
Real-time connectors must handle varying data volumes without degrading latency or reliability. During normal operations, systems might sync hundreds of records per minute. During bulk imports or batch updates, volumes spike to thousands of records per second.
Polling-based systems struggle with volume spikes because each poll consumes fixed API quota regardless of how many records changed. Batch systems handle spikes naturally by processing everything in the scheduled window, but latency increases proportionally with data volume.
CDC-based platforms scale efficiently because they consume transaction logs, which database systems optimize for high throughput. When 10,000 records update simultaneously, CDC captures all changes from the log without impacting application performance or consuming additional API calls.
Production CDC deployments maintain consistent latency even during peak loads through parallel processing, intelligent buffering, and adaptive batching. When change volume exceeds target throughput, systems queue changes in memory while respecting ordering constraints, then drain queues as capacity permits.
Stacksync automatically scales processing capacity based on change volume, adding workers during spikes and reducing them during quiet periods. This elastic scaling maintains sub-second latency without manual intervention or infrastructure changes.
When evaluating data connectors, prioritize sub-second latency through CDC, bi-directional flow with automatic conflict resolution, and comprehensive system coverage over batch processing alternatives that create operational blind spots and polling solutions that exhaust API quotas.
