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Modern enterprises implementing real-time data synchronization face a fundamental challenge: efficiently processing massive volumes of database changes while applying business logic in operational pipelines. Traditional CDC approaches struggle to deliver filtering, transformation, and routing capabilities at the sub-millisecond latency that operational systems demand [1].
Stacksync addresses this technical constraint by embedding custom processing functions directly within bi-directional synchronization pipelines, enabling organizations to filter, transform, and route data changes in single-digit microseconds without compromising reliability or consistency.
Stacksync powers real-time and two-way sync between CRMs, ERPs and databases, allowing engineers to sync and consolidate data at scale while focusing on business logic rather than API infrastructure . Within these high-throughput pipelines, three fundamental operations consistently emerge:
Filter: Organizations require specific database changes based on inclusion criteria, exclusion rules, or complex conditional logic—not every modification captured by CDC systems [2]. Processing unnecessary events downstream creates inefficiencies across entire synchronization architectures.
Transform: Raw database payloads rarely match target system requirements. Operational systems need PII redaction, timestamp reformatting, field derivation, and data normalization—all with sub-second latency [3].
Route: Modern data architectures require intelligent distribution of events to specific destinations based on content analysis, metadata evaluation, or business rules [4].
Applying this logic downstream through separate services adds latency, overhead, and architectural complexity that undermines real-time operational capabilities [5]. Database triggers increase source system load, while configuration-based rules lack the flexibility for complex processing requirements.
Stacksync's unified function primitive addresses all three operations using the same interface, eliminating multiple tool dependencies while delivering enterprise-grade performance.
Filter functions return boolean values to determine which database changes propagate through synchronization pipelines:
def filter(_action, record, _changes, _metadata) do
record["region"] in ["us-west", "us-east"] and record["status"] == "active"
end
This filter processes only records from specific geographic regions with active status, eliminating downstream processing of irrelevant data while maintaining field-level precision.
Transform functions reshape database changes into target-appropriate JSON structures during synchronization:
def transform(_action, record, _changes, metadata) do
%{
"customer_id" => record["id"],
"email_normalized" => String.downcase(record["email"]),
"sync_timestamp" => metadata.commit_timestamp,
"source_table" => "#{metadata.table_schema}.#{metadata.table_name}"
}
end
This approach creates clean, immediately usable payloads with normalized data and contextual metadata, eliminating post-processing requirements [3].
Routing functions dynamically determine event destinations based on content analysis:
def route(_action, record, _changes, metadata) do
destination = case record["priority_level"] do
"critical" -> "alerts.high_priority"
"standard" -> "#{metadata.table_schema}.standard"
_ -> "events.general"
end
%{"topic" => destination}
end
This routing logic directs critical events to priority queues while maintaining standard processing flows for regular updates.
Stacksync implements functions using a restricted Elixir interpreter optimized for microsecond-latency execution within high-volume CDC pipelines. This architectural choice delivers unmatched performance while maintaining operational simplicity.
Performance Requirements: Real-time data pipelines processing 50,000+ messages per second demand sub-millisecond function execution [6]. Stacksync's approach eliminates the latency penalties of external service calls or separate processing stages.
Language Selection: Most organizations haven't implemented Elixir, but complex data processing requires full programming language expressiveness rather than limited configuration options. Stacksync includes a "Copy for ChatGPT" feature providing LLMs with complete context for translating requirements into working Mini Elixir code, eliminating the learning curve.
Unified Architecture: Rather than managing multiple specialized tools for different pipeline stages, Stacksync provides a single platform engineered specifically for real-time, bi-directional data synchronization with embedded processing capabilities.
Stacksync integrates function development into standard engineering workflows through multiple interfaces:
Console Development: The Stacksync Console provides an integrated IDE for writing and testing functions against live data streams with immediate feedback.
Configuration as Code: Functions integrate with CI/CD pipelines through declarative configuration:
functions:
region_filter:
type: filter
code: |
def filter(_action, record, _changes, _metadata) do
record["region"] in ["us-west", "us-east"]
end
customer_transform:
type: transform
code: |
def transform(_action, record, _changes, metadata) do
%{
"customer_id" => record["id"],
"email" => String.downcase(record["email"]),
"updated_at" => metadata.commit_timestamp
}
end
priority_router:
type: routing
code: |
def route(_action, record, _changes, metadata) do
%{
"destination" => "#{metadata.table_schema}.#{metadata.table_name}"
}
end
syncs:
- name: customer_pipeline
source_table: customers
filter: region_filter
transform: customer_transform
routing: priority_router
destination:
type: database
connection: "postgresql://target-db"
Version Control Integration: Functions exist as plain text, enabling standard development practices including automated testing, code review, and deployment automation.
Organizations implementing Stacksync report up to 80-90% reduction in integration maintenance effort compared to custom development or point solutions. Functions extend these benefits to data processing logic:
Eliminated Infrastructure: Organizations avoid building and maintaining separate filtering, transformation, and routing services while achieving superior performance.
Unified Operations: Point solutions create complex, untraceable data flows with fragmented governance and monitoring. Stacksync provides centralized processing within the synchronization platform .
Real-Time Consistency: Stacksync's true two-way sync ensures that updates in any system instantly reflect across all connected systems , with functions processing changes at synchronization speed.
Enterprise Security: Functions operate within Stacksync's SOC 2, GDPR, HIPAA, and ISO 27001 compliance framework with enterprise-grade monitoring and error handling.
Functions are available across all Stacksync synchronization capabilities. This establishes a unified and consistent data backbone across critical business systems, automating processes that were previously manual while empowering teams with reliable, real-time information .
Implementation Process:
Ready to eliminate custom data processing infrastructure while achieving microsecond-latency performance? Explore how Stacksync's functions can transform your real-time data synchronization architecture without the complexity of traditional approaches.
Start building with Stacksync's real-time functions →
