Data Pipelines That Never Let You Down
Fragile pipelines that break silently cost you time, money, and trust. InterCode builds data pipelines with robust error handling, automatic retries, and proactive monitoring so your data arrives on time, every time.
Reliable Data Delivery at Any Scale
Data pipelines are the plumbing of modern analytics and operations. When they work, nobody notices. When they break, everything downstream suffers. InterCode specializes in building data pipelines that are robust enough to handle failures gracefully and scalable enough to grow with your data volumes.
We build both batch pipelines for periodic data processing and streaming pipelines for real-time use cases. Every pipeline includes comprehensive error handling with dead-letter queues, automatic retries with exponential backoff, and alerting that notifies your team the moment something goes wrong.
Our orchestration layer, built on Apache Airflow or Prefect, provides a single pane of glass for monitoring all your data flows. You can see pipeline status, execution history, data freshness, and error logs without digging through multiple tools.
What We Deliver
Production-grade data pipelines from ingestion to monitoring.
Batch & Streaming Pipelines
Choose the right processing model for each use case, from nightly batch loads to sub-second streaming.
- Incremental and full refresh modes
- Exactly-once delivery guarantees
Data Source Integration
Connect to databases, APIs, file systems, SaaS platforms, and event streams with reliable connectors.
- Pre-built and custom connectors
- Change data capture (CDC)
Pipeline Orchestration
Airflow and Prefect DAGs that coordinate complex multi-step workflows with dependency management.
- DAG dependency management
- Backfill and replay support
Error Handling & Retries
Graceful failure handling with dead-letter queues, automatic retries, and circuit breakers.
- Exponential backoff retries
- Dead-letter queue processing
Monitoring & Alerting
Real-time visibility into pipeline health, data freshness, and processing throughput.
- Execution history dashboards
- SLA-based freshness alerts
Our Pipeline Development Process
Source & Target Mapping
We document every data source, its schema, update frequency, and the target destination for each dataset.
- Source system profiling
- Schema documentation
Pipeline Architecture
Design the pipeline topology including processing stages, error handling strategy, and orchestration.
- Processing stage definition
- Failure recovery design
Connector Development
Build or configure connectors for each data source with proper authentication and rate limiting.
- API pagination handling
- Rate limit management
Transformation Logic
Implement data transformations with validation checks at each stage to ensure data quality.
- Schema validation
- Business rule enforcement
Orchestration & Scheduling
Set up Airflow or Prefect DAGs with scheduling, dependencies, and SLA monitoring.
- Cron-based scheduling
- Cross-pipeline dependencies
Monitoring & Handoff
Deploy monitoring dashboards, configure alerts, and train your team on pipeline operations.
- Grafana dashboards
- On-call runbooks
Source & Target Mapping
We document every data source, its schema, update frequency, and the target destination for each dataset.
- Source system profiling
- Schema documentation
Pipeline Architecture
Design the pipeline topology including processing stages, error handling strategy, and orchestration.
- Processing stage definition
- Failure recovery design
Connector Development
Build or configure connectors for each data source with proper authentication and rate limiting.
- API pagination handling
- Rate limit management
Transformation Logic
Implement data transformations with validation checks at each stage to ensure data quality.
- Schema validation
- Business rule enforcement
Orchestration & Scheduling
Set up Airflow or Prefect DAGs with scheduling, dependencies, and SLA monitoring.
- Cron-based scheduling
- Cross-pipeline dependencies
Monitoring & Handoff
Deploy monitoring dashboards, configure alerts, and train your team on pipeline operations.
- Grafana dashboards
- On-call runbooks
Pipeline Technologies We Use
Proven tools for building reliable data pipelines at any scale.
We use Fivetran or Airbyte for standard SaaS integrations and build custom pipelines with Airflow and Python for complex or non-standard sources. Kafka handles streaming when real-time is required.
Client Results
Achieved 99.9% pipeline reliability across 50+ data sources with automated failover and retry logic.
Built a Kafka streaming pipeline delivering pricing updates to search results in under 200 milliseconds.
Reduced pipeline maintenance effort by 60% through standardized orchestration patterns and self-healing error handling.
Why InterCode for Data Pipelines
Reliability Engineering
We design for failure from the start, building pipelines that handle errors gracefully instead of breaking silently.
Observability Built In
Every pipeline includes monitoring, alerting, and logging so you always know the state of your data.
Scale-Ready Architecture
Our pipelines handle today's data volumes and scale horizontally when your data grows tenfold.
Clean, Maintainable Code
Pipeline code is modular, tested, and documented so your team can maintain and extend it independently.
Frequently Asked Questions
It depends on your latency requirements. If data freshness of minutes to hours is acceptable, batch pipelines are simpler and cheaper to operate. Streaming is necessary for real-time dashboards, fraud detection, or event-driven architectures. Many organizations use both, with streaming for critical paths and batch for everything else.
Our pipelines include automatic retries with exponential backoff, dead-letter queues for records that fail validation, and circuit breakers that prevent cascading failures. Monitoring alerts your team within minutes of a failure, and our orchestration tools support easy backfill to reprocess failed runs.
Yes. We use tools like Fivetran and Airbyte for standard SaaS integrations, which provide pre-built connectors with automatic schema detection and incremental sync. For custom or unsupported sources, we build Python-based connectors with the same reliability guarantees.
We implement data quality checks at ingestion, transformation, and loading stages. This includes schema validation, null and uniqueness checks, freshness monitoring, and business rule enforcement. Failed records are quarantined in dead-letter queues for investigation rather than silently dropped.
A single-source batch pipeline with basic transformations takes 1-2 weeks. A multi-source pipeline with complex transformations and streaming components typically takes 4-8 weeks. We deliver pipelines incrementally, starting with the highest-priority data sources.
Ready for Reliable Data Pipelines?
Tell us about your data sources and delivery requirements. We will build pipelines that keep your data flowing reliably around the clock.
Contact Us