Overview
Data that sits in one system when it is needed in another is a liability. Whether it is operational data that needs to flow between business platforms in real time, analytical data that needs to be aggregated from multiple sources for reporting, market data that needs to be ingested, processed, and stored at high throughput, or event data that needs to trigger downstream actions as it arrives — the infrastructure that moves data between systems is as critical as the systems themselves.
A data pipeline that works is invisible. Data arrives where it needs to be, in the shape it needs to be in, when it needs to be there. A data pipeline that does not work is immediately visible — in stale reports, failed integrations, missing records, and the manual intervention required to keep things running. The difference between the two is not the concept but the engineering — how the pipeline handles failures, how it manages throughput under load, how it validates data quality, how it recovers from interruptions, and how it behaves when upstream systems deliver data late, out of order, or in unexpected formats.
We design and build data pipelines that are engineered to work in production, not just in the happy path. Automated, monitored, resilient to failure, and built for the throughput your data volumes require.
What a Data Pipeline Actually Is
The term covers a wide range of architectures depending on the data volumes, latency requirements, and system boundaries involved. Understanding which pipeline architecture fits which requirement is the starting point of every pipeline project we take on.
Batch pipelines process data in scheduled runs — extracting a set of records from a source system, transforming them, and loading them into a destination. They are appropriate when data does not need to be current in real time, when the source system only supports periodic exports, or when the transformation logic is expensive enough that continuous processing would be wasteful. Well-engineered batch pipelines handle incremental extraction — processing only new or changed records rather than full dataset reloads — with idempotent load logic that prevents duplicates when a run is retried after failure.
Streaming pipelines process data continuously as it arrives — ingesting events from queues, message buses, or API streams, processing each event as it comes in, and delivering results to downstream systems with minimal latency. They are appropriate when data currency matters — trading systems, real-time analytics, operational dashboards, event-driven automation — and when the data volumes are high enough that batch processing would introduce unacceptable delays or resource spikes.
Change data capture pipelines monitor source databases for changes — inserts, updates, and deletes — and propagate those changes to downstream systems in near real time. They are appropriate when the source system does not provide an API for change notification, when the volume of changes is too high for polling to handle efficiently, or when the downstream system needs to maintain a replica of the source data that stays current.
ETL and ELT pipelines — extract, transform, load and its reverse — are the classical data warehouse pattern, moving data from operational systems into analytical stores with transformations applied either before loading or within the destination system. We build both patterns depending on where the transformation compute is best placed and what the downstream analytical requirements demand.
Engineering for Production
The gap between a pipeline that works in development and one that works reliably in production is where most pipeline projects fail. Production pipelines face conditions that development environments do not — upstream systems that change their output format without notice, network interruptions mid-run, destination systems that are temporarily unavailable, data volumes that spike beyond expected ranges, and clock skew between systems that causes events to arrive out of expected order.
We engineer pipelines for these conditions explicitly:
Fault tolerance and retry logic. Every pipeline stage has defined failure behaviour. Transient failures — network timeouts, temporary unavailability, rate limit responses — trigger retries with exponential backoff and jitter. Persistent failures are caught, logged with full context, and routed to dead letter queues or alerting channels rather than silently dropped or causing cascading failures downstream.
Idempotency. Pipeline runs that are retried after failure should produce the same result as runs that complete successfully the first time — not duplicate records, not partial loads, not inconsistent state. We design processing logic to be idempotent at every stage, so that retry is always a safe operation.
Exactly-once and at-least-once semantics. Depending on the pipeline's requirements, we implement the appropriate delivery guarantee. For financial data, trading records, and any context where duplicate processing would cause correctness problems, we implement exactly-once semantics with deduplication tracking. For contexts where occasional reprocessing is acceptable and throughput matters more, at-least-once with idempotent consumers is the right trade-off.
Schema evolution handling. Source systems change their data formats. New fields appear, existing fields change type, required fields become optional, entire structures are reorganised. Pipelines that are brittle to schema changes break silently or noisily every time an upstream system deploys an update. We design schema handling with explicit evolution strategies — forward and backward compatibility, schema registry integration where appropriate, and graceful degradation when unexpected changes arrive.
Backpressure management. When a pipeline stage processes data slower than the upstream stage produces it, the resulting queue growth eventually exhausts memory or causes cascading failures. We implement explicit backpressure mechanisms that propagate flow control signals upstream, allowing the pipeline to handle burst traffic gracefully without unbounded queue growth.
Data quality validation. Data that enters a pipeline incorrectly and propagates to downstream systems silently causes more damage than a pipeline that fails loudly on bad data. We implement data quality checks at pipeline ingestion points — completeness checks, type validation, range checks, referential integrity validation, and business rule validation — with routing logic that handles invalid records appropriately rather than letting them corrupt downstream data stores.
Monitoring and Observability
A pipeline without monitoring is an assumption that everything is working. We build observability into every pipeline we deliver:
Run-level metrics. Every pipeline run is instrumented with metrics covering records processed, records failed, processing duration, throughput rate, and lag against the source system. These metrics are exposed to monitoring infrastructure and used to drive alerting on anomalies — runs that take longer than expected, throughput that drops below normal, error rates that exceed defined thresholds.
Record-level lineage. For pipelines where auditability matters — financial data, compliance-relevant records, customer data — we implement record-level lineage tracking that records where each piece of data came from, what transformations were applied to it, and where it was delivered. This makes debugging data quality issues tractable and provides the audit trail that compliance requirements often demand.
Lag monitoring. Streaming pipelines that fall behind their source data need to be detected before the lag becomes operationally significant. We instrument pipeline lag continuously and alert when it exceeds defined thresholds, giving operations teams visibility into pipeline health before downstream systems are affected.
Alerting and escalation. Pipeline failures that are not detected and addressed promptly cause data loss and downstream system problems. We configure alerting on failure conditions with appropriate escalation paths — immediate notification for critical pipeline failures, aggregated reporting for lower-severity issues — integrated with whatever notification infrastructure the organisation uses.
Data Transformation
Moving data from one system to another is rarely a direct copy. Source systems represent data in structures that serve their own purposes — optimised for transaction processing, legacy schema designs, vendor-specific conventions — that rarely match the structure needed by destination systems. The transformation layer is where the mismatch is resolved.
We build transformation logic that handles the full range of real-world transformation requirements:
Structural transformation. Flattening nested structures, normalising denormalised records, pivoting rows to columns and columns to rows, splitting compound fields, merging fields from multiple source records into single destination records, and restructuring hierarchies to match destination schema requirements.
Data type conversion and normalisation. Dates in inconsistent formats normalised to a standard representation. Numbers in locale-specific string formats converted to proper numeric types. Enum values in source-specific codes mapped to destination-system values. Free text fields parsed for structured content. Currency values converted to a common base with appropriate precision handling.
Business rule application. Calculations that derive new values from source data — cost allocations, margin calculations, aggregations across related records, lookups against reference data, conditional logic that applies different transformations based on record characteristics. These are implemented as explicit, testable transformation functions rather than embedded SQL or opaque stored procedures.
Enrichment. Augmenting source data with additional context from reference systems — looking up customer details from a CRM, adding product category hierarchies from a product catalogue, appending geographic data from address fields, joining financial data with market reference data. Enrichment logic is designed with appropriate caching to avoid unnecessary upstream API calls on every record.
High-Throughput Pipeline Architecture
For pipelines processing high data volumes — market data feeds ingesting thousands of events per second, ecommerce platforms processing high-order volumes across multiple channels, analytics pipelines aggregating data from large user bases — throughput architecture is a design consideration from the start, not a performance fix applied after the pipeline is built.
Parallelisation. Pipeline stages that can be parallelised are designed for concurrent execution from the beginning — with appropriate partitioning strategies that distribute work evenly, avoid hot spots, and maintain ordering guarantees where the pipeline semantics require them.
Columnar storage. For analytical pipelines where the destination is a data warehouse or analytical store, we use columnar storage formats — Parquet being the standard — that dramatically reduce storage requirements and query times for analytical workloads compared to row-oriented formats.
Compression and serialisation efficiency. High-throughput pipelines spend a meaningful fraction of their compute on serialisation and deserialisation. We select serialisation formats appropriate to the throughput requirements — binary formats where throughput matters most, human-readable formats where debuggability is more important than raw performance.
Incremental processing. Full dataset reloads that process every record on every run do not scale. We design pipelines for incremental processing — tracking high-water marks, processing only new or changed records, and maintaining the state required to do so accurately even across pipeline restarts and failures.
Pipeline Integrations
Data pipelines connect systems. The range of systems we connect pipelines to covers the full breadth of our integration experience:
Business platforms. Exact Online, AFAS, Twinfield, SAP, Salesforce, HubSpot — accounting, ERP, and CRM systems that are common sources and destinations for business data pipelines, consumed via REST APIs with appropriate rate limit management and incremental extraction strategies.
Financial and trading. Exchange APIs including Binance, Bybit, and Kraken for market data and trade record pipelines. Accounting platforms for financial reconciliation pipelines. MetaTrader for trading system data extraction.
Data warehouses and analytical stores. PostgreSQL, MySQL, SQLite for operational data stores. Columnar stores and data warehouse platforms for analytical pipelines. Redis for high-speed intermediate storage and cache layers.
Technologies Used
- Rust — high-throughput streaming pipelines, latency-critical data processing, binary protocol parsing, performance-sensitive transformation logic
- C# — batch pipeline services, Excel and file parsing, enterprise system connectivity, Windows-hosted pipeline services
- SQL (PostgreSQL, MySQL, SQLite) — pipeline data stores, staging tables, reconciliation tracking, audit logs
- Parquet / columnar formats — efficient storage for high-volume analytical pipeline outputs
- Redis — pipeline state storage, deduplication tracking, high-speed intermediate data buffers
- REST / WebSocket — source and destination system connectivity across all major business platforms
- Docker — containerised pipeline deployment for consistent, isolated execution environments
- Systemd — reliable pipeline scheduling and service management on Linux infrastructure
- Next.js / TypeScript — pipeline monitoring dashboards and operational interfaces
When to Come to Us
You need a custom data pipeline when the data movement your business requires cannot be served reliably by generic integration tools, when throughput or latency requirements exceed what off-the-shelf solutions handle, when the transformation logic is complex enough that it needs to be properly engineered and tested, or when the reliability requirements of your operations demand more than a fragile point-to-point integration can provide.
We also work with organisations that have existing pipelines that are causing operational problems — runs that fail intermittently, data quality issues that appear unpredictably, throughput that cannot keep up with growing data volumes, or pipelines that are so fragile that any upstream change breaks them. We audit existing pipeline infrastructure, identify the root causes of reliability problems, and rebuild what needs to be rebuilt.
Move Your Data. Reliably. At Scale.
Data pipelines are infrastructure. They need to be engineered with the same rigour as any other critical infrastructure — designed for failure, monitored continuously, and built to keep running when conditions are not ideal. That is how we build them.