Overview
Data processing is where raw data becomes useful. A market feed delivering thousands of events per second that needs to be parsed, validated, enriched, and stored in milliseconds. A nightly batch job processing millions of transaction records that needs to complete within a maintenance window. A stream of user events from a large application that needs to be aggregated in real time to power live dashboards and trigger automated responses. An analytical workload that needs to join and aggregate across hundreds of millions of records to answer business questions in seconds rather than hours.
The common thread is scale ā and scale exposes the difference between data processing code that works and data processing infrastructure that performs. When data volumes are small, almost any implementation works. When volumes grow, the architectural decisions made early ā how data is partitioned, how processing is parallelised, how state is managed, how failures are handled, how compute resources are allocated ā determine whether the system keeps pace or falls progressively further behind.
We build high-performance data processing systems designed for the volumes your data actually reaches, not just the volumes it starts at. Using Rust for performance-critical processing engines, C# for enterprise data workloads and file processing, and cloud compute infrastructure where elastic scaling is the right answer ā we design and implement data processing systems that handle what you throw at them.
The Processing Models We Build
Batch Processing Batch processing operates on bounded datasets ā a defined set of records that are processed together in a single run. It is the right model when completeness matters more than immediacy, when the source data is only available periodically, when the processing logic is expensive enough that per-record real-time processing would be wasteful, or when the downstream system only needs to be updated on a schedule rather than continuously.
Batch processing at scale introduces its own set of engineering challenges. Memory management becomes critical when individual records are small but the dataset contains millions of them. Processing time becomes a constraint when business operations depend on batch completion within defined windows. Partial failure handling becomes complex when a batch is halfway through processing and the system encounters an unrecoverable error ā the restart strategy needs to be defined explicitly rather than discovered at the worst possible time.
We build batch processing systems that handle these challenges correctly ā with chunked processing that bounds memory usage regardless of input size, checkpoint mechanisms that enable restarts from the point of failure rather than from the beginning, parallel processing across independent record partitions that reduces wall-clock processing time, and comprehensive run reporting that gives operations teams visibility into what was processed, what was skipped, and why.
Real-Time Stream Processing Stream processing operates on unbounded datasets ā a continuous flow of events that arrives without a defined end. It is the right model when data currency matters, when downstream systems need to reflect changes as they happen, when event-driven processing needs to trigger actions in near real time, or when accumulating data before processing would introduce unacceptable latency.
Stream processing at scale requires managing state across a continuous flow of events without the natural boundaries that batch processing provides. Aggregations that span time windows need to handle late-arriving events correctly. Stateful operations need to manage memory carefully as state accumulates. Exactly-once processing semantics need to be maintained across system restarts and failures. The processing rate needs to keep up with the ingestion rate under all load conditions, including traffic spikes.
We build stream processing systems in Rust where the throughput and latency requirements demand it ā achieving processing rates and latency characteristics that higher-level stream processing frameworks cannot match when the data volumes are high enough. For workloads where the operational familiarity of established frameworks outweighs the performance advantage of custom implementations, we design and implement on the appropriate platform.
Micro-Batch Processing Micro-batch processing sits between pure streaming and traditional batch ā accumulating events over short time windows (seconds to minutes) and processing them together. It trades the minimal latency of true streaming for simpler state management and more efficient processing of related events together. For many real-world applications ā analytics pipelines, aggregation workloads, reporting feeds ā micro-batch is the right trade-off.
Parallel and Distributed Processing When a single processing node cannot handle the required throughput or when processing time on a single node would exceed acceptable limits, distributing work across multiple compute nodes becomes necessary. We design parallel and distributed processing architectures that partition work effectively, coordinate across nodes without creating bottlenecks, handle node failures gracefully, and scale horizontally as data volumes grow.
Performance Engineering
Raw processing throughput is determined by a combination of algorithmic efficiency, data structure choices, parallelism, and the efficiency of the runtime executing the code. We approach performance engineering at all of these levels:
Algorithmic efficiency. The choice of algorithm matters more than implementation language at large scale. O(n log n) versus O(nĀ²) is the difference between a system that handles a tenfold data volume increase with modest resource growth and one that becomes unusable. We select algorithms appropriate to the data volumes and access patterns of each processing workload, and we validate performance characteristics against representative data volumes before deployment.
Data structure selection. Cache-friendly data structures that minimise memory allocations and maximise CPU cache utilisation make a significant difference in processing throughput for high-volume workloads. In Rust particularly, the ability to control memory layout precisely enables data structure choices that are not available in garbage-collected languages ā and the performance difference at high throughput is substantial.
Parallelism. Modern compute resources have multiple CPU cores. Processing systems that do not use them leave performance on the table. We implement data parallelism ā partitioning datasets across cores and processing partitions concurrently ā and task parallelism ā executing independent processing stages concurrently ā where the processing logic allows it. Rust's ownership model makes data-parallel processing safe without the data race risks that parallel programming in other languages requires careful discipline to avoid.
I/O efficiency. Data processing systems spend a significant fraction of their time on I/O ā reading from databases, writing to storage, communicating with upstream and downstream systems. We use asynchronous I/O throughout our Rust implementations, allowing processing to continue while I/O operations complete rather than blocking on each operation sequentially.
Memory efficiency. Processing large datasets without loading them entirely into memory requires streaming processing approaches that handle data in chunks. We design processing pipelines to operate with bounded memory usage regardless of input size ā essential for production systems where memory pressure affects all processes on the host.
Cloud Compute for Elastic Scale
Some data processing workloads are inherently elastic ā the compute required varies dramatically based on data volumes that fluctuate over time. A processing system sized for peak load wastes resources during off-peak periods. A system sized for average load falls behind during peaks. Cloud compute infrastructure provides the elasticity to match compute resources to actual workload.
AWS EC2 and Auto Scaling For processing workloads that run continuously or on schedule, we deploy on AWS EC2 instances sized appropriately for the workload ā with Auto Scaling groups that add instances during high-load periods and terminate them when load decreases. Processing work is distributed across the instance pool through work queues, with each instance pulling and processing jobs independently.
Spot instances reduce compute costs significantly for batch processing workloads that can tolerate interruption ā with checkpoint mechanisms that allow interrupted processing jobs to resume on a new instance rather than starting from scratch.
AWS Lambda for Event-Driven Processing For event-triggered processing workloads where the event rate is variable and the processing duration per event is bounded, serverless compute through AWS Lambda eliminates the need to provision and manage processing infrastructure at all. Each event triggers an independent Lambda invocation, the platform scales automatically to the event rate, and costs are proportional to actual processing rather than provisioned capacity.
Lambda is the right choice for processing workloads triggered by file arrivals in S3, messages in SQS queues, records in DynamoDB streams, or API Gateway events ā where the processing logic is self-contained per event and the invocation model maps naturally to the trigger pattern.
AWS S3 for Data Storage at Scale Large-scale data processing requires storage that scales with data volumes without operational overhead. AWS S3 provides effectively unlimited storage at low cost, with the throughput characteristics to support high-rate data ingestion and retrieval. We use S3 as the primary storage layer for large-scale processing pipelines ā storing raw input data, intermediate processing outputs, and final results in Parquet or other columnar formats that support efficient analytical queries against stored data.
AWS SQS for Decoupled Processing Queuing between processing stages decouples producers from consumers, buffers demand spikes, and enables independent scaling of each stage. AWS SQS provides durable, scalable queuing that handles the message volumes of high-throughput processing pipelines with visibility timeout management, dead letter queue routing for failed processing attempts, and FIFO ordering where processing sequence matters.
Hetzner and VPS for Predictable Workloads For processing workloads with predictable, stable resource requirements, dedicated or virtual private server infrastructure on providers like Hetzner delivers better price-to-performance than cloud compute for equivalent resources ā without the operational complexity of cloud infrastructure management. Our processing services run as managed systemd services on Linux, with the monitoring and alerting infrastructure to maintain visibility into processing health.
Data Processing Use Cases We Build For
The range of data processing workloads we have designed and implemented spans every sector we serve:
Financial data processing. Trade record processing and P&L calculation across large position histories. Reconciliation processing that matches records across multiple financial systems at scale. Risk calculation engines that process portfolio data to produce exposure metrics. Regulatory reporting pipelines that aggregate and format transaction data to meet reporting obligations.
Market data processing. High-frequency market data ingestion from exchange feeds ā tick data, order book updates, trade records ā processed at the rates exchange feeds deliver. Historical market data processing for backtesting pipelines that need to process years of tick data efficiently. Price calculation and aggregation across multiple instruments and time horizons.
Ecommerce data processing. Order processing pipelines that handle high-volume order flows across multiple channels. Inventory calculation engines that maintain accurate stock levels across warehouses as orders, receipts, and adjustments flow through. Product data processing pipelines that normalise, enrich, and distribute product information across sales channels and feeds.
Analytics and reporting. Event processing pipelines that aggregate user behaviour data from large application user bases into the metrics that power product analytics. Reporting pipelines that aggregate data from multiple operational systems into the consolidated views that management reporting requires. Cohort analysis and segmentation processing that runs complex analytical workloads against large user datasets.
Blockchain and onchain data. Block and transaction data ingestion from blockchain nodes ā processing the full history of a chain or maintaining a real-time index of new blocks as they are produced. Event log processing that extracts and decodes smart contract events into structured records. Token transfer and balance calculation engines that maintain accurate holder records across large transaction histories.
Trading system data. Position and exposure calculation engines that process trade records to maintain accurate real-time position data. Performance attribution processing that calculates returns and risk metrics across portfolios. Strategy backtesting engines that process historical market data at the throughput rates that make large-scale parameter optimisation tractable.
Data Quality at Scale
Processing data at scale without validating it produces incorrect outputs at scale. Data quality enforcement is built into every processing system we deliver:
Schema validation at ingestion boundaries ensures that data arriving from upstream systems conforms to expected structures before it enters the processing pipeline ā catching format changes and data quality issues at the source rather than discovering their effects downstream.
Statistical validation detects anomalies that pass schema validation but are statistically implausible ā prices outside expected ranges, volumes that exceed historical maximums by orders of magnitude, timestamps that are implausibly far in the past or future. These checks catch data quality issues that are not format errors but are still wrong.
Referential integrity validation ensures that records that reference other records ā orders referencing products, transactions referencing accounts, events referencing users ā reference records that actually exist in the expected state. Missing references are caught and handled explicitly rather than causing downstream processing failures.
Completeness monitoring tracks whether expected data has arrived ā detecting missing files, gaps in time series data, and lower-than-expected record counts that indicate upstream data delivery problems rather than legitimate quiet periods.
Technologies Used
- Rust ā performance-critical processing engines, high-throughput stream processing, memory-efficient batch processing, binary data parsing
- C# ā enterprise data workloads, Excel and file processing, Microsoft ecosystem data integration, complex business logic processing
- SQL (PostgreSQL, MySQL, SQLite) ā structured data storage, processing state management, analytical query layers
- Parquet / columnar formats ā efficient large-scale analytical data storage and retrieval
- Redis ā high-speed intermediate data storage, processing state, deduplication tracking
- AWS EC2 / Auto Scaling ā elastic compute for variable-load processing workloads
- AWS Lambda ā serverless event-driven processing for triggered workloads
- AWS S3 ā scalable object storage for large-scale data pipeline inputs and outputs
- AWS SQS ā durable message queuing for decoupled processing pipeline stages
- Hetzner / VPS ā dedicated infrastructure for stable, predictable processing workloads
- REST / WebSocket ā upstream data source and downstream system connectivity
Choosing the Right Processing Architecture
Not every data processing requirement needs the same solution. A startup processing thousands of records daily has different architecture needs than an enterprise processing billions. A financial system with strict latency requirements needs different design choices than an analytics pipeline where overnight completion is acceptable.
We start every data processing engagement by understanding the actual requirements ā current data volumes and projected growth, latency and freshness requirements, consistency and correctness guarantees needed, operational constraints, and budget. From this we design the architecture that is appropriate to the requirement ā not the most technically impressive architecture, not the most familiar one, but the one that delivers what is actually needed at a cost that makes sense.
Process Your Data. At Any Scale.
Whether you are processing thousands of records or billions, running batch jobs on a schedule or ingesting continuous streams in real time, operating on a single server or across a cloud compute cluster ā we build the processing infrastructure that handles it correctly.