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プライバシーポリシー利用規約
Scale • Backend & Distributed Systems

Backend &
Distributed Systems

Engineer high-throughput, reliable backend systems and distributed architectures for demanding workloads. We build the infrastructure that powers your product at scale.

Discuss Your Backend ChallengeView Case Studies
25+
Engineers
40+
Clients
58
Case Studies
Backend
Distributed Systems • Cloud • Data
Distributed
High Avail.
Data Layer
Petabyte
Latency
<50ms P99
Architecture
Event-Driven

Backend Systems We Build

From high-throughput APIs and event-driven architectures to real-time systems and distributed task processing -- we engineer backend systems built for scale.

API Platforms

High-throughput API platforms (REST, GraphQL, gRPC) serving thousands to millions of requests per second

Event-Driven Systems

Event-driven backends using event sourcing, CQRS and message-based communication for high-volume async events

Real-Time Services

Real-time backend services for live dashboards, collaborative features, notifications and streaming data

Engineering Capabilities

Deep expertise across API architecture, distributed systems, data engineering, performance optimization and reliability engineering.

API Architecture

  • REST and GraphQL API design
  • API gateway and rate limiting
  • Versioning and backward compatibility
  • Authentication and authorization

Backend Engineering Approach

A structured approach to backend system development -- from architecture assessment through design, build, production hardening and scale.

01

Architecture Assessment

Analyze current system, identify bottlenecks, map dependencies and evaluate scaling requirements.

02

System Design

Design target architecture with service boundaries, data models, API contracts and infrastructure plan.

03

Proof of Concept

Validate approach on the highest-risk component -- performance, data consistency or integration.

04

Iterative Development

Build in sprints with continuous load testing and performance validation.

05

Integration & Migration

Connect with existing systems, migrate data and implement backward compatibility.

06

Production Hardening

Load testing, chaos engineering, monitoring setup and incident response procedures.

Technology Stack

The exact stack is selected based on system requirements -- not a fixed technology mandate.

Backend

🐍Python / FastAPI
🟢Node.js / NestJS
🔷Golang
TSTypeScript
☕Java / Spring Boot

Messaging

📨Apache Kafka

Related Solutions

Explore related capabilities that complement backend and distributed systems engineering.

Cloud Performance Engineering

Optimize cloud infrastructure costs, auto-scaling and performance for your backend systems.

Explore

AI Product Engineering

Build AI-powered products with production backend infrastructure and distributed AI workloads.

Explore

Backend Use Cases

We engineer backend systems across industries and use cases -- from API scalability and event-driven processing to multi-region architectures and data pipelines.

API Scalability

Scale APIs from hundreds to thousands of requests per second

Event-Driven Processing

Build async event processing for order fulfillment, notifications, analytics

Microservices Migration

Decompose monoliths into independent, scalable backend services

Real-Time Data Streaming

Live updates, notifications and real-time dashboards

Search Infrastructure

High-performance search and filtering across large datasets

Background Job Processing

Distributed task queues for batch processing, imports and exports

Data Pipeline Architecture

ETL pipelines, data transformation and analytics infrastructure

Multi-Region Architecture

Distribute services across regions for latency and availability

Engineered for Scale

We engineer backend systems for production -- optimized for throughput, latency, reliability and observability at scale.

Throughput

Horizontal scaling, load balancing, connection pooling and async processing

Latency

Query optimization, caching, CDN and efficient serialization

Reliability

Circuit breakers, retry logic, graceful degradation and failover

Engineering Team Model

The team is structured around your system requirements, not a fixed package. Team composition adapts based on architecture needs.

2-Person Squad

Focused backend initiative. Targeted API development, performance optimization or specific service build.

3-Person Squad

Substantial backend module development. Microservices, event-driven systems or data-intensive features.

5-Person Squad

Full backend engineering with Technical Lead, Backend Engineers, DevOps and QA.

How We Work Together

A flexible engagement model that grows with your system -- from initial assessment to long-term engineering partnership.

1

Architecture Assessment

Understand your system, identify bottlenecks, evaluate scaling requirements

2

System Design & POC

Design the target architecture, validate with a proof of concept on the highest-risk component

3

System Build

Develop the backend systems with a dedicated engineering team

4

Backend Engineering Team

Ongoing team embedded in your engineering organization for continuous development

5

Relevant Case Studies

SCTE-35 Ad Marker Signaling & Media Trailer Insertion Pipeline
Video Encoding

SCTE-35 Ad Marker Signaling & Media Trailer Insertion Pipeline

A streaming media company needed a robust, automated pipeline for injecting SCTE-35 ad markers into live and VOD streams, along with the ability to insert promotional trailers (pre-roll, mid-roll, and post-roll) at precisely timed positions — enabling monetization across FAST channels, live events, and on-demand content libraries.

AWS Elemental MediaLiveAWS Elemental MediaConvertAWS Elemental MediaPackage

Facing a Backend Challenge?

Tell us about your system, where it is struggling and what scale you need to reach. We will assess the architecture and recommend the right approach.

Discuss Your Backend ChallengeView Case Studies

Data Processing Pipelines

Data processing pipelines that transform, enrich and route data at scale - batch, stream and hybrid architectures

Microservices Architectures

Decompose monolithic backends into well-bounded microservices with proper data ownership and operational tooling

High-Availability Systems

Multi-region deployment, automatic failover, health checking, circuit breakers and graceful degradation

Integration Platforms

Backend platforms integrating multiple third-party APIs, legacy systems and data sources through standardized interfaces

Backend for AI Products

Backend architectures supporting AI inference, model serving, queue-based AI processing and hybrid sync/async API patterns

API documentation and SDKs

Distributed Systems

  • Event-driven architecture
  • Message queues (Kafka, SQS, RabbitMQ)
  • Service-to-service communication
  • Distributed transactions and sagas
  • Consistency and partition tolerance

Data Architecture

  • Database schema design and optimization
  • Read/write splitting and replication
  • Caching strategies (Redis, CDN)
  • Search indexing (Elasticsearch)
  • Data partitioning and sharding

Performance Engineering

  • Load testing and benchmarking
  • Latency optimization
  • Connection pooling and resource management
  • Query optimization
  • Horizontal and vertical scaling

Reliability Engineering

  • Circuit breakers and retry patterns
  • Graceful degradation
  • Health checks and failover
  • Monitoring and alerting
  • Incident response and post-mortems
07

Scale & Optimize

Performance tuning, infrastructure scaling, cost optimization and capacity planning.

📬
AWS SQS / SNS
🐇RabbitMQ
🔴Redis Pub/Sub

Data & Storage

🐘PostgreSQL
🍃MongoDB
🔴Redis
🔍Elasticsearch
⚡DynamoDB

Infrastructure

☁️AWS
🐳Docker / Kubernetes
🏗️Terraform
🔄GitHub Actions
⚖️Load Balancers

Monitoring

📊Datadog
📈Prometheus / Grafana
👁️AWS CloudWatch
🛡️Sentry
🔍ELK Stack

Software Modernization

Decompose monoliths, modernize legacy backends and migrate to microservices architecture.

Explore

Database Optimization

Query optimization, indexing, caching and read replica strategies

Integration Architecture

Reliable integration with third-party APIs, webhooks and data sources

Data Consistency

Transaction management, eventual consistency patterns and data validation

Observability

Distributed tracing, metrics, logging, alerting and SLO tracking

Security

API authentication, rate limiting, input validation and data encryption

Production Reference

Engineered backend systems handling millions of daily API requests with sub-100ms latency, 99.9%+ availability and automated scaling for demand spikes.

Custom Team

Team composition adapts to system requirements. Can include data, infrastructure or domain specialists.

Long-Term Partnership

Strategic technology partner for system evolution and engineering scale

Fixed-scope engagements are available when requirements are sufficiently defined. The right investment depends on system scope, technical complexity, team composition and roadmap.

+9
Read Case Study
AWS Media Services for FAST Channel Streaming over SRT
Video Encoding

AWS Media Services for FAST Channel Streaming over SRT

A media company needed to establish reliable, low-latency contribution feeds for their FAST channels using the Secure Reliable Transport (SRT) protocol — enabling high-quality content ingest from remote studios, cloud playout systems, and syndication partners over unpredictable internet connections.

AWS Elemental MediaConnectAWS Elemental MediaLiveAWS Elemental MediaPackage+7
Read Case Study
AWS Media Services for FAST Channel Streaming over HLS
Video Encoding

AWS Media Services for FAST Channel Streaming over HLS

A media company needed to launch Free Ad-Supported Streaming Television (FAST) channels — 24/7 linear streams of curated video content delivered over HLS to smart TVs, set-top boxes, and web/mobile players, monetized through programmatic ad insertion.

AWS Elemental MediaLiveAWS Elemental MediaPackageAWS Elemental MediaTailor+8
Read Case Study
View All Case Studies