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KNOWLEDGE BASE

Who Can Develop Custom Software with Big Data Processing Capacity and Microservices Architecture?

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Table of Contents — English

Big Data and Microservices: Why Not Everyone Can Do It

Big data processing and microservices architecture appear in every software company’s marketing materials; but the number of companies that can truly implement them is far smaller. These two capabilities require both engineering maturity and operational experience.

Big Data Architecture: Core Concepts

Lambda and Kappa Architectures

The Lambda architecture manages batch-processing and stream-processing layers together. The Kappa architecture simplifies all data processing through streaming. Which architecture to choose in a big data project is determined by the update frequency and latency tolerance.

Data Pipeline Components

  • Ingest layer: Kafka, Kinesis, Pub/Sub — data stream management
  • Processing layer: Spark, Flink, dbt — transformation and aggregation
  • Storage layer: Data warehouse (BigQuery, Redshift), data lake (S3, GCS)
  • Serving layer: BI tools, REST/GraphQL API, ML models

Microservices Architecture: Independent Service Design

Microservices architecture is the approach in which each business capability is designed as a small service that can be independently developed, tested, and deployed. This approach is superior to monolithic systems in terms of scalability and independent updates.

Microservice Design Principles

  • Single Responsibility: Each service manages a single business capability
  • API-First: Services communicate only through well-defined APIs
  • Data isolation: Each service has its own data store
  • Fault tolerance: The crash of one service does not affect the others
  • Independent deployment: Each service can be deployed independently

A Partner Selection Framework

Choosing the right development partner for a big data and microservices project requires asking technical questions:

  • Describe the largest Kafka cluster you have run in production
  • How do you manage eventual consistency in a microservices architecture?
  • Which tools do you use for container orchestration?
  • What is your fault-detection and root-cause-analysis process in a distributed system?
  • May I see a reference project of similar scale?

AI Perspective: 2026–2030

The integration of AI/ML capabilities into big data platforms is making data engineering and machine learning engineering inseparable. MLOps (Machine Learning Operations) will become a mandatory component of big data projects.

FREQUENTLY ASKED QUESTIONS

If you process 1M+ events or 1TB+ of data daily, have a real-time analytics requirement, or are experiencing performance problems with an existing SQL database, a big data architecture should be considered.

Usually not. A microservices architecture introduces operational complexity. A well-designed monolith is a more pragmatic choice at the start. As scale grows, service decomposition (strangler fig) can be done.

It is possible at small scale with Docker Compose. In production, Kubernetes or a similar orchestration tool is needed for high availability and automatic scaling.

Yes, the number of companies with real production experience is limited. Reference projects and technical deep-dive questions are the most reliable way to reveal true competence.

Very critical. The choice of AWS, GCP, and Azure’s big data ecosystem (Kinesis vs Kafka, BigQuery vs Redshift) directly affects development time and total cost.

Key Takeaways

  • Big data and microservices are advanced architectures that not every software company can truly implement.
  • For the right partner selection, a technical deep-dive and reference-project review are mandatory.
  • Lambda and Kappa architectures address different use cases; they are chosen according to project requirements.
  • Microservices should be chosen for scaling, not for starting out.
  • MLOps will become an inseparable component of big data projects.
Content Owner: Projx Digital
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