Last updated October 8, 2026

Data Engineering Services

Build event pipelines and data integrations with explicit contracts, replay checks and an operations handover.

Who this service fits

Platform teams replacing fragile event consumers or manual exports
Product teams connecting live sources to reporting or application decisions

90+ engineers
28 industries
13+ years in business

107 reviews

141 reviews

13 reviews

54 reviews

27 reviews 5.0 342+ verified reviews across five platforms

Your business results matter

Achieve them with minimized risk through our bespoke innovation capabilities

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Last updated

October 8, 2026 by Dmytro Nasyrov, Founder and CTO. Content reflects Pharos Production delivery data as of that date. Editorial policy.

Practice led by Dmytro Nasyrov

Founder and CTO

23+ years in custom software development. Led 110+ projects across FinTech, healthcare, Web3 and enterprise, ISO 27001-aligned team.

Data pipelines grounded in published delivery

The Pro Gambling case describes live match and odds ingestion with Kafka and Flink. Sagas applies event processing to uploads and location changes. These projects provide the delivery context for our data engineering offer: connect sources, process their events and expose data to applications or reporting.

Start with a single business decision and its required sources. We define the event contract, transformations and failure handling before choosing a processing stack. A new pipeline can sit beside an existing platform. A complete warehouse replacement is a separate scope.

What we do not do

Business owners approve definitions and data access. Engineering cannot infer an authoritative business metric from conflicting sources.
A warehouse migration, BI redesign or model-training program requires a separate specification.
Latency, retention and support commitments are agreed for the actual workload.

Define the pipeline before estimating it

Inputs: Source inventory, sample events, existing schemas and the receiving application or report.
Deliverables: Source adapters, transformation code, data contracts and an acceptance report, with deployment and recovery instructions.
Client decisions: System of record, field ownership, retention policy and an escalation owner for rejected records.
Cost drivers: Source access, schema differences, backfill size, change frequency and recovery requirements. Pricing is scoped after discovery.

Scheduled transfers or event processing?

Choose the simpler approach that meets the receiving system's freshness requirement. Live processing adds operational work. A daily report may not need it.

Factor	Scheduled transfers	Event processing
Business trigger	A report can wait for an agreed refresh window.	A new event must update an application or decision promptly.
Source access	Exports, database reads or APIs can be sampled in batches.	The source provides events or a suitable change stream.
Recovery	Rerun a bounded transfer and compare the resulting totals.	Replay from a checkpoint while preventing duplicate side effects.
Operating model	Track completed runs and missed refreshes.	Monitor consumer lag, rejected events and downstream availability.

An illustrative pipeline acceptance checklist

For a live odds feed, a review record can follow source_event_id -> event_time -> schema_version -> transformed_record -> consumer_result. This is an example of a test artifact, not a client dataset.

Source and contract

Supply valid and malformed samples. Verify required fields, timestamps and version handling. Unknown fields must follow a documented compatibility policy instead of silently breaking a consumer.
Replay and correction

Replay a known event window. Confirm that duplicates do not create extra downstream records. Include a late correction and show which value becomes authoritative.
Partial failure

Interrupt a consumer or source connection. Record the recovery position, retry behavior and rejected records. Demonstrate that an operator can identify the affected range.
Reconciliation and ownership

Compare an agreed source sample with the destination. Investigate missing or changed records and assign an exception owner. Deliver the mapping, test evidence and recovery runbook.

Published record

Published Pharos research

Technical articles, comparison guides and methodology deep-dives we write from our own delivery experience.

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Consensys
Gate Io
Coinbase
Ludo
Core Scientific
Debut Infotech
Axoni
Alchemy
Starkware
Mara Holdings
MicroStrategy
Nubank
Okx
Uniswap
Riot
Leeway Hertz

Source adapters

Connect the agreed APIs, event feeds or exports. Document credentials handling and the limits imposed by each source.

Processing and quality

Transform records into the receiving contract. Route malformed inputs to an inspectable exception flow and test schema changes.

Operations handover

Provide deployment instructions, monitoring checks and a recovery runbook. Name the responsibilities shared with your platform team.

Dmytro Nasyrov

Founder and CTO Pharos Production

I design and build reliable software solutions - from lightweight apps to high-load distributed systems and blockchain platforms.

PhD in Artificial Intelligence, MSc in Computer Science (with honors), MSc in Electronics & Precision Mechanics.

13 years in architecture of great software solutions tailored to customer needs for startups and enterprises
23 years of practical enterprise customized software production experience
Lecturer at the National Kyiv Polytechnic University
Doctor of Philosophy in Artificial Intelligence
Master's degree in Computer Science, completed with excellence
Master's degree in Electronics and precision mechanics engineering

Pipeline assessment

Inspect an existing flow or define a new source-to-consumer contract. Deliver a prioritized scope and an implementation proposal. Best for: Teams deciding what to repair or build.

Scoped after discovery

Popular choice

Pipeline implementation

Build the agreed adapters and processing flow. Complete replay, failure and reconciliation tests before handover. Best for: An approved pipeline with accessible sources.

Scoped after discovery

Modernization and backfill

Change an existing flow in stages. Agree a bounded historical dataset and compare the destination before retiring the old path. Best for: Platforms with an existing data workload.

Scoped after discovery

Prices vary based on project scope, complexity, timeline and requirements. Contact us for a personalized estimate.

Request staff augmentation

Need extra hands on your software project? Our developers can jump in at any stage - from architecture to auditing - and integrate seamlessly with your team to fill any technical gaps.

Popular choice

Hire dedicated experts

Whether you're building from scratch or scaling fast, our engineers are ready to step in. You stay in control, and we handle the code.

Outsource your project

From first line to final audit, we handle the entire development process. We will deliver secure, production-ready software, while you can focus on your business.

Our engineers work with 187+ technologies across 10 categories: Frameworks, AI, Blockchains, DevOps, Clouds, Databases, Brokers, Tests, Programming, UI/UX.

Frameworks: Backend Frameworks: Spring Boot, Erlang OTP, NodeJS, Phoenix, NestJS, Django, FastAPI, Express.js; Front End Frameworks: React, Next.JS, Svelte, Angular, Vue.js, Remix, Astro, Nuxt.js; Mobile Apps Frameworks: iOS, Android, Flutter, React Native, Capacitors, Ionic, Swift, Kotlin, Java, Dart
AI: LLM Providers: OpenAI GPT, Anthropic Claude, Google Gemini, Meta Llama, Mistral AI, Cohere, Ollama, xAI Grok; AI Frameworks: LangChain, LangGraph, CrewAI, AutoGen, Hugging Face, PyTorch, TensorFlow, scikit-learn, LlamaIndex, Keras, XGBoost, LightGBM, OpenCV, spaCy, ONNX Runtime; Vector Databases: Pinecone, Weaviate, Qdrant, Chroma, pgvector, Milvus, FAISS; MLOps and Infrastructure: MLflow, Weights & Biases, DVC, Kubeflow, AWS SageMaker, Azure ML, Google Vertex AI, NVIDIA Triton, Airflow, Ray Serve, vLLM; AI Agent Tools: OpenAI Agents SDK, Claude MCP, Semantic Kernel, Haystack
Blockchains: Private and Public Blockchains: Ethereum, TON, Corda, Tron, Hedera, Stellar, Consensys GoQuorum, Solana, Arbitrum, Binance Smart Chain (BSC), Sei, Celo, Hyperledger, MultiversX, IOTA, Polkadot, Aptos, Neo, Flow, Algorand, Avalanche, EOS, Optimism, Polygon, Cosmos, Sui, Tezos, Ontology, Fantom, NEAR Protocol, VeChain, Base, IPFS; Cloud Blockchain Solutions: Amazon Managed Blockchain, Amazon QLDB, IBM Blockchain, Oracle Blockchain
DevOps: DevOps Tools: Kubernetes, Terraform, Docker, Istio, Prometheus, Grafana, Jenkins, ArgoCD, Ansible, GitHub Actions, GitLab CI, Pulumi, Datadog, New Relic, Vault
Clouds: Clouds: Amazon Web Services, Azure, Google Cloud, Cloudflare, Vercel, DigitalOcean
Databases: Databases: PostgreSQL, MySQL MariaDB, Redis, Cassandra, Neo4J, MongoDB, Elasticsearch, Solr, Ignite, ClickHouse, TimescaleDB, DynamoDB, Supabase, CockroachDB, ScyllaDB
Brokers: Event and Message Brokers: Kafka, RabbitMQ, Flink, Apache Pulsar, Amazon SQS, Amazon SNS, NATS
Tests: Test Automation Tools: Postman, Appium, Cucumber, Selenium, JMeter, Cypress
Programming: Programming Languages: Solidity, FunC, Rust, GoLang, Elixir, Erlang, C++, Java, JavaScript, TypeScript, Scala, Python, C#, .NET, PHP, Ruby, Dart, SQL
UI/UX: UI/UX Design Tools: Figma, Zeplin, InVision, Sketch, Miro, Marvel, Balsamiq, Photoshop, Illustrator, XD, After Effects, Corel Draw

Backend Frameworks 8

Spring Boot

Erlang OTP

NodeJS

Phoenix

NestJS

Django

FastAPI

Express.js

Front End Frameworks 8

React

Next.JS

Svelte

Angular

Vue.js

Remix

Astro

Nuxt.js

Mobile Apps Frameworks 10

Capacitors

Ionic

65+ industry awards

2 days

Team Assembly

Our company starts and assembles an entire project specialists with the perfect blend of skills and experience to start the work.
1-4 months

MVP

We'll design, build and launch your MVP, ensuring it meets the core requirements of your software solution.
6-12 months

Production

We'll create a complete software solution that is custom-made to meet your exact specifications.
Ongoing

Continuous Support

Our company will be right there with you, keeping your software solution running smoothly, fixing issues and rolling out updates.

Proven Results

Pro Gambling. Sports Forecasting Platform

Sagas. Time-lapse Social Network

Lucky Bets. Casino Games Aggregator

View all case studies →

A migration team in a cutover war room watching a replication-lag dashboard, one engineer holding a phone ready to call a rollback.

Legacy Data Migration Strategy

Google Cloud's own migration guidance states the constraint every cut-over plan has to accept: genuinely zero downtime is impossible. This guide works through the decision a legacy data migration actually turns on, big bang against outbox-based trickle against log-based change data capture, why a dual write without a transactional outbox can silently diverge two databases, reconciliation tiered from row counts to field-level checks and the rehearsal and rollback discipline AWS's own cutover guidance describes.

Dmytro Nasyrov Sep 18, 2026 24 min read

Read

Macro of a printed source document marked with cut lines at its section breaks and one exception clause bracketed back to the rule above it.

RAG Data Pipeline

A RAG corpus is a live system with state, not a build artifact. This piece traces one document through arrival, change, duplication, deletion and embedding model migration, and shows where five major vector stores document contradictory answers to the same event.

Dmytro Nasyrov Aug 24, 2026 25 min read

Read

View all articles

Questions that change a data engineering scope

Last updated: October 8, 2026

Can you repair an existing pipeline instead of replacing it?

Yes. Begin with a failing run or event sequence, the current code and access to a safe test environment.
The assessment separates source defects from transformation and consumer failures. If a bounded repair meets the requirement, it can be commissioned without a platform rebuild.
Do Kafka and Flink have to be part of our stack?

No. They appear in the published cases linked here. Your sources, receiving systems and operating team determine the implementation.
An API transfer or a managed pipeline may be a better fit than adding a streaming platform.
How is data engineering different from software integration?

Use this engagement when the main deliverable is a data pipeline with transformation, validation and recovery evidence. Choose software integration when the main requirement is an operational workflow between applications.
The scope can include both, with separate acceptance criteria.
Can the same engagement include historical data?

A backfill is included only when its source range and validation method are agreed. We identify records that can be reconstructed and exceptions that need business decisions.
An unbounded historical migration is estimated separately from the live pipeline.
What is needed for a proposal?

Provide the source systems, destination, representative schemas and the required refresh behavior. State who can grant test access and approve the data definitions.
The proposal names deliverables and dependencies, with acceptance tests recorded separately. It does not assume a budget from source count alone.

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Can you repair an existing pipeline instead of replacing it?

Yes. Begin with a failing run or event sequence, the current code and access to a safe test environment. The assessment separates source defects from transformation and consumer failures. If a bounded repair meets the requirement, it can be commissioned without a platform rebuild.

Do Kafka and Flink have to be part of our stack?

No. They appear in the published cases linked here. Your sources, receiving systems and operating team determine the implementation. An API transfer or a managed pipeline may be a better fit than adding a streaming platform.

How is data engineering different from software integration?

Use this engagement when the main deliverable is a data pipeline with transformation, validation and recovery evidence. Choose software integration when the main requirement is an operational workflow between applications. The scope can include both, with separate acceptance criteria.

Can the same engagement include historical data?

A backfill is included only when its source range and validation method are agreed. We identify records that can be reconstructed and exceptions that need business decisions. An unbounded historical migration is estimated separately from the live pipeline.

What is needed for a proposal?

Provide the source systems, destination, representative schemas and the required refresh behavior. State who can grant test access and approve the data definitions. The proposal names deliverables and dependencies, with acceptance tests recorded separately. It does not assume a budget from source count alone.

Bring a source and a decision

Describe which data arrives, who needs it and what fails today. We will scope a pipeline or a bounded repair around that workload. For operational application workflows, see software integration.

Dmytro Nasyrov, Founder and CTO at Pharos Production

Dmytro Nasyrov Founder & CTO Let's work together!

Scope your data pipeline

Tell us the sources, destination and refresh requirement. Use representative schemas rather than confidential production records.

Your contact details

Name Please enter your name

Telegram / WhatsApp

Email Please enter a valid email address

Message Please enter your message

Need NDA

We use your details only to reply to your request. Data Privacy and Legal Notice

We typically reply within 24 hours

hello@pharosproduction.com

Contact us

Contact us today to discuss your project. We're ready to review your request promptly and guide you on the best next steps for collaboration
Same day
NDA

We're committed to keeping your information confidential, so we'll sign a Non-Disclosure Agreement
1 day
Plan the Goals

After we chat about your goals and needs, we'll craft a comprehensive proposal detailing the project scope, team, timeline and budget
3-5 days
Finalize the Details

Let's connect on Google Meet to go through the proposal and confirm all the details together!
1-2 days
Sign the Contract

As soon as the contract is signed, our dedicated team will jump into action on your project!
Same day

Headquarters in Las Vegas, Nevada. Engineering office in Kyiv, Ukraine.

We also work with clients through dedicated local teams in Las Vegas, New York and San Francisco.

5348 Vegas Dr, Las Vegas, NV 89108, United States

44-B Eugene Konovalets Str. Suite 201, Kyiv 01133, Ukraine

Data Engineering Services

Data pipelines grounded in published delivery

Define the pipeline before estimating it

Scheduled transfers or event processing?

An illustrative pipeline acceptance checklist

Source and contract

Replay and correction

Partial failure

Reconciliation and ownership

Published Pharos research

Platforms we work with

Pipeline assessment

Pipeline implementation

Modernization and backfill

Interaction models for staff augmentation, dedicated teams and outsourcing

Request staff augmentation

Hire dedicated experts

Outsource your project

Frameworks

Backend Frameworks 8

Front End Frameworks 8

Mobile Apps Frameworks 10

AI and Machine Learning

LLM Providers 8

AI Frameworks 15

Vector Databases 7

MLOps and Infrastructure 11

AI Agent Tools 4

Blockchains

Private and Public Blockchains 33

Cloud Blockchain Solutions 4

DevOps

DevOps Tools 15

Clouds

Clouds 6

Databases

Databases 15

Brokers

Event and Message Brokers 7

Tests

Test Automation Tools 6

Programming

Programming Languages 18

UI/UX

UI/UX Design Tools 12

Team Assembly

MVP

Production

Continuous Support

Proven Results

Pro Gambling. Sports Forecasting Platform

Sagas. Time-lapse Social Network

Lucky Bets. Casino Games Aggregator

Legacy Data Migration Strategy

RAG Data Pipeline

Questions that change a data engineering scope

Can you repair an existing pipeline instead of replacing it?

Do Kafka and Flink have to be part of our stack?

How is data engineering different from software integration?

Can the same engagement include historical data?

What is needed for a proposal?

Bring a source and a decision

Scope your data pipeline

1 Contact us

2 NDA

3 Plan the Goals

4 Finalize the Details

5 Sign the Contract

Contact us

NDA

Plan the Goals

Finalize the Details

Sign the Contract