Apache Kafka development and consulting services
Apache Kafka development and consulting: event-driven architecture, producers and consumers in Java, .NET, Python and Go, Kafka Connect and Debezium change data capture, stream processing with Kafka Streams and Apache Flink, managed Kafka on Confluent Cloud, Amazon MSK and Azure Event Hubs, and cluster operations, migrations and upgrades, with dedicated Kafka engineers for your team or a Kafka project delivered end to end.
What we build with Kafka
Event-driven architecture on Kafka
Topics, partitions, keys and retention designed around your business events, event schemas agreed between teams, and patterns such as the transactional outbox and idempotent consumers, so an event is neither lost nor applied twice.
Kafka producers and consumers
Services in Java with Spring for Apache Kafka, .NET with Confluent.Kafka, Python and Go, with retries, dead letter topics, transactions where a duplicate would cost money, and share groups (Queues for Kafka, production-ready since Kafka 4.2) where work is spread across consumers like a queue.
Kafka Connect and Debezium
Database changes streamed into Kafka with Debezium, topics delivered to warehouses, search indexes and object storage by sink connectors, and topics materialised as Iceberg or Delta tables with Confluent Tableflow, on Confluent Cloud, MSK Connect or Strimzi.
Stream processing with Flink and Kafka Streams
Joins, windowed aggregations and enrichment in Kafka Streams inside your Java services, or in Apache Flink jobs and Flink SQL on Confluent Cloud, Amazon Managed Service for Apache Flink or your own cluster, for fraud checks, live metrics and alerts.
Confluent Cloud, MSK and Event Hubs
Managed Kafka on Confluent Cloud, Amazon MSK with Express brokers or MSK Serverless, Azure Event Hubs through its Kafka endpoint, Aiven or Redpanda, chosen for throughput, retention, region and cost, and provisioned in Terraform.
Self-managed Kafka on Kubernetes
Kafka 4 clusters running on KRaft without ZooKeeper, on Kubernetes with the Strimzi operator or on virtual machines, with rack-aware replicas, tiered storage, Cruise Control for rebalancing and rolling upgrades without downtime.
Schema Registry and data contracts
Avro, Protobuf or JSON Schema in Confluent Schema Registry, Apicurio Registry or AWS Glue Schema Registry, with compatibility rules checked in CI so a producer cannot break its consumers with a change.
Kafka migrations and upgrades
ZooKeeper clusters moved to KRaft before the upgrade to Kafka 4, workloads moved off RabbitMQ, ActiveMQ or IBM MQ, and clusters moved between self-managed Kafka, MSK and Confluent Cloud with MirrorMaker 2 or Cluster Linking, consumer offsets included.
Kafka monitoring and security
Consumer lag, under-replicated partitions and throughput in Prometheus and Grafana or Confluent Control Center, TLS on every connection, SASL with OAuth or mutual TLS, ACLs per service, and topic, quota and ACL changes made through code review.
Hire Kafka engineers
Dedicated Kafka engineers
Kafka engineers who join your team full time, work in your repositories, tracker and meetings, and report to your lead. You interview them; we carry the Ukrainian contract, payroll, invoicing and leave.
Kafka project delivery
A team that takes the Kafka project from scope to release: estimate, build, tests and deployment, with a technical lead on our side who owns the plan and the quality.
Kafka support and take-overs
An existing Kafka system taken over from another team or kept running: a read-only review and a written list of risks first, then fixes and new features in order of impact.
The dedicated team page explains how specialists join your team, and the outsourcing page covers project delivery, take-overs and how we charge.
Who works on your Kafka project
Kafka engineers
Cluster design, operations, Connect and upgrades
Backend engineers
Java, .NET, Python and Go producers and consumers
Data engineers
Stream processing and lakehouse sinks
Cloud and DevOps engineers
Terraform, Kubernetes and monitoring
QA engineers
Contract and load tests for event flows
Event streaming you can operate
Design comes before code: partition keys chosen for the ordering the business needs, schemas with compatibility rules, and delivery semantics decided per flow, which for most flows means at-least-once delivery with idempotent consumers and for a few means transactions. Topics, ACLs, quotas and connectors are declared in code and reviewed like any other change.
Before go-live the cluster is load-tested at the expected peak with headroom to spare, alerts are set on consumer lag and under-replicated partitions, and runbooks cover a lost broker, a stuck consumer and replaying a topic. Upgrades are rehearsed in staging first.
Other data and AI services we provide
AI and machine learning
AI and machine learning development services: forecasting, fraud detection, recommendation systems, computer vision, NLP and speech recognition, and MLOps, with dedicated machine learning engineers or project delivery.
Generative AI development
Generative AI development services: RAG over your documents, AI agents, chatbots, LLM features, document processing, fine-tuning, evaluation and voice agents, with dedicated AI developers or project delivery.
Data engineering
Data engineering services: ETL pipelines, Spark, dbt and streaming, warehouses and lakehouses on Snowflake, Databricks, BigQuery, Redshift and Microsoft Fabric, with dedicated data engineers or project delivery.
Data analytics and Power BI
Data analytics and Power BI services: dashboards in Power BI, Tableau and Looker, product analytics, A/B testing, financial and marketing reporting, with dedicated data analysts and BI developers or project delivery.
Database development
Database development and administration services: schema design, query tuning, migrations and backups on SQL Server, PostgreSQL, MySQL, Oracle and MongoDB, with dedicated database developers and DBAs or project delivery.
Computer vision development
Computer vision development services: object detection, visual inspection, OCR, video analytics, medical imaging and models on edge devices, with dedicated computer vision engineers or project delivery.
Data annotation
Data annotation and labelling services for AI: image, video, text, audio and LiDAR labelling, RLHF and evaluation data, with quality control on every batch, from dedicated data annotators or project delivery.
MLOps
MLOps services and consulting: ML platforms, CI/CD for models, model registries, serving and autoscaling, GPU infrastructure, drift monitoring and LLMOps, with dedicated MLOps engineers or project delivery.
Databricks development
Databricks development and consulting: lakehouse set-up, Lakeflow pipelines, Unity Catalog, migrations, AI/BI, machine learning and cost control, with dedicated Databricks engineers or project delivery.
Snowflake development
Snowflake development and consulting: warehouse design, Snowpipe and Openflow ingestion, dbt, migrations, Cortex AI, security and cost control, with dedicated Snowflake developers or project delivery.
Workflow automation
Workflow and AI automation services: n8n, Make, Zapier and Power Automate flows, AI agents in workflows, CRM, finance and document automations, with dedicated automation developers or project delivery.
Questions about Kafka development
Which industries do your Kafka engineers work in?
Fintech, banking and insurance, for payments, transaction monitoring and trading data; e-commerce and retail, for orders, inventory and clickstream; logistics, for shipment tracking and fleet events; telecom and cloud infrastructure, for network and usage events; advertising and marketing technology, for bid and impression streams; manufacturing and energy, for sensor and meter data; and enterprise SaaS, for events between microservices.
Confluent Cloud, Amazon MSK or our own cluster?
It depends on where the rest of your systems run and how much operating you want to take on. Confluent Cloud brings managed connectors, Flink, Schema Registry and Tableflow in one service; MSK and Event Hubs sit inside your own AWS or Azure account and bill; a self-managed cluster with Strimzi gives full control in exchange for running it. The trade-offs are set out for your volumes before anything is bought.
Can you upgrade a cluster that still runs on ZooKeeper?
Yes. Kafka 4 runs only on KRaft, so a ZooKeeper cluster is first migrated to KRaft on a 3.x release, with controllers added and metadata moved while the cluster keeps serving traffic, and then upgraded to Kafka 4 once the clients have been checked against the removed APIs.
How quickly can Kafka engineers start?
When the right Kafka engineer is available, the start is gated only by your interview and the NDA and IP assignment. Otherwise we run a search, which typically produces candidate profiles within two to three weeks, and nobody starts until you have said yes.
How do we hire Kafka engineers through BigTree108?
Tell us the work, the seniority and the hours you need. We propose one or two people with their profiles, you interview them the way you would interview your own hire, and you sign one agreement with BIG TREE 108 LLC and receive one invoice a month.
Who owns the work they produce?
You do. Every specialist has a signed contract with BigTree108 that assigns all work product to the company, and our agreement with you assigns it onward. Code, designs and documents are delivered into your own repositories and tools, not kept where only we can change them.
Building or running Kafka?
Tell us your event volumes, the languages of your services and where the cluster runs. You get an answer within one business day: a plan, an architecture review, or candidate profiles.