Will CDC slow down our production database?

Log-based capture reads the transaction log rather than querying tables, so the load on the source is minimal — typically far lighter than the batch extracts it replaces.

What happens when a downstream system is offline?

Events are retained in the log, so consumers resume from their last committed offset and catch up automatically. Nothing is lost while a target is down.

How quickly can an engagement start?

Discovery usually begins within one to two weeks of scope agreement, and a delivery team is typically mobilised within three to four weeks depending on the skills involved.

Can you work alongside our in-house team?

Yes — most of our engagements are blended. We work in your repositories, ceremonies, and tooling, and we plan for knowledge transfer from the start so capability stays with you.

How are engagements priced?

Fixed price for well-bounded projects, monthly capacity for dedicated pods, and outcome-linked SLAs for managed services. We are explicit about assumptions and what would change the number.

NeonAITech services — AI and data engineering, product engineering, cloud and DevOps, managed operations, security and quality

Real-Time Data Integration & Streaming

Move data between systems in seconds, not overnight batches.

Book Free Consultation All Services

< 60s

Source-to-dashboard latency

99.9%

Pipeline delivery reliability

80%

Fewer overnight batch failures

Overview

What Real-Time Data Integration & Streaming means at NeonAITech

We replace brittle nightly extracts with event-driven integration — change data capture, streaming pipelines, and schema-governed topics — so every downstream system, dashboard, and model works from data that reflects what happened minutes ago.

Tools & Platforms

Apache Kafka
Debezium
Flink
Kinesis
Airbyte
Airflow

We are not tied to a single vendor — the stack follows the problem, your existing estate, and your team’s skills.

What We Deliver

Change Data Capture Pipelines

Log-based CDC from operational databases into your warehouse, lakehouse, or search layer without loading the source system.

Event Streaming Backbone

Kafka-based topics with schema registry, contracts, and replay, giving every consumer a governed way to subscribe.

Reverse ETL & Activation

Push modelled data back into CRM, marketing, and support tools so insight lands where the work actually happens.

Capabilities

Inside the Engagement

Kafka, Kinesis, and Event Hubs

Debezium and log-based CDC

Schema registry and contract enforcement

Exactly-once and idempotent processing

Backfill, replay, and dead-letter handling

How We Work

A delivery rhythm you can see into

Every Real-Time Data Integration & Streaming engagement runs the same four phases, with AI used wherever it removes effort rather than adds novelty.

01
Discover
We map the current state, agree the outcome, and size the work — so scope is a shared decision, not a surprise.
02
Design
Architecture, delivery plan, and success measures are set before build, with costed options where trade-offs exist.
03
Build
Short increments with working output you can review, steer, and stop — never a black box until go-live.
04
Operate
We measure against the agreed outcomes, hand over documentation, and stay on for support where you want it.

Engagement Models

Buy it the way that fits

Fixed-Scope Project

A defined outcome, timeline, and price. Best when requirements are clear and the deliverable is well bounded.

Dedicated Pod

A cross-functional team working to your backlog and priorities, scaling up or down with a month’s notice.

Managed Service

Ongoing ownership against agreed SLAs, with a share of capacity reserved for continuous improvement.