Data engineering servicesData that moves. Decisions that don't wait.
Axelerant provides data engineering services for teams that need data moving: event streams and fault-tolerant pipelines, governed warehouses and lakes, and unified customer profiles activated across every channel. Data that only sits in a warehouse is potential; data that powers the next experience is the return.
message throughput engineered on AWS SQS
Kafka-streamed updates at real-time sports-platform scale
multi-source customer data unified and activated via CDP
Everyone has data. Almost nobody has it flowing.
Most organizations don't have a data problem, they have a data plumbing problem. Sources multiply, pipelines silently drop messages, customer records fragment across systems, and third-party cookies are gone while first-party data sits unactivated.
Pipelines that fail silently.
Messages dropped under load, no retry path, no dead-letter capture. Failures discovered by customers, not dashboards.
Customers split across systems.
Web, email, commerce, and CRM each holding a fragment. No unified profile means every personalization engine runs on partial truth.
Data collected, never activated.
First-party data accumulating in storage while marketing waits on it. Insight trapped one integration away from impact.
The fix is engineering the whole path: ingest, unify, activate.
Six services. One full data path.
We treat data as one continuous path: ingest from every source, unify it under one governed identity, and activate it where the business runs.
Event Streaming & Real-Time Pipelines
Kafka cluster architecture, partition optimization, high-throughput streaming for real-time products.
Reliable Async Processing
Queue-based processing with dead letter queues for fault tolerance and durability, so spikes are absorbed and nothing is silently dropped.
Warehouses & Multi-Source Data Services
Streamlining complex, many-source data estates into governed warehouses and lakes with AWS-native services.
CDP Strategy & Implementation
Identity resolution and unified customer profiles: use-case prioritization, ingestion, audience implementation, platform integration, reporting. Lytics preferred partner.
First-Party Data & Personalization
Customer data strategy for the post-cookie era. Unified profiles powering personalized experiences and campaigns.
Data Strategy Consulting
Roadmaps connecting data infrastructure investment to experience and revenue outcomes.
The data stack we ship with every engagement.
Engineered where data can't afford to lag.
Structured data the whole world reads.
For the Internal Displacement Monitoring Centre, a headless data architecture behind global displacement reporting: structured datasets, complex visualizations, and multi-source data unified into one governed foundation for policymakers worldwide.
Less legacy plumbing, more data product.
Embedded data engineers helped Red Hat modernize its marketing data infrastructure: decommissioning legacy batch and streaming services, retiring over 25 custom Python jobs and more than 150 Kafka topics, and consolidating onto modern platforms so teams could focus on new data and AI products.
From a few messages a day to millions a second.
A Kafka-streamed real-time data pipeline built for extreme concurrency: live odds and updates ingested, processed, and delivered through a fully observable pipeline with at-least-once delivery, no message left behind under load.
Warehouse-first delivery on Snowflake, BigQuery, Redshift, dbt and Kafka since the modern data stack era
The platform that watches your pipelines.
Every engagement ships with Bott, our engagement agent: full project context from day one, AI-reviewed PRs, and DORA-level delivery transparency in every steering review. On data engagements, Bott also monitors freshness and quality contracts, surfaces schema drift before it breaks downstream, and triages incidents back to the producing team.
Learn how we deliver →Data engineering, in the open.
- Data engineering consulting: what a good engagement producesRead →
- How to choose a data engineering company: seven questionsRead →
- Using AWS SQS with Dead Letter Queues for enhanced reliabilityRead →
- How to improve your application's performance using Kafka partitionsRead →
- The importance of first-party data in personalizing customer experiencesRead →
Data engineering services: common questions.
- What do your data engineering services include?
- Pipeline design and build (batch and real-time streaming with Kafka, Flink and AWS SQS), warehouse and lakehouse engineering on Snowflake, Databricks, BigQuery and Redshift, orchestration with Airflow, data quality and observability, and CDP implementation for unified customer profiles.
- Can you modernize or migrate a legacy data platform?
- Yes. We audit the current estate, retire redundant jobs and topics, and consolidate onto modern platforms in phases, so reporting keeps running while the migration happens. Red Hat is a published example.
- How does a data engineering engagement start?
- Usually with a short data architecture review: we map sources, pipelines and consumers, name the failure points, and agree on the first measurable fix before any larger build.
- Do you offer data engineering consulting or embedded teams?
- Both. We run assessments and roadmaps as consulting work, and embed data engineers alongside your team for build and operations.
Stop collecting. Start activating.
Whether your pipelines are dropping messages, your customer data is fragmented across five systems, or your first-party data is sitting idle, let's engineer the path from event to experience.
The same team carries it through.
We don't hand the work to a build partner and walk out. The people who frame the bet are the people accountable when it ships. Read how the other capabilities plug in.