Data Movement
Data where it needs to be, when it needs to be there.
High-throughput data transfer across systems — streaming, batch, and CDC pipelines that are reliable at scale.
Data movement is infrastructure. When it works, nobody thinks about it. When it fails — or when it delivers data that is delayed, duplicated, or out of sequence — the systems that depend on it fail too. We build data movement infrastructure that is designed for reliability at scale, with the observability to know when something goes wrong before its impact is felt downstream.
What we build
The right data movement architecture depends on your latency requirements, your volume, and whether you need streaming, batch, or change-driven approaches — or a combination. We design the right architecture for your situation and implement it to production standards.
Kafka Event Streaming
Apache Kafka is the backbone of high-throughput event streaming in enterprise environments. We design and deploy Kafka clusters — on self-managed infrastructure, Confluent Cloud, or MSK — with appropriate topic design, partitioning strategies, retention policies, and consumer group architecture. We implement producers and consumers that handle back-pressure, offset management, and exactly-once semantics where your use case requires it. For organizations already running Kafka, we also diagnose and fix performance and reliability issues in existing clusters.
CDC Pipelines (Debezium)
Change Data Capture with Debezium captures row-level changes from your source databases — PostgreSQL, MySQL, SQL Server, Oracle — and streams them to Kafka topics in near real time. This enables downstream systems to react to data changes as they happen without polling, without full table scans, and without the source database feeling the load of CDC reads. We configure Debezium connectors with appropriate snapshot strategies, filter rules, and schema evolution handling, and we design the Kafka topic structure that makes CDC events consumable for your downstream use cases.
Flink Stream Processing
Apache Flink provides the stateful stream processing layer for workloads that require transformations, aggregations, joins, and enrichments on live data streams — not just passthrough movement. We build Flink jobs for real-time aggregations over event streams, stream-to-stream joins across Kafka topics, windowed computations for time-series analytics, and stream enrichment with lookups against reference data. Flink's exactly-once processing guarantees and managed state make it the right choice when correctness under failure is a hard requirement.
Batch ETL (Airbyte, Custom)
Not every data movement use case requires streaming. For batch workloads — nightly loads, weekly snapshots, historical backfills — we implement efficient batch ETL using Airbyte where its connector catalog covers your sources, and custom pipeline code where it does not. We design batch jobs that are idempotent, partitioned for parallelism, and observable, so re-runs after failure are safe and efficient.
Real-Time Sync
Keeping multiple systems synchronized in real time — a primary database and a search index, an operational store and an analytics cache, a source of truth and a set of downstream read replicas — requires careful design around consistency guarantees, conflict resolution, and failure recovery. We build real-time sync infrastructure that makes explicit trade-offs between consistency and availability, and documents those trade-offs so the systems that depend on the sync can be designed accordingly.
Hybrid and Multi-Cloud Data Movement
Organizations running across on-premise and multiple cloud environments need data movement infrastructure that handles the network topology, the latency, and the security requirements of cross-environment data transfer. We design hybrid and multi-cloud data movement architectures that use appropriate transport mechanisms, handle encryption in transit, and do not create single points of failure at the network boundary between environments.
Schema Evolution Handling
Schemas change. Sources add columns, rename fields, change types, and remove deprecated attributes. Data movement infrastructure that does not account for schema evolution breaks when sources change — often silently, with data that is present but wrong. We design schema evolution handling using schema registries, compatibility policies, and consumer-side evolution strategies so your pipelines survive upstream changes without manual intervention.
Data Movement Stack
The right data movement stack depends on your latency requirements, volume, and consistency guarantees. We select the appropriate pattern and tooling for each use case — not the one we default to.
Streaming
Apache Kafka for high-throughput event streaming on self-managed or MSK infrastructure. Confluent Cloud for managed Kafka with enterprise connectors and Schema Registry. AWS Kinesis for AWS-native streaming workloads. Redpanda for Kafka-compatible streaming at lower operational overhead.
Change Data Capture
Debezium for row-level CDC from PostgreSQL, MySQL, SQL Server, and Oracle — streaming changes to Kafka topics in near real time without polling or full table scans. AWS DMS for managed CDC in AWS environments. Custom PostgreSQL logical replication for cases where Debezium introduces more complexity than the problem warrants.
Stream Processing
Apache Flink for stateful stream processing with exactly-once guarantees — aggregations, stream-to-stream joins, windowed computations, and enrichment against reference data. Kafka Streams for lighter-weight in-process stream processing that does not require a separate cluster. Spark Structured Streaming for organizations already running Spark who need streaming integrated with their batch processing layer.
Batch ETL
Airbyte where its connector catalog covers your sources — nightly loads, weekly snapshots, and historical backfills. Custom Python and Go pipelines for sources and destinations that no off-the-shelf connector handles. AWS Glue for serverless ETL in AWS environments integrated with the Glue Data Catalog.
Reverse ETL
Census and Hightouch for syncing curated data from the warehouse back to operational tools — CRM, marketing platforms, support systems, and product databases. Custom sync jobs where the standard reverse ETL tools cannot express the required transformation logic or delivery guarantees.
How We Design Data Movement
Data movement failures are almost always design failures — not infrastructure failures. Schema mismatches, missing delivery guarantees, and no plan for schema evolution account for the majority of production incidents. We design to prevent all three.
1. Source & Sink Mapping
We document every source schema, delivery guarantee — at-least-once, exactly-once — and latency requirement before designing the pipeline. Mismatches between what a source can actually deliver and what the downstream system expects are the most common cause of data movement failures. Surfacing them at design time costs far less than discovering them in production.
2. Pattern Selection
We match the movement pattern to the requirement: CDC for low-latency database replication, batch for bulk transfers where latency tolerance is measured in hours, streaming for event-driven architectures that need to react to changes as they happen. We do not over-engineer — streaming has real operational complexity that batch does not, and applying it where batch is sufficient creates cost and maintenance burden without benefit.
3. Schema Evolution Strategy
We design for schema change from day one. Pipelines that break on a new column added to a source table are a liability — and they are the norm when schema evolution is not designed for explicitly. We use schema registries and backward-compatible formats where it matters, and we define explicit compatibility policies for each pipeline so schema changes have a managed upgrade path.
4. Reliability & Monitoring
Every pipeline ships with lag monitoring, dead letter queues for failed records, and runbooks for common failure modes. We do not hand off a pipeline without an operations guide. The test of a well-built data movement system is not that it works under normal conditions — it is that it recovers correctly when something goes wrong.
Where data movement matters
Data movement infrastructure is required wherever the business runs on multiple systems that need to share data reliably and efficiently.
Primary-to-replica replication for read scaling, disaster recovery, and geographic distribution. CDC-based replication that keeps replicas current with minimal lag and without full table scans against the source.
Streaming operational data from transactional systems to analytics infrastructure — keeping dashboards and alerting systems current with data that is minutes, not hours, old.
Kafka-based event buses that decouple microservices from each other, enabling asynchronous communication, event sourcing patterns, and audit-friendly event logs that capture the full history of system state changes.
Data migration from on-premise to cloud, from one cloud provider to another, or from legacy systems to modern platforms — with validation, cutover planning, and rollback capability.
Keeping operational systems — CRM, ERP, billing, support — synchronized so the authoritative data in one system is reflected accurately in the others without manual reconciliation.
Why AR Data
Data movement infrastructure is one of the areas where enterprise delivery experience matters most. The organizations that depend on this infrastructure — HP/DXC's managed services environments, Iron Mountain's records management systems — run on data pipelines that cannot afford to fail. We carry that standard into every engagement.
The difference between infrastructure built to enterprise standards and infrastructure built to get through a demo is visible over time: in the failure rates, the recovery times, the operational burden, and the reliability of the downstream systems that depend on data arriving correctly. We build the former.
We use agentic workflows in our own build process to deliver faster. Fixed-scope engagements mean you know what you are getting before we start — the deliverables, the timeline, and the cost.
Ready to move your data reliably?
30 minutes. We scope your sources, your destinations, your volume, and what reliability looks like for your environment. No pitch deck.
