Hadoop • Spark • Databricks • Analytics

Big Data & Analytics

From raw operational exhaust to decisions the business trusts — we design, build, and run the full data platform: ingestion, distributed compute, the lakehouse, analytics, and machine learning.

Data platforms engineered like infrastructure — because they are.

01 — Approach

A Data Platform Is Infrastructure

Most data initiatives fail the same way networks fail: not at the shiny layer, but in the plumbing. Dashboards disagree because pipelines silently drop records; models rot because nobody owns the feature data; a cluster bought for scale spends its life at four percent utilisation. The industry calls this a data strategy problem. We call it what it is — an engineering problem.

HCS builds data platforms with the same operations-grade discipline we bring to carrier networks: explicit architecture, boring reliability, measured cost. We work across the whole stack — Hadoop estates that still carry production, Spark and PySpark compute, the Databricks lakehouse, orchestrated data engineering, and the analytics and data science layers the business actually sees.

Engage a single capability or the full platform: each area below stands alone, and all of them are built to compose.

Flagship Data Service — Apache Kafka

Apache Kafka: Planned, Architected, Implemented, Operated — and Taught

The event backbone as a full-lifecycle engagement — from the first topic design to the team that runs it without us.

Kafka is where our infrastructure discipline and our data practice meet. A broker cluster is a distributed storage system with a consensus protocol, disk I/O characteristics, and network listeners — which is to say it is infrastructure, and it fails like infrastructure. We treat it that way: KRaft controller quorums isolated from data brokers, dedicated listeners so consensus traffic never contends with client ingest, storage and page-cache behaviour sized deliberately, and observability wired in before the first production topic.

Above the cluster sits the part most projects get wrong: data contracts. Without Schema Registry enforcement, one producer change breaks every downstream consumer at 3 a.m. We standardise on Avro or Protobuf with compatibility modes chosen per topic, so schemas can evolve without synchronised downtime.

And we teach it. Our principal delivers multi-day hands-on Kafka workshops as a Senior Consultant Trainer — KRaft clusters built from scratch, schema-evolution labs, Kafka Connect and CDC, ksqlDB, Strimzi on Kubernetes, and real-time AI enrichment pipelines. Client teams can be trained on the very architecture we deploy for them, which is the difference between a platform you own and a platform you rent from your consultant.

Plan & Architect

Use-case qualification — whether the problem genuinely needs streaming, or a queue or batch job would serve better
Capacity and sizing: throughput, retention, partition count, replication factor, and the storage bill that follows
KRaft cluster topology with controller/broker role separation and isolated CONTROLLER, INTERNAL, and EXTERNAL listeners
Topic and partition-key design for ordering guarantees, consumer parallelism, and future growth
Schema strategy: Avro vs Protobuf vs JSON Schema, subject naming, and per-topic compatibility modes
Kafka vs RabbitMQ vs NATS vs managed-service decision, costed honestly against your team’s operating capacity

Implement & Integrate

Cluster build-out on-premises, in cloud, or hybrid — with security from day one (TLS, SASL/SCRAM or mTLS, ACLs)
Confluent Schema Registry deployment with contract enforcement at ingress
Kafka Connect pipelines and Debezium CDC from Postgres, MySQL, Oracle, and SQL Server
Producer and consumer engineering: idempotence, transactions, batching, compression, and rebalance behaviour
Stream processing in Kafka Streams and ksqlDB — enrichment, joins, windowed aggregation
Sink integration into lakehouse, warehouse, search, and vector databases for real-time RAG
Real-time AI enrichment: asynchronous consumers calling local or hosted models with rate limiting and dead-letter routing

Run on Kubernetes — Strimzi

Kafka on Kubernetes via the Strimzi operator, with `KafkaNodePool` resources modelling KRaft controller and broker pools separately
Declarative cluster management: topics, users, and ACLs as custom resources under version control and GitOps
Operator-driven rolling upgrades, broker scaling, and partition rebalancing without hand-run maintenance windows
Storage classes, pod anti-affinity, and rack awareness so a node loss is not a data loss
Listener and ingress design for in-cluster, cross-cluster, and external client access
Deployed on your own Kubernetes — EKS/GKE/AKS or self-managed k3s/RKE2 on Proxmox or VMware

Operate, Monitor & Enable

Observability: JMX metrics into Prometheus and Grafana, with consumer-lag tracking as a first-class SLO
Alerting on the things that actually matter — under-replicated partitions, lag growth, controller failover, disk pressure
Runbooks and chaos drills: broker loss, controller failover, and rebalance storms rehearsed before they happen
Performance forensics: partition skew, poison-pill records, slow consumers, and rebalance thrash
Ongoing operations under retainer, or co-managed alongside your platform team
Hands-on team training — KRaft, schema governance, Connect/CDC, ksqlDB, Strimzi, and streaming AI patterns
Structured handover: architecture documentation, runbooks, and named internal owners

KRaft (Kafka 4.x)
Schema Registry
Kafka Connect & Debezium
ksqlDB & Kafka Streams
Strimzi on Kubernetes
RabbitMQ
Prometheus & Grafana
Team training included

Engage any stage — an architecture review, a greenfield build, a rescue of a cluster nobody understands, or a workshop for your engineers. Start with what you are actually running today.

Discuss a Streaming Engagement

02 — What We Deliver

Big Data Capabilities

Seven capabilities that take data from source systems to production decisions — each one deliverable on its own.

HDFS & YARNHive · HBaseModernisation

Hadoop & Distributed Platforms

Keep the estate honest — run it well, or modernise it deliberately.

A decade of big-data investment lives in Hadoop clusters that still carry production workloads — and now need either disciplined operation or a deliberate exit. We do both. We stabilise and tune HDFS, YARN, Hive, and HBase estates that have drifted, and we plan migrations to lakehouse and cloud object storage that preserve the data, the lineage, and the jobs — instead of the big-bang rewrite that stalls for two years.

Cluster health assessment: HDFS integrity, YARN scheduling, small-files pathology, and capacity truth
Performance tuning of Hive/Tez and HBase workloads that have slowed with data growth
Security hardening: Kerberos, Ranger policies, encryption at rest and in transit
High availability, disaster recovery, and upgrade planning for ageing distributions
Migration engineering: Hadoop to Databricks, cloud object storage, or hybrid — job by job, with parity checks
Decommissioning done right: data verified, lineage retained, licence and hardware costs recovered

A distributed estate that is either running well — or being retired on a plan, not a prayer.

PySparkStructured StreamingJob Tuning

Apache Spark & PySpark

Distributed compute that is fast, correct, and affordable.

Spark is the workhorse of modern data processing — and the easiest place to burn money. The gap between a naïve PySpark job and a tuned one is routinely 10x in runtime and cost: partitioning, join strategy, shuffle behaviour, and memory sizing decide whether the cluster works or thrashes. We write production-grade PySpark — batch and Structured Streaming — and we tune the jobs you already have, with the config decisions explained, not folklore.

PySpark pipeline development: typed transformations, testable jobs, and CI-friendly project structure
Structured Streaming for near-real-time processing — watermarks, state, and exactly-once sinks
Performance forensics: skew, shuffle explosions, spill, and the joins that cause them
Cluster and job sizing across Databricks, EMR, Dataproc, and on-premises Spark
Delta Lake / Parquet table design: partitioning, compaction, and Z-ordering that fit the queries
Migration of legacy ETL, pandas, and SQL workloads into distributed Spark
Team enablement: PySpark standards, code review, and hands-on engineer training

Pipelines that finish in minutes instead of hours — on clusters half the size.

Delta LakeUnity CatalogMLflow

Databricks Lakehouse

One governed platform for data engineering, analytics, and ML.

The lakehouse ended the false choice between the flexible-but-chaotic data lake and the governed-but-rigid warehouse. We design and run Databricks platforms built around that promise: Delta Lake for reliable tables, Unity Catalog for governance that auditors accept, medallion architecture that keeps bronze chaos away from gold decisions, and MLflow so models ship with lineage. And because Databricks bills by the second, we treat cost as a first-class design constraint, not a quarterly surprise.

Workspace and account architecture: environments, networking, identity, and IaC-managed setup
Medallion (bronze/silver/gold) design with Delta Live Tables / Lakeflow pipelines
Unity Catalog governance: lineage, access control, data sharing, and audit evidence
Photon, serverless, and cluster policy strategy — performance and spend engineered together
Databricks SQL warehouses serving BI at concurrency, with query tuning
MLflow-managed model lifecycle from experiment to served endpoint
FinOps for Databricks: tagging, chargeback, and monthly cost reviews with named owners

A single governed platform where engineers, analysts, and scientists work on the same trusted data.

Apache KafkaRabbitMQKafka Connect · ksqlDB

Distributed Event Streaming & Message Queues

Kafka for the log of record. RabbitMQ for the work that must get done.

These two are constantly confused, and choosing wrong is expensive. Apache Kafka is a distributed, replayable commit log: events are appended to partitions, retained for a configured window, and read independently by many consumer groups at their own offsets — so a new consumer can replay last month’s traffic without asking the producer for anything. RabbitMQ is a message broker: an exchange routes each message to queues where it is consumed once, acknowledged, and removed — with per-message TTLs, priorities, and rich routing topologies. Kafka answers "what happened, in order, and who wants to read it?". RabbitMQ answers "this task must be executed exactly once, by one of these workers, now." Most serious estates need both, and we design the boundary between them deliberately.

Platform selection with the honest trade-off: Kafka, RabbitMQ, NATS, Redpanda, or a managed service — sized to your throughput and team
Kafka cluster architecture on KRaft (ZooKeeper is gone in Kafka 4.x): controller quorum and broker roles separated, listeners isolated
Topic design that survives growth: partitioning and key strategy, replication, retention, compaction, and ordering guarantees
Schema governance with Confluent Schema Registry — Avro/Protobuf contracts and BACKWARD/FORWARD compatibility enforced at ingress
Kafka Connect and CDC (Debezium) pipelines from databases into lakehouse, search, and vector sinks
Stream processing with Kafka Streams and ksqlDB — joins, windowing, and stateful aggregation
RabbitMQ topologies: exchanges and binding keys, work queues, dead-letter exchanges, quorum queues, and backpressure
Delivery-semantics engineering: idempotent producers, transactions, exactly-once effects, consumer-lag and rebalance tuning
Migration work: from database polling, cron batches, or a strained queue into a properly designed streaming backbone

One event backbone the whole business reads from — instead of nine point-to-point integrations nobody owns.

Go deeperData Engineering & Pipelines Integration Engineering (Helix Studios)

Kafka & CDCAirflowdbt

Data Engineering & Pipelines

The plumbing everything else depends on — built boring on purpose.

Every dashboard, model, and "data-driven decision" sits on top of pipelines — and pipelines are where data platforms quietly rot. We build ingestion and transformation that behaves like production software: Kafka and CDC for movement, Airflow for orchestration, dbt for modelled, tested transformations, and data-quality checks that fail loudly before the numbers reach the board pack. Idempotent, observable, documented — deliberately boring, because boring is what reliable looks like.

Batch and streaming ingestion: APIs, databases, files, CDC (Debezium), and Kafka event streams
Orchestration with Airflow/Dagster: dependencies, retries, backfills, and SLA alerting
Transformation with dbt and Spark — version-controlled, tested, and documented models
Warehouse and lake modelling: dimensional design, slowly changing dimensions, and contracts
Data quality engineering: expectations, anomaly detection, and quarantine flows
Observability and lineage so "why is this number wrong?" has a fast answer
Platform choices matched to budget — from single-node Postgres honesty to lakehouse scale

Data that arrives on time, passes its tests, and carries lineage you can show an auditor.

Power BISemantic ModelsSelf-Service

Analytics & Business Intelligence

Dashboards the business trusts — because the numbers reconcile.

The hard part of BI was never the charts — it is getting one agreed version of the truth in front of people who make decisions. We build the semantic layer first: KPIs defined once, owned by someone, reconciled against finance. Then the Power BI and modern BI stack on top — executive scorecards, operational dashboards, and self-service models — plus the adoption work that decides whether it becomes the way the company runs or another unused portal.

KPI and metric definition workshops — one agreed formula per number, with a named owner
Power BI architecture: semantic models, DAX, incremental refresh, and row-level security
Executive scorecards and operational dashboards designed for decisions, not decoration
Databricks SQL, Fabric, and warehouse-direct BI serving at real concurrency
Self-service enablement: certified datasets, governance, and analyst training
Embedded and customer-facing analytics for products and partner portals
Report estate rationalisation — retiring the 400 reports nobody opens

Meetings that start from the same number — and end with a decision instead of a reconciliation.

Forecasting & ChurnMLOpsMeasured Impact

Data Science & Machine Learning

Models judged in production, not in notebooks.

The gap between a promising notebook and a model the business relies on is engineering: pipelines that feed it, monitoring that watches it, and retraining that keeps it honest as the world drifts. We take data science from problem framing — is this even a modelling problem, and what is a wrong prediction worth? — through feature engineering and model development to MLOps: deployment, drift detection, and measured business impact. Forecasting, churn, credit risk, anomaly detection, optimisation — applied to your operations, not in the abstract.

Use-case framing and honest feasibility: baseline first, model only if it beats the baseline
Demand forecasting, churn and propensity, credit scoring, anomaly and fraud detection
Feature engineering on Spark/Databricks with reusable, documented feature pipelines
MLOps: MLflow tracking, model registry, CI/CD for models, and serving (batch and real-time)
Drift and performance monitoring with retraining triggers — models age like infrastructure
Interpretability and model governance for regulated and board-level decisions
Team upskilling: pairing your analysts into applied data-science practice

A small portfolio of models in production with measured returns — not a graveyard of pilots.

03 — Ecosystem

Platforms We Build On

Tooling chosen for the problem, not the hype cycle — deployed on-premises, in the cloud, or hybrid.

Apache Spark
Databricks
Hadoop Ecosystem
Apache Kafka
Airflow
dbt
Power BI
MLflow

Start With the Data You Actually Have

A Phase 0 Review maps your sources, pipelines, platforms, and reporting as one system — and tells you the shortest honest path from where your data is to what the business needs from it.

Begin Phase 0 Review Talk to an Engineer