Packaged Hadoop platforms
We deliver Hadoop stacks that operations can run: HDFS, YARN, Hive, Spark, security, and the backup and upgrade path. Packaging means runbooks and owners, not a pile of open-source tarballs.
Hadoop Platforms
Packaged Hadoop platforms and distributed processing for serious data volumes.
Agam Intelligence still builds and modernises Hadoop-based platforms for organisations that have real volume: packaged clusters, distributed processing, and the operational discipline to keep them running. We also plan the path off Hadoop when the estate and cost say it is time.
Intelligence
What we do
Distributed processing that has been in production, not only in talks.
Before AI was on every homepage, we were packaging Hadoop platforms and BI for enterprises handling serious volumes of data. That work has not vanished. We design, operate, and - when it is the right call - migrate Hive, HDFS, Spark-on-Hadoop, and the jobs that still earn their keep, without pretending every workload belongs on a warehouse overnight.
Enabling outcome-based Hadoop platforms and distributed processing.
Distributed processing at volume
Batch and large-scale Spark or MapReduce jobs designed for the data you actually have. We tune partitions, file formats, and resource pools so jobs finish in the window the business needs.
Hive, HBase, and SQL-on-Hadoop
Warehouse-style access on Hadoop when that is still the right store. We design tables, compaction, and governance so analysts are not one full-scan away from melting the cluster.
Security and multi-tenancy
Kerberos, ranger-style policies, encryption, and tenant isolation so a shared cluster is not a free-for-all. Enterprise Hadoop is a security programme as much as a storage programme.
Operations and upgrades
Clusters rot without patching, capacity planning, and job hygiene. We operate or support the platform: monitoring, small-file problems, and upgrades that do not take a silent week of downtime.
Modernisation and exit paths
When cloud warehouses or lakehouses are the better home, we plan workload-by-workload migration - Spark jobs, Hive tables, and BI - rather than a slogan to “leave Hadoop” by Friday.
Case studies
See the work
Clinical analytics leadership could finally trust
A regional health network needed executive dashboards and predictive models grounded in governed clinical data.
An enterprise copilot that survived legal and security review
A B2B SaaS vendor needed RAG-based product assistance grounded in customer docs - not a generic chatbot.
Fraud signals surfaced before losses hit the ledger
A payments processor needed anomaly detection that compliance could defend - not another black-box alert queue.
Why work with us?
Predictable business outcomes
Jobs complete in the window, costs are visible, and the platform has an owner - whether you stay on Hadoop or move in slices.
Lower operating risk
Security, upgrades, and capacity planning keep a cluster from becoming an unpatchable, single-person system.
Modernise with confidence
Keep the workloads that still belong on Hadoop, and migrate the ones that do not - without a big-bang bet on a new logo.
Trends and insights
Hadoop and distributed processing news and perspectives
AI Adoption: Strategy Before Implementation
Pressure to ship AI pilots is real, but teams that skip strategy stall on unclear ROI, messy data, and governance gaps. This article explains why use-case clarity, data readiness, and guardrails must come before model selection - and how governance can actually accelerate adoption.
Read the article
Is Your Technology Stack Future Ready?
Future-ready data and AI platforms are designed for change from the start - pipelines that can evolve, models that can retrain, and architecture that does not force a rewrite every time the business asks a new question.
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Signs Your Engineering Team Needs a Technical Audit
When dashboards disagree, models drift, and nobody owns the pipeline, the problem is often systemic. This article covers the warning signs and what an independent audit should examine across data, process, and platform.
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