Big Data Consulting Services

A workload becomes a big data problem when its volume, its arrival rate or its variety breaks the tool that used to handle it. The symptoms are recognizable: a nightly batch that no longer finishes before the business day, a database that cannot hold a full history at the grain analysts want, event streams that arrive faster than anything downstream can consume, or a warehouse that has no answer for text, images, logs and telemetry. Iseyon designs and builds the architecture that handles those conditions, then hands it over as something your engineers can operate.

Distributed processing frameworks such as Apache Spark sit under most of this work, alongside cloud object storage, stream transports like Apache Kafka, and open table formats that make large file collections behave like tables. The technology choices matter less than the discipline around them, which is where Iseyon spends its time.

Strategy and Roadmap

Iseyon assesses what exists before proposing anything: current pipelines and their real runtimes, where data quality actually fails, what the business is unable to answer today, and which constraints are technical rather than organizational. The roadmap that comes out of it is sequenced so each phase delivers something usable on its own, rather than requiring a two year build before the first result appears.

Data Architecture and Platform Design

Iseyon designs for the access patterns the data has to serve. That covers separating storage from compute so each scales on its own terms, choosing batch, micro-batch or streaming per pipeline rather than as a house style, partitioning and file layout that suit how the data is queried, and open table formats where transactional guarantees over object storage are needed. We size deliberately, since over-provisioning is the usual first draft of a cost problem.

Data Engineering and Pipeline Development

Iseyon builds ingestion and transformation that behave predictably under failure: incremental and idempotent loads so a rerun does not duplicate, schema change handled rather than crashed on, data quality expectations declared alongside the transformation, and lineage available from the serving table back to the source. Late and out-of-order data is designed for at the start, because retrofitting it means rebuilding the pipeline.

Real-Time and Streaming Data

Iseyon implements event-driven pipelines using stream transports such as Apache Kafka with stream processing on top, including topic and partition design, delivery semantics chosen for the use case, watermarking and windowing for late arrivals, and replay from the log when something downstream needs to be rebuilt. Streaming is applied where a decision genuinely cannot wait, since it costs more to run and more to operate than batch.

Advanced Analytics and Machine Learning

Iseyon connects big data platforms to machine learning workflows for forecasting, recommendation, segmentation and anomaly detection, with features computed from the same governed tables that feed reporting so training and serving do not drift apart. Model deployment, monitoring and retraining paths are part of the delivery rather than a later phase.

Data Governance and Security

Iseyon implements cataloging and classification, access control at table, row and column level, masking or tokenization for sensitive fields, encryption in transit and at rest, retention and deletion aligned to your obligations, and audit logging that a reviewer can actually use. Governance is built during the project with your security and compliance teams involved, not added after a finding.

Cloud Big Data Platforms

Iseyon builds on the major cloud platforms using managed services where they reduce operational burden and self-managed components where control matters more. Elastic compute, object storage tiering and separation of workloads by criticality are used so a heavy ad hoc query cannot starve a production pipeline.

Performance and Cost Optimization

Iseyon profiles before tuning: skew and spill in distributed jobs, small file problems, shuffle behavior, cluster sizing against actual utilization, and storage tiering for cold data. Cost is tagged and attributable per team or product, with alerting on anomalies so a runaway job is caught by monitoring rather than by an invoice.

Modernization and Integration

Iseyon migrates legacy data platforms without a single high-risk cutover: dependency mapping, rebuilding what earns its place instead of translating everything, parallel running with reconciliation until numbers agree, then moving consumers across in groups. Decommissioning the old estate is planned into the work, since running both platforms indefinitely erases the reason for moving.

Handover

Iseyon leaves behind architecture documentation with the reasoning behind each decision, runbooks for the failure modes seen during the build, catalog and access administration, the cost model with the levers that move it, and enablement for the engineers who will extend the platform. The engagement is finished when your team can add the next pipeline without us.

Big Data Technology Stack: Legacy Hadoop and Cloud-Native Compared