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 no longer finishes before the business day. A database cannot hold full history at the grain analysts want. Event streams arrive faster than anything downstream can consume them, or the warehouse 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 discipline around the technology matters more than the choice of it. That discipline 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 cannot answer today, and which constraints are technical and which are organizational. We sequence the roadmap so each phase delivers something the business can use on its own, starting with the first.

Data Architecture and Platform Design

Iseyon designs for the access patterns the data has to serve. We separate storage from compute so each scales on its own terms. We choose batch, micro-batch or streaming per pipeline, on the merits of that pipeline. Partitioning and file layout follow how the data is queried, and we use open table formats where the workload needs transactional guarantees over object storage. 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. Loads are incremental and idempotent, so a rerun produces the same result with no duplicates. Schema changes are handled inside the pipeline. Data quality expectations are declared alongside the transformation, and lineage runs from the serving table back to the source. We design for late and out-of-order data at the start, because retrofitting it means rebuilding the pipeline.

Real-Time and Streaming Data

Iseyon builds event-driven pipelines on stream transports such as Apache Kafka, with stream processing on top. The work covers 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 has to be rebuilt. We apply streaming where a decision genuinely cannot wait. It costs more to run and more to operate than batch, so it has to earn its place.

Advanced Analytics and Machine Learning

Iseyon connects big data platforms to machine learning workflows for forecasting, recommendation, segmentation and anomaly detection. Features come from the same governed tables that feed reporting, so training and serving stay aligned. Model deployment, monitoring and retraining paths ship as part of the delivery.

Data Governance and Security

Iseyon implements cataloging and classification, access control at table, row and column level, and masking or tokenization for sensitive fields. Encryption covers data in transit and at rest, retention and deletion follow your obligations, and audit logging is something a reviewer can actually use. We build governance during the project, with your security and compliance teams involved from the design stage.

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. We use elastic compute, object storage tiering and separation of workloads by criticality, 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. We tag cost so it is attributable per team or product, and we alert on anomalies so monitoring catches a runaway job the day it runs. The invoice should hold no surprises.

Modernization and Integration

Iseyon migrates legacy data platforms in stages. We map dependencies, rebuild the pipelines that earn their place and retire the rest, run old and new in parallel with reconciliation until the numbers agree, then move 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, and catalog and access administration. Your engineers also get the cost model with the levers that move it, and enablement to 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