From transactional SQL databases to petabyte-scale analytical platforms — we handle the full data lifecycle including migrations, warehouse modernization, pipelines, and governance.
Most organizations have more data than they can use, and less insight than they need. The gap is almost always infrastructure — fragmented systems, untrusted pipelines, and no single source of truth. Zhoton closes that gap.
We cover the entire data journey: assessing your current landscape, designing target architectures, migrating workloads, building pipelines, implementing governance, and enabling the self-service analytics that drive real decisions.
Start a Conversation →Practical, outcome-focused engagements designed around your business — not generic toolkits.
Structured migration of on-premise databases to Azure SQL, Azure Synapse, Snowflake, Databricks, AWS Redshift, or Amazon RDS — with zero-data-loss guarantees, validation checkpoints, and minimal downtime.
Scalable data pipelines using Azure Data Factory, dbt, Apache Spark, and AWS Glue — from daily batch processing to real-time streaming with Kafka and Azure Event Hubs.
Re-architect legacy data warehouses into modern cloud-native platforms with columnar storage, elastic compute, and separation of storage and compute that scales to your demand without waste.
Data cataloging, lineage tracking, quality rules, and access policies using Azure Purview and Apache Atlas — so your data is trustworthy, auditable, and secure across the organization.
Ingest and act on streaming data using Azure Event Hubs, Apache Kafka, AWS Kinesis, and Spark Structured Streaming — enabling real-time operational dashboards and event-driven applications.
Unified lakehouse architectures on Azure Data Lake or AWS S3 with Delta Lake or Apache Iceberg — combining the flexibility of data lakes with the reliability and performance of data warehouses.
Certified hands-on expertise across the tools that power modern enterprise IT.
Deep dive into your environment, goals, and constraints.
Architecture review and precise scoping with cost estimates.
Tailored solution with defined milestones and deliverables.
Agile delivery with weekly updates and transparent reporting.
Post-launch support, knowledge transfer, and optimization.
We use change data capture (CDC), dual-write patterns, and phased cutovers to migrate with minimal production impact. Every migration includes rollback plans and validation checkpoints before any cutover.
It depends on your workload profile, team skills, and existing investments. Snowflake excels at analytics workloads; Databricks is strong for ML and large-scale processing; Azure Synapse integrates tightly with the Microsoft ecosystem. We assess your situation and recommend objectively.
Yes. Data quality remediation is often the first step. We profile your data, identify root causes, implement validation rules and cleansing pipelines, and put governance frameworks in place to prevent future degradation.
Talk to one of our certified experts — no obligation, just a genuine conversation about what's possible for your organization.