CMARIX offers end-to-end data engineering services that enable enterprises to create robust, scalable, and governed data platforms. We create and deliver cutting-edge ELT pipelines, streaming architectures, cloud-native data platforms, data lakehouses, and data quality solutions to turn data into intelligence for analysis, AI, and other enterprise purposes.
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CMARIX is a data engineering company that transforms complex, fragmented data environments into robust, efficient platforms. CMARIX engineers design scalable pipelines, lakehouses, and cloud data architecture governance that enables businesses to gain faster insights into analytics and AI.
Build a scalable data architecture aligned with your business, analytics, and AI goals.
Our data engineering consultants evaluate your existing data architecture, identify deficiencies, and create a future-proof data platform that meets your workloads, data volumes, and compliance needs. We help you determine ingestion, storage, processing mechanisms, governance, and technology choices for a data platform that is scalable, secure, and fit for analysis and AI right from the start.
Stack: Snowflake | Databricks | BigQuery | Azure Synapse | AWS | Azure | Google Cloud | dbt | Apache Spark | Terraform
Reliable, testable, and maintainable pipelines from source to serve.
Our team of experts creates ELT and ETL pipelines for the ingestion of data from relational databases, SaaS APIs, event streams, and flat files, transformation of the data using dbt or Spark models, and delivery of the datasets to your warehouse or lakehouse on a pre-specified schedule – all with built-in data quality checks, schema contracts, and runbooks. Please refer to our data integration offerings for data connectivity and MDM services.
Stack: dbt | Apache Spark | Fivetran | Airbyte | Python | Airflow | Dagster | Snowflake | BigQuery
Cloud-native data platforms built for your cloud provider and your data volumes.
Our cloud data engineering services are built on AWS, Azure, and Google Cloud, leveraging managed services to maximize scalability and reduce operational complexity. We architect and implement modern data platforms with AWS Glue, Redshift, S3, EMR, Azure Synapse Analytics, Data Factory, ADLS, Google BigQuery, Dataflow, and Pub/Sub. If your primary requirement is a modern data warehouse, our Data Warehouse Consulting Services provide specialized expertise for Snowflake, BigQuery, and Azure Synapse.
Stack: AWS Glue | AWS Redshift | Azure Synapse | Azure Data Factory | GCP BigQuery | GCP Dataflow | dbt | Terraform
Low-latency event pipelines for use cases that cannot wait for a batch.
We design and build streaming data pipelines using Apache Kafka and Apache Flink for use cases that require real-time data availability: fraud-detection pipelines, operational dashboards, live recommendation feeds, and IoT sensor ingestion. Every streaming pipeline is built with exactly-once semantics, backpressure handling, and consumer lag monitoring. For petabyte-scale and distributed processing, see our big data development services.
Stack: Apache Kafka | Apache Flink | Kafka Streams | Spark Structured Streaming | Kinesis | Pub/Sub | Redis
A unified analytics and AI data layer that scales with your business.
We design and implement lakehouse architectures on Databricks, Apache Iceberg, and Delta Lake that give you the flexibility of a data lake with the query performance and governance of a data warehouse. Every lakehouse build includes a medallion architecture (bronze, silver, gold), access controls, and the table format and compaction strategy appropriate to your query patterns. For modern lakehouse architecture on Databricks, Iceberg, or Delta, see our data lakehouse consulting services.
Stack: Databricks | Delta Lake | Apache Iceberg | Apache Hudi | Snowflake | dbt | Spark | AWS S3 | ADLS
Modernize legacy data platforms without disrupting business operations.
Our services include helping companies migrate their existing ETL systems, on-premises data warehousing systems, and monolithic data platform solutions to cloud-native systems with minimal disruptions. Our areas of expertise include schema migration, pipeline refactoring, data validation, concurrent execution, and cut-over planning.
Stack: AWS DMS | Azure Data Factory | Google Database Migration Service | dbt | Apache Spark | Snowflake | BigQuery | Terraform
Know when your data is wrong before your stakeholders do.
Our data platforms are architected to ensure observability at every point along the way. This ranges from automatic quality assurance and schema validation to freshness checks, anomaly detection, and lineage tracing to ensure that your data pipelines and analyses run smoothly.
Stack: Great Expectations | dbt Tests | Monte Carlo | Soda | Apache Atlas | OpenLineage | Grafana | Prometheus
Keep your data platform reliable, secure, and continuously improving.
Our managed data engineering service includes monitoring, pipeline management, performance and cost optimization, and platform management, even after deployment. We solve pipeline issues, optimize workloads, implement improvements, and enforce governance practices so you can focus on analytics and innovation rather than managing the platform itself.
Stack: Airflow | Dagster | dbt Cloud | Grafana | Prometheus | Datadog | Terraform | Kubernetes
A modern data platform is a layered system in which ingestion, transformation, storage, quality, and serving each has a defined responsibility and a defined contract with the adjacent layer. This is the architecture CMARIX implements for enterprise data engineering services engagements.
We examine your current data ecosystem, including source systems, pipelines, downstream consumers, platform architecture, and operational bottlenecks. This results in a prioritized roadmap that highlights reliability and scalability issues and identifies the improvements needed to create a modern data platform.
Timeline: Week 1–2
Deliverables: Source inventory · Consumer requirements · Pipeline health scorecard · Gap analysis · Prioritized roadmap
We design an architecture that is production-ready and reflects your existing infrastructure, future expansion possibilities, and performance targets. Instead of suggesting the usual technologies, we compare each one to your specific applications, operational model, and future maintenance needs.
Timeline: Week 2–4
Deliverables: Architecture blueprint · Tool selection matrix · Integration design · Infrastructure cost estimate · Migration sequencing
From ingestion connectors to transformation engines, from validations to DAG orchestration, everything runs in parallel, and pipeline iterations are deployed to a staging environment through biweekly sprints. This is not just magic happening on PowerPoint presentations but something that you can observe firsthand.
Timeline: Week 4–14
Deliverables: Ingestion pipelines · dbt model library · Data quality tests · Orchestration configuration · Schema contracts
The platform is instrumented with quality gates, freshness checks, and schema changes to let you know about a problem in your pipelines before a stakeholder notices an anomaly in one of the dashboards.
Timeline: Week 10–14
Deliverables: Quality test suite · Freshness SLA monitors · Schema change alerts · Anomaly detection · Lineage documentation
We operate both the existing and newly constructed pipelines simultaneously, validate that their consumer outputs are the same, and then perform the cutover sequentially to avoid downtime. Runbooks have been developed for all the operations procedures.
Timeline: Week 12–16
Deliverables: Cutover plan · Parallel run results · Consumer sign-off · Runbooks · Security review
We deliver to you an end-to-end platform with complete documentation, including the data dictionary, pipeline runbooks, and architectural decision logs. CMARIX provides SLA-backed ongoing support services to those seeking partners to manage pipelines, schema changes, and platform scaling as data grows.
Timeline: Week 14 onward
Deliverables: Platform documentation · Data dictionary · Team training · SLA agreement · Support retainer options
CMARIX provides data platform services that your teams can review and secure for security, compliance, and governance purposes. All data platforms have access controls, lineage, quality documentation, and compliance settings built specifically for your regulatory context.
CMARIX engineers select tooling based on your cloud provider, data volumes, latency requirements, and the team's operational capacity to maintain it after handover.
CMARIX delivers enterprise data engineering services tailored to the data volumes, source-system landscapes, and compliance requirements of each industry.
Healthcare organizations manage complex data across EHRs, medical devices, laboratories, imaging systems, and patient applications. CMARIX builds clinical data pipelines, EHR integrations, FHIR-compliant data layers, and patient analytics infrastructure that bring fragmented healthcare data into governed environments. Solutions support secure data ingestion, transformation, validation, lineage, and auditing while accounting for HIPAA requirements. Reliable data foundations enable healthcare teams to access consistent information for clinical analytics, operational reporting, research, and AI-driven healthcare applications.
Dive Into More
CMARIX is a data engineering company that transforms fragmented data ecosystems into scalable, cloud and platform-agnostic platforms. Our engineers build production-grade pipelines, secure, scalable, and governed architectures, and provide migration, handover, and managed support to accelerate analytics and AI adoption and drive business growth.
Data Pipelines in Production
In-House Data & Platform Engineers
Client Retention Rate
Years in Product Engineering
CMARIX structures data engineering services engagements to match your platform maturity and delivery timeline, from a focused pipeline audit to a fully built cloud data platform.
What you get:
A comprehensive assessment of your data infrastructure covering pipeline reliability, data quality, scalability and governance.
Clear modernization roadmap with prioritized next steps
What you get:
A dedicated team of data engineers, dbt developers and cloud architects delivers your production data platform end to end.
Fully deployed, production-ready data platform
What you get:
An extension of your in-house team providing ongoing pipeline development, platform optimization and operational support.
Reliable, scalable and continuously optimized data platform
Audit and Roadmap is priced between USD 8,000 and USD 15,000 for a 2-3 week period. Build service is priced at USD 40,000-USD 120,000 and is spread over a period of 2-5 months. Monthly Retainer Pricing is USD 6,000-USD 20,000. All engagements follow milestone-based billing with fixed budgets and deliverables.
A data engineering project usually lasts from 2 to 5 months, depending on the project's scale, data sources, platform complexity, and cloud migration. An audit and roadmap for a data platform takes 2 to 3 weeks, whereas a pipeline modernization project takes 4 to 8 weeks. An enterprise-wide implementation that includes cloud migration and multiple data sources will take several months.
ETL involves transforming data prior to loading it into the target system. In ELT, the untransformed data is loaded first and then transformed using tools such as dbt in the target system. At CMARIX, we have chosen the ELT approach for our cloud warehouse and lakehouse environments due to its ease of implementation, rapid debugging, and ability to leverage cloud-native warehouses for processing.
A data lakehouse is an amalgamation of low-cost storage from a data lake and the high-performance querying capabilities of a data warehouse. A data lakehouse is the best fit when you need a storage layer that supports SQL analytics and machine learning development on the same dataset, or when your data volume makes a fully managed warehouse extremely expensive. CMARIX provides lakehouses on Databricks, Delta Lake, and Apache Iceberg.
Yes. CMARIX runs migration engagements from legacy ETL platforms including Informatica, SSIS, Talend, and custom Oracle PL/SQL pipelines to modern cloud-native stacks. We run old and new pipelines in parallel during the migration period, validate the equivalence of the outputs, and execute a sequenced cutover to protect downstream consumers who currently depend on your data.
All pipelines developed by CMARIX come packaged with automated data quality tests in dbt or Great Expectations to ensure that row counts, null counts, referential integrity, and schemas are validated in each execution. SLAs for freshness ensure that a notification is sent to the data team if pipeline execution is delayed.
CMARIX delivers cloud data engineering services on AWS, Azure, and GCP. On AWS, we build with S3, Glue, Redshift, and EMR. On Azure, we use Synapse Analytics, Data Factory, and ADLS. On GCP, we build with BigQuery, Dataflow, and Cloud Storage. We also work across multi-cloud environments for teams with data assets spread across multiple providers.
Data pipelines, transformation models, orchestration configurations, data quality testing frameworks, platform documentation, and runbooks for production are provided by CMARIX. Each engagement includes deliverables that your team can manage and operate post-handover. No prototypes and unstructured scripts.
Data engineering services cover the design, build, and operation of the infrastructure that moves data from source systems to the analysts, data scientists, and applications that need it: ingestion pipelines, transformation layers, data warehouses, lakehouses, streaming systems, and data quality frameworks. CMARIX delivers these as standalone pipeline builds, full platform engineering engagements, or ongoing retainer support.
Data engineers build and maintain the infrastructure that makes data available: pipelines, storage, transformation, and quality. Data scientists use that infrastructure to build models and surface insights. Most data science projects fail not because of modeling problems but because of data engineering problems. CMARIX addresses both, and we design the data infrastructure with the downstream modeling and analytics use cases in mind from the start.
The process should begin with an audit of your data platform. This will allow CMARIX to evaluate the current state of your pipeline, source systems, consumer needs, and infrastructure, and generate a gap analysis with prioritization of steps required for completion. Our clients usually start with an audit.
Your unique concepts will be crafted into a remarkable end result by our team.
