CMARIX delivers end-to-end big data development services that help enterprises build future-ready data platforms capable of handling petabyte-scale workloads and billions of streaming events. Our team of talented big data engineers, along with in-depth knowledge of Apache Spark, Apache Flink, Apache Kafka, Databricks, Hadoop, Snowflake, Delta Lake, and AWS, Azure, and Google Cloud, makes our solutions highly robust and drives business results.
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Trusted by 2000+ Happy Clients, Including Fortune 500 Companies
CMARIX delivers big data services across the full platform lifecycle, from strategy and architecture through processing, analytics, visualization, cloud migration, and ongoing operations. Every big data solution is scoped to your data volumes, latency requirements, and the downstream consumers your platform needs to serve.
Build the right big data architecture before investing in infrastructure.
CMARIX evaluates your existing data ecosystem, growth projections, workload characteristics, latency requirements, and business objectives to design a scalable big data architecture. Our consultants define the right processing framework, storage model, cloud strategy, and technology stack while creating a roadmap that balances performance, scalability, governance, and long-term operating costs.
Stack: Architecture Assessment | Platform Selection | Cloud Strategy | Technology Roadmap | TCO Analysis
Connect, ingest, and unify data from every business system.
We develop scalable data ingestion pipelines that collect structured, semi-structured, and unstructured data from enterprise applications, databases, APIs, IoT devices, event streams, and third-party platforms. Every pipeline is designed for high throughput, reliability, fault tolerance, and seamless integration across your big data ecosystem.
Stack: Apache Kafka | Apache NiFi | Apache Spark | Kafka Connect | Debezium (CDC) | Airbyte | Apache Airflow
Process continuous data streams with minimal latency.
CMARIX builds streaming applications that analyze events on the go and provide solutions like real-time analytics, monitoring, fraud detection, predictive maintenance, recommendation systems, and operational intelligence. We design highly scalable streaming frameworks that can handle millions of events consistently.
Stack: Apache Kafka | Apache Flink | Spark Streaming | AWS Kinesis | Azure Event Hubs | Google Pub/Sub
Build scalable storage platforms for analytics, AI, and business intelligence.
We create and build state-of-the-art data lake architecture and lakehouse designs for enterprise data to ensure efficient data analytics and machine learning processes. We focus on reducing storage costs, improving query speed, and simplifying data management in our cloud computing environment.
Stack: Delta Lake | Apache Iceberg | Apache Hudi | AWS S3 | Azure Data Lake Storage | Google Cloud Storage | Databricks
Process massive datasets with distributed computing frameworks.
Our engineers develop distributed data processing pipelines for batch, interactive, and large-scale analytical workloads. Every processing workflow is optimized using partitioning strategies, query optimization, workload balancing, and parallel execution to ensure consistent performance as data volumes continue to grow.
Stack: Apache Spark | Apache Flink | Trino | Presto | PySpark | Scala | Databricks | AWS EMR
Deploy cloud-native big data platforms that scale with demand.
CMARIX designs and implements managed big data platforms across AWS, Microsoft Azure, and Google Cloud. We architect cloud-native environments that combine distributed compute, storage, orchestration, security, and monitoring while reducing operational complexity and infrastructure management overhead.
Stack: AWS EMR | AWS Glue | AWS Kinesis | Azure Databricks | Azure HDInsight | Google Cloud Dataproc | Google Cloud Dataflow
Modernize legacy big data platforms without disrupting operations.
We transfer the existing Hadoop ecosystem, Hive data warehouse, HDFS storage system, and MapReduce jobs to the modern cloud-based big data architecture. All transfers include verification, performance tuning, compatibility tests, and gradual deployment to ensure business continuity and data integrity.
Stack: Hadoop Migration | Hive to Spark | HDFS Migration | MapReduce Modernization | Databricks | AWS EMR | Google Cloud Dataproc
Keep your big data platform secure, optimized, and continuously available.
CMARIX offers continuous management for your platform, load balancing, clustering management, scaling infrastructure, security updates, cost optimization, and performance tuning. With our managed operations, you can be assured that your big data platform will remain stable, while your engineering team can work towards innovations.
Stack: Platform Monitoring | Cluster Optimization | Performance Tuning | SLA-Based Support | Cost Optimization | Security Management | Capacity Planning
A production big data platform moves data through four distinct layers, each with defined throughput contracts and failure recovery mechanisms. This is the architecture CMARIX implements for big data development services engagements at petabyte scale.
A structured, milestone-gated process that moves from requirements to a production big data platform with validated throughput benchmarks and no big-bang cutover risk.
The evaluation of your existing data environment would include an analysis of the data sources, the ingestion rates, processing, queries, projections for growth, and latency requirements before you can begin designing your architecture. This way, your data environment will be designed based on actual workloads rather than expected capacity.
Timeline: Week 1–2
Deliverables: Data Source Inventory · Volume & Velocity Assessment · Workload Profiling · Consumer SLA Requirements · Gap Analysis
We design the target state of the distributed platform; we choose compute/storage capabilities based on your measured load, and we model the total cost of ownership at your existing and forecasted data scale. All architecture decisions are recorded along with rationale.
Timeline: Week 2–4
Deliverables: Architecture blueprint · Platform selection matrix · Partitioning strategy · Cost model · Migration sequencing
The cluster is provisioned, ingestion connectors and processors are developed, and orchestration is done via 2-week sprints; every processor has been benchmarked for performance capability, failover capability, and documentation.
Timeline: Week 4–14
Deliverables: Cluster configuration · Ingestion pipelines · Processing jobs · Orchestration configuration · Unit and integration tests
These large-scale production workloads are used to verify throughput, latency, infrastructure utilization, and processing cost before going into production. Bottleneck tuning is performed, resource allocation is fine-tuned, and scalability is confirmed to achieve business and operational goals.
Timeline: Week 10–14
Deliverables: Throughput Benchmarks · Scalability Validation · Latency Analysis · Cost Optimization Report · Performance Tuning Documentation
We operate both old and new systems simultaneously, test output equivalence for every consumer, and perform the switch seamlessly without any risk of downtime. Migration of existing Hadoop workloads within the company follows the exact same process.
Timeline: Week 12–16
Deliverables: Parallel run results · Output validation report · Cutover plan · Consumer sign-off · Runbooks
Full documentation, architecture diagrams, operational manuals, and knowledge transfers are offered to ensure continued ownership of the platform. For those that need help with operations on a continuing basis, CMARIX offers managed services that include monitoring, tuning, maintenance, and security updates of the platform.
Timeline: Week 14 onward
Deliverables: Platform Documentation · Architecture Runbooks · Team Handover · SLA-Based Managed Support · Ongoing Platform Optimization
CMARIX provides large-scale data platforms that are auditable and controllable by your security, compliance, and financial teams. Each platform is delivered with access control, data lineage, data quality monitoring, and cost governance pre-configured from the get-go.
CMARIX engineers select distributed processing frameworks, storage formats, and cloud platforms based on your data volumes, latency targets, and cost constraints, not default preferences.
CMARIX provides big data analytics and platform engineering services for businesses with data sizes, event rates, and complexity that exceed the capabilities of regular tools.
Telecommunications companies generate massive volumes of network, subscriber, and usage data that require high-throughput processing. CMARIX builds big data solutions for processing call detail records, analyzing network events, monitoring subscriber behavior, and supporting real-time network operations. Distributed data architectures process billions of events while maintaining scalability and reliability. These platforms support network performance analytics, customer behavior analysis, anomaly detection, capacity planning, and proactive service optimization across complex telecommunications environments.
CMARIX is an innovative big data development company that helps companies tackle the challenges that arise when using traditional ETL methods and legacy databases by creating scalable cloud-based data platforms. Our experience includes distributed computing and cloud platforms, batch and real-time data engineering, performance and cost optimization, and migration, governance, and managed operations services. By developing highly performant, scalable, and resilient data ecosystems, companies can process huge amounts of data efficiently for Clients
In-House Data and Platform Engineers
Client Retention Rate
Years in Product Engineering
CMARIX structures big data development services engagements to match your platform maturity and scale requirements, from a proof-of-concept processing pipeline to a fully operated enterprise big data platform.
Validate your distributed processing architecture against your real data volumes. CMARIX benchmarks one processing workload, validates throughput and cost at scale, and delivers an architecture recommendation and investment decision framework.
What you get:
A dedicated team of distributed systems engineers, cloud architects, and data engineers builds, tunes, and delivers your production big data platform end to end with full throughput validation and handover documentation.
What you get:
CMARIX operates your big data infrastructure: cluster health monitoring, job failure response, performance tuning as volumes grow, cost optimization reviews, security patching, and monthly platform health reporting.
What you get:
Big data development services cover the design, build, and operation of distributed data processing platforms that handle data volumes, velocities, and varieties that standard relational databases and single-node tools cannot process reliably. This includes data lakes, streaming pipelines, Spark and Flink processing jobs, distributed query engines, and the governance and observability infrastructure that keeps them running in production.
Standard data engineering tools handle most workloads well up to a few terabytes of data processed per day. You need big data solutions when: your processing jobs take hours rather than minutes and cannot be optimized further, your data volumes are growing faster than your current infrastructure can absorb, you need sub-second latency on queries across billions of rows, or your streaming event rates exceed what a single Kafka consumer can process. CMARIX assesses your actual workloads before recommending a distributed platform.
A data lake stores raw, unprocessed data in object storage (S3, ADLS, GCS) with minimal structure. A data lakehouse adds a structured table-format layer (Delta Lake, Iceberg, Hudi) on top of object storage that provides ACID transactions, schema enforcement, and SQL query performance at the same cost-effective storage cost. Most modern big data platforms CMARIX builds use a lakehouse architecture because it combines the flexibility and cost of object storage with the reliability and queryability of a warehouse.
For a Proof of Concept Sprint that tests a single processing workload, costs range between USD 12,000 and USD 22,000 for 3 to 5 weeks. For a full-fledged production platform setup, costs vary from USD 50,000 to USD 150,000 and take anywhere between 3 and 6 months. The cost of a managed operations service retainer is in the range of USD 8,000 to USD 25,000 monthly.
Our CMARIX services provide cloud big data solutions using AWS (EMR, Glue, Kinesis, Redshift), Azure (HDInsight, Databricks, Synapse, ADLS), and GCP (Dataproc, Dataflow, BigQuery, Pub/Sub). In addition, we offer Databricks, a cloud-agnostic Spark platform suitable for customers who want to work consistently in any cloud environment.
Yes. CMARIX provides structured migrations from Hadoop clusters to the cloud, including HDFS-to-object-storage migration, Hive-to-Delta Lake or Iceberg migration, MapReduce-to-Spark job transformation, and decommissioning of on-premises clusters. We migrate both old and new systems simultaneously, verify output equivalence for all downstream consumers, and migrate gradually.
Cost architecture is a critical component of all data platforms at CMARIX. We define auto-scaling policies, spot/preemptible VMs strategy, cold data management policy for data migration to cheaper storage tiers, result caching strategy, and job-level cost dashboards. We also conduct regular cost optimization assessments of our managed operations services as data sets and jobs grow more complex.
Start with a big data architecture review. CMARIX will measure your current data volumes, query patterns, processing runtimes, and cost profile, then recommend whether a distributed platform investment is justified and what architecture fits your requirements. Most clients start with a PoC Sprint to benchmark their most critical workload at scale before committing to a full platform build.
Your unique concepts will be crafted into a remarkable end result by our team.
