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Cloud Problems: Solved

The foundation for frictionless data-driven business.

Expert services to accelerate data science exploration, discovery, and modeling.

By speeding up data prep, the experts at CloudGeometry help keep your data science team moving. Our Data Science Pipeline Services transform data sources into a reliable source of continuous agility at scale — to solve business problems faster.

Harness automation

Ensure reliable data flow and drive business agility

Seamlessly expand supply

Get the datasets you need for more nimble exploration

Optimize scale / spend

Get the processing power of data science infrastructure

How it works

At CloudGeometry, we give data science teams the power of elastic compute and storage resources — without the headaches of cloud pipeline operations. We build and run data prep end-to-end, so data scientists and app developers don’t waste time carrying water from the data lake themselves.

It means we take care of custom coding, building workflows, event-driven frameworks, continuous data refinement, and production deployment — so data endpoints are always fresh and available.

How we can help

Data Science Automation

Assess data sources and data science roadmap, to calibrate baseline for continuous agile data delivery.

End-to-end Buildout

Architect and deploy cloud data pipeline infrastructure using key opensource/AWS technologies tailored to iterative data exploration.

Turnkey Onboarding

Orderly knowledge transfer to ensure your data science team’s hands-on mastery of pipeline management and execution.

Production Readiness

Operationalize configuration and management with Docker/Kubernetes to maximize uptime and scalability, backed by 24/7 support.

Dataflow Integration

How CloudGeometry Dataflow Integration Blueprint uses auto-recovery for continuous data integrity

Data Science Pipeline

Using our Data Science Pipeline Blueprint helps you operationalize for sustainable strategic value

How we do it

We have proven expertise building data pipelines with both Open Source and AWS Native technology stacks. Making it work, even as the flow of data accelerates, is always a trade-off: it’s a balancing act across time to market, budget constraints, flexibility to move to another cloud provider, and even understanding the flavor of problem-solving styles within an organization.

Alex Ulyanov
CTO, CloudGeometry

Open Source Stack

AWS Native Tools

Data Ingest

Kafka Connect

Blazing fast solution for well-defined data sources


Ideal solution when data transformation and cleansing is required

Kinesis Firehose

Fully managed service for real-time streaming


Best for bulk data migration of relatively slow amount of data

Snowball (Import/Export)

Good for bulk data migration of large amount of data from on premises to cloud when internet connection is slow

Storage Gateway

When you want to integrate existing on-premises data processing platform with AWS cloud
Data Storage


Good when strict consistency is not a concern; offers cross-datacenter replication out of the box


Good at intensive reads and integrates well with Apache big data stack


Scalable distributed object-, block- and file-level storage without a single point of failure


Highly scalable distributed file system for high-load cluster computing


Best when low latency and infinite scalability is required


Best managed storage for OLAP


Best for blob storage
Scale-out data processing


Best per-message streaming processing framework when fault-tolerant local state is required

Spark Streaming

Good for micro-batching with scalability and high-throughput in mind

Kafka Streams

Ideal when input and output data is stored in Kafka and you need simple data processing


Ideal for quick-running per-message processing


Best when you need Spark, Hadoop, or Hbase and don’t want to manage it


Novice friendly data visualization and analysis tool with interactive workflows


Turnkey solution when rich and interactive dashboards are required
Machine Learning


Best when you need full control over your network


Best when you want to add ML to you Apache big-data stack


Best option when you want to quickly build and test a network


One-click solution to build, train, and deploy machine learning models

ML Studio

GUI to build your machine learning model

Connect the dots with CloudGeometry.