A well-architected infrastructure designed to adapt to the continuous iteration that data science demands

Harness scale and automation

Harness scale and automation to keep analytic professionals productive

Operationalize data science

Operationalize data science to provide sustainable strategic value

Quickly build data pipelines

Quickly build data pipelines to solve business problems faster


Is keeping data science productive becoming an uphill struggle?

Your team has the skills – business knowledge, statistical versatility, programming, modeling, and visual analysis – to unlock the insight you need. But you can’t connect the dots if they can’t connect reliably with the data they need.

One-off processes, minimal reuse

Different tools and approaches make standardization difficult without introducing unnecessary rigidity

New ideas don’t fit old data models

Operational processes create data that ends up locked in silos tied to narrow functional problems

Friction vs. innovation

Experimentation can be messy, but out-of-the-box exploration needs to preserve the autonomy of data scientists


At CloudGeometry, we think there’s a better way

The Data Science Pipeline by CloudGeometry gives you faster, more productive automation and orchestration across a broad range of advanced dynamic analytic workloads. It helps you engineer production-grade services using a portfolio of proven cloud technologies to move data across your system.

Built from the leading AWS technologies for ingest, streaming, storage, microservices, and processing technologies, it gives you the versatility to experiment across data sets, from early phase exploration to machine learning models. You get a data infrastructure ideally suited for unique demands of access, processing, and consumption throughout the data science and analytic lifecycle.

Data Science Pipeline: Key Features

  • Acquire/Ingest Any Source Data

    Mix/match transactional, streaming, batch submissions from any data store.

  • Operationalize Machine Learning

    Manage data flows and ongoing jobs for model building, training, and deployment

  • End-to-end Data Versatility

    Flexible data topologies to flow data across many-to-many origins and destinations.

  • Build Canonical Datasets

    Characterize and validate submissions; enrich, transform, maintain as curated datastores

  • Analytics as Code

    Foster parallel development and reuse w/rigorous versioning and managed code repositories

  • Simplify Data Exploration

    Leverage search/indexing for metadata extraction, streaming, data selection

  • Data Science Workbench

    Notebook-enabled workflows, all major libraries: R, SQL, Spark, Scala, Python, even Java and more

  • Elastic Microservices

    Easily configure and run Dockerized event-driven, pipeline-related tasks with Kubernetes

  • Agile Analytics

    Cut friction of transformation, aggregation, computation; more easily join dimensional tables with data streams, etc.

How We Do It

Data-science projects can go sideways when they get in over their head on data engineering and infrastructure tasks. They get mired with a Frankenstein cloud that undermines repeatability and iteration. 

We’ve solved for that with a generalizable, production-grade data pipeline architecture; it’s well-suited to the iteration and customization typical of advanced analytics workloads and data flows. Tghat provides much more direct path for achieving real results that are both reliable and scalable.

Alex Ulyanov
CTO, CloudGeometry

Scale-out Data Lake for Data Science

End-to-end Analytics Processing


Fast, scalable, simple, and cost-effective way to analyze data across data warehouses/data lakes

10x faster performance optimized by machine learning, massively parallel query execution, and columnar storage


Cloud native RDBMS combines cost-efficient elastic capacity and automation to slash admin overhead

Engines include PostgreSQL, MySQL, MariaDB, Oracle Database, SQL Server and Amazon Aurora,

Amazon S3

Store and retrieve any amount of data from anywhere on the Internet; extremely durable, highly available, and infinitely scalable at very low costs

Easily create and store data at any and every stage of data pipeline, for both sources and destinations


Interactive query service using standard SQL to analyze data stored in Amazon S3

Leverages S3 as a versatile unified repository, with table, partition definitions, and schema versioning

Elastic Search

Deploy, secure, operate, and scale Elasticsearch to search, analyze, and visualize data in real-time.

Integrates seamlessly with Amazon VPC, KMS, Kinesis, AWS Lambda, IAM, CloudWatch and more


Nonrelational database delivers reliable performance at any scale w/single-digit millisecond latency

Built-in security, backup and restore, with in-memory caching, low-latency access


Ingests/process/analyze data in real time; take action instantly. No need to wait for before processing begins.

Extensible to application logs, website clickstreams, and IoT telemetry data for machine learning


Elastic Big Data Infrastructure process vast amounts of data across dynamically scalable cloud infrastructure

Supports popular distributed frameworks such as Apache Spark, HBase, Presto, Flink and more

Elastic Container Service for Kubernetes (EKS)

Deploy, manage, and scale containerized applications using Kubernetes on AWS on EC2

Microservices for both sequential or parallel execution; use on-demand, reserved, or spot instances


Quickly and easily build, train, and deploy machine learning models at any scale

pre-configured to run TensorFlow, Apache MXNet, and Chainer in Docker containers


Fully managed extract, transform, and load (ETL) service to prepare & load data for analytics

Generates PySpark or Scala scripts, customizable, reusable, and portable; define jobs, tables, crawlers, connections


Cloud-powered BI service that makes it easy to build visualizations and perform ad-hoc and advanced analysis

Choose any data source; combine visualizations into business dashboards and share securely

Clients who have benefited from our solutions

Development Partner Client A&E
Development Partner Client Thinfilm
Development Partner Client GE Digital
Development Partner Client Adobe
Development Partner Client Gali Health
Development Partner Client Nurego
Development Partner Client CaaStle
Development Partner Client GAP
Development Partner Client Urban Outfitters
Development Partner Client American Eagle
Development Partner Client Ann Taylor
Development Partner Client New York & Company
Development Partner Client Express
Development Partner Client Rebecca Taylor
Development Partner Client Gwynnie Bee
Development Partner Client Zypmedia
Development Partner Client GlobeIn
Development Partner Client Krypton
Development Partner Client Amdocs
Development Partner Client Culver
Development Partner Client SeaWorld
Development Partner Client Playphone
Development Partner Client Neurotech ADHD Solutions
Development Partner Client King.com
Development Partner Client Imantics

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