Platform Blueprints
Built to drive data science infrastructure.
Flexible cloud data pipeline powering iterative advanced analytics at scale.
Data Science Pipeline
A well-architected infrastructure blueprint designed to adapt to the continuous iteration that data science demands.
Harness scale and automation
Keep analytics professionals productive
Operationalize data science
Provide sustainable strategic value
Quickly build data pipelines
Solve business problems faster
The Problem
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.
The Solution
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 data ingest, streaming, storage, microservices, and real-time processing, 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.
Get good data in so you can
get better data out.
Key Features
Acquire/Ingest Any Source Data
Mix/match transactional, streaming, batch submissions from any data store.
Build Canonical Datasets
Characterize and validate submissions; enrich, transform, maintain as curated datastores.
Data Science Workbench
Notebook-enabled workflows for all major libraries: R, SQL, Spark, Scala, Python, even Java, and more.
Operationalize Machine Learning
Manage data flows and ongoing jobs for model building, training, and deployment.
Analytics as Code
Foster parallel development and reuse w/rigorous versioning and managed code repositories.
Elastic Microservices
Easily configure and run Dockerized event-driven, pipeline-related tasks with Kubernetes.
End-to-end Data Versatility
Flexible data topologies to flow data across many-to-many origins and destinations.
Simplify Data Exploration
Leverage search/indexing for metadata extraction, streaming, data selection.
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. that 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
24/7 NOC & Expert DevOps teams
RedShift
Aurora/RDS
Amazon S3
Athena
Elastic Search
DynamoDB
Kinesis
EMR
Elastic Container Service for Kubernetes (EKS)
Sagemaker
Glue
