Platform Blueprints

Built to drive data science infrastructure.

Flexible cloud data pipeline powering iterative advanced analytics at scale.

SaaS Acceleration

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

Data Science Pipeline
Harness scale and automation
Keep analytics professionals productive
Data Science Pipeline
Operationalize data science

Provide sustainable strategic value

Data Science Pipeline
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.

Data Science Pipeline
Platform Blueprints
One-off processes, minimal reuse
Different tools and approaches make standardization difficult without introducing unnecessary rigidity.
Platform Blueprints
New ideas don’t fit old data models

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

Platform Blueprints
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

Data Science Pipeline
Acquire/Ingest Any Source Data

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

Data Science Pipeline
Build Canonical Datasets

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

Data Science Pipeline
Data Science Workbench

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

Data Science Pipeline
Operationalize Machine Learning

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

Data Science Pipeline
Analytics as Code

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

Data Science Pipeline
Elastic Microservices

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

Data Science Pipeline
End-to-end Data Versatility

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

Data Science Pipeline
Simplify Data Exploration

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

Data Science Pipeline
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.

Data Science Pipeline
Alex Ulyanov
CTO, CloudGeometry

Scale-out Data Lake for Data Science

24/7 NOC & Expert DevOps teams

Data Science Pipeline

RedShift

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

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

Data Science Pipeline

Aurora/RDS

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

Data Science Pipeline

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

Data Science Pipeline

Athena

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

Data Science Pipeline

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

Data Science Pipeline

DynamoDB

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

Data Science Pipeline

Kinesis

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

Data Science Pipeline

EMR

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

Data Science Pipeline

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

Data Science Pipeline

Sagemaker

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

Data Science Pipeline

Glue

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

Data Science Pipeline

QuickSight

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

All set with  a 2 + b 2 = c 2 ?
Great. Ready to solve for x n ?

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