Data engineering: Solved

The foundation for frictionless data-driven business

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

Analytics & Data Science Pipeline Services

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
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 open-source/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.

Learn more about proven CloudGeometry Services

How CloudGeometry Dataflow Integration Blueprint uses auto-recovery for continuous data integrity
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 image
Alex Ulyanov
CTO, CloudGeometry
Open Source Stack
AWS Native Tools
Data Ingest
Kafka Connect
Blazing fast solution for well-defined data
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 amountof 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.