Harness scale and automation to keep analytic professionals productive
A well-architected infrastructure designed to adapt to the continuous iteration that data science demands
Operationalize data science to provide sustainable strategic value
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 ElevationData, we think there’s a better way
The Data Science Pipeline by ElevationData 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
Easily configure and run Dockerized event-driven, pipeline-related tasks with Kubernetes
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.