Data Lake Development

Built to Last from the Start

Too many Data Lakes fail

Building a Data Lake is a complex task for any organization, requiring the integration of several different technologies for the purposes of data storage, ingestion, processing, operations, governance, security, and data analytics. Finding, and implementing, the right mix is challenging, which is why so many first-run enterprise Data Lakes fail.

At Next Pathway, we help organizations to architect and implement long-lasting and well-governed Data Lakes their business can rely on for years to come. With a properly designed Data Lake, our clients experience huge benefits to their data consumption pipelines, including reduced data-prep times, easier data discovery and faster business insights; while meeting regulatory requirements regarding data privacy, security and data governance.

Starting the journey toward a more data-informed business is important. Decades ago, CIOs may recall that data wasn’t even a topic readily discussed outside of IT. Fast forward to today, with the magnitude of analytic needs and the diversity of tools currently available, data is now a board-level topic where the focus is on leveraging it more effectively to drive business growth. Data Lakes are a major ingredient to help businesses store, manage and consume their data faster, cheaper, and more secure.

Want to Build a Successful Data Lake?

Get the conversation started and let us know a bit about your project

Accelerators

To assist our clients, where necessary, we’ve developed a set of accelerators to aid in the data manufacturing pipeline – providing smarter data ingestion and governance.

Data Manufacturing Pipeline

Cornerstone® is a patented, fully automated, metadata-driven data platform that automates the ingestion, technical standardization, security, metadata capture and lineage of data into big data environments or the Cloud.

Data Curation Accelerator

FUSE is a framework to allow large enterprises to design, build and deploy large-scale data integration workflows inside of Big Data environments.

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