DataForge, a leading data architecture solution provider, introduces the market’s first scalable, automated, and prepackaged data architecture platform. Tailor-made for companies building new data lakehouses on the Databricks platform, DataForge streamlines the largest bottleneck to digital enablement and gives enterprises the flexibility they need to mature their data capabilities effectively and efficiently.
Top AI ML Insights: Why Architecture Matters with Generative AI and Cloud Security
Until now, designing and evolving data architecture over time often required a full rebuild every three to five years, with build timelines lasting as long as two years. Enter DataForge. DataForge’s architecture-as-software approach evolves architecture, structures, and technologies for customers over time to eliminate rebuilds and cut timelines down by over 60 percent. It uses predefined structures, auto dependencies, and auto-code checking to optimize Data Engineering workflows, reduce errors, and enforce best practices – allowing for true agile development and reducing operational overhead to maintain the platform by over 40 percent.
“Enterprises can hardly afford to have their data engineers focused on boilerplate code, manual management tasks, and constant rebuilds just to stay competitive,” explained Matt Kosovec, DataForge co-founder and CEO. “DataForge empowers companies to keep teams focused on extracting value from the data, and to make smarter, more informed decisions.”
DataForge offers a wide range of capabilities, including:
Centralize data seamlessly: Designed with the growing enterprise in mind, DataForge can centralize data from hundreds of databases in days and utilizes custom file format processing, ongoing synchronization, and advanced mapping with the ability to quickly adjust and transform data elements against any target data warehouse model or even publish data to custom file formats and APIs.
Go beyond data architecture: Extensive cluster management and automation features can help save over 40% on cloud spend. With a fully automated orchestration and workflow engine that eliminates the need to manually define dependencies or directed acyclic graphs. To complement the Databricks Unity Catalog, DataForge offers an expanded catalog with queryable configurations and operational metrics. It also comes with the most detailed, comprehensive column-level lineage available to track and manage transformation logic and assist with audits.
Data Management News: Planet Completes Acquisition of Sinergise; Set to Expand Planet’s Earth Data Platform
Add Data Product Offerings to your existing Software-as-a-Service: DataForge provides customers with simplified data models, self-service reporting enablement, and seamless integration with customer data services. In addition, it can help companies enhance their core platforms by deploying and operating ML/AI/LLM services within the lakehouse framework, enabling advanced data analysis and predictive modeling.
“Service Logic has received tremendous benefit from our partnership with the DataForge team and technology over the past six years,” said Levi Reeves, VP of Integrations and FP&A at Service Logic, a DataForge customer. “The platform has accelerated our data transformation and integration across a challenging landscape of systems, generating compelling operational metrics, and demonstrating the value of our business model, all without the need for a large team of in-house data experts.”
Recommended CIO Influence News: Liquidware Launches Liquidware Ready Program
[To share your insights with us, please write to sghosh@martechseries.com]
