Data Engineering

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A Modern Data Strategy for the U.S. Department of Defense

In October of 2020, the DOD published a new Data Strategy targeted at using “data at speed and scale for operational advantage and increased efficiency.”

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WHITE PAPER

DataOS Data Evolution & Modern Data Management

The world is rapidly shifting to a digital first model for every organization due to world events. The long-known expansion of the size of data has suddenly kicked up its pace.

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A Modern Data Strategy for Enterprises

Regardless of industry, size, or product offering, every company has to ask the same question—“Regardless of the amount of data I have, how much of it is actually usable?”

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Often the case with legacy systems, SaaS applications, data warehouses, and data lakes, your data is widely distributed across platforms and technologies. Although this data is massively abundant, it is also isolated and difficult to access and use. It's simply due to how data engineering and management has evolved over the decade.

A crucial part of analytics, data engineering is often overlooked when compared to data science. Michelle Goetz, Principal Analyst at Forrester Research, shared that there were 12 times as many unfilled data engineering jobs as data scientist jobs. Breadth and depth of required skills — which is likely to include Java, Python, or Scala — limits the number of qualified people to work as data engineers. Knowledge and understanding of different architectures, frameworks, and technologies is crucial, ranging from relational databases to NoSQL, from batch ETL to data stream processing, and from traditional data warehousing to data lakes and beyond. Data engineers are simultaneously data engineers and software engineers. Simply put, there is no data without data engineers. And with the massive demand outpacing available talent, data fabrics can automate a significant portion of data engineering when it comes to reuse and repeatability, expanding the organization's data engineering capacity.

On the same note, fast, reliable, and repeatable delivery of clean, accurate data for reporting and analytics remains a big challenge. When data is decentralized and spread across multiple platforms, pipeline processing must be able to cover on-premise, cloud, multi-cloud, and hybrid environments. Ensuring these data pipelines remain sustainable is a major challenge as business needs, data sources, and technologies continuously change and evolve. Entire analytics supply chains can get disrupted when a single pipeline fails, and any repair work is a painfully slow undertaking. Emerging practices hold promise but the shift that needs to be made is a massive one. Data operations is simply not practical without automatrion.

In this respect, DataOS aims to fully support the automation needed for DataOps to succeed with capabilities to automate across on-premises, cloud, and hybrid data environments.

Want to see how DataOS solves your specific integration and data management challenges?

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