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As of 2022, the global market for digital twins was valued at $11.12 billion — and experts estimate an impressive annual growth rate of 37.5% from 2023 to 2030.
Digital twins help connect the physical and virtual worlds, allowing workers to timely identify when things need fixing, work more efficiently, and create less waste. So, embracing the potential of digital twins is a crucial move as the logistics sector steps into the new Industry 5.0 era.
Now that I’m free to share the news that I’ve landed at Kentik — a visionary company filled by an amazing group of folks who believe that the value of their team goes far beyond what they might offer to the business — I wanted to take a minute to reflect on my job search, comment on the state of the job market, and share some lessons I’ve picked up along the way.
Let me be clear — I’m under no illusion that the world has breathlessly awaited the thoughts of a middle-aged white dude and will now, graced with my heretofore-undiscovered wisdom, be a truly better place. People with far more knowledge, experience, and expertise have written and spoken on this topic, with data and examples that are far more eloquent and compelling than anything I could hope to share.
In the midst of our ever-expanding digital landscape, data management undergoes a metamorphic role as the custodian of the digital realm, responsible for the ingestion, storage, and comprehension of the immense volumes of information generated daily. At a broad level, data management workflows encompass the following phases, which are integral to ensuring the reliability, completeness, accuracy, and legitimacy of the insights (data) derived for business decisions.
Within these stages, Data Ingestion acts as the guardian of the data realm, ensuring accurate and efficient entry of the right data into the system. It involves collecting targeted data, simplifying structures if they are complex, adapting to changes in the data structure, and scaling out to accommodate the increasing data volume, making the data interpretable for subsequent phases. This article will specifically concentrate on large-scale Data Ingestion tailored for both batch and near real-time analytics requirements.