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Cloud computing lowered the cost and improved accessibility to tools for storing large volumes of data. In the early 2000s, Hadoop caused a revolution in large scale batch processing. Since then, companies have been building ways to store and access their data faster and more efficiently.
At the same time, the sheer volume of data has increased and machine learning has given rise to methods of extracting signal from seemingly inconsequential data points. This confluence of factors gave rise to the role of the data engineer. A data engineer defines the data pipeline and supports data scientists and machine learning engineers.
Tobias Macey hosts the “Data Engineering Podcast,” where he covers the fast moving world of data engineering–including databases, cloud providers, and open source tools. Tobias and I covered a range of topics in the data engineering space and also spent significant time discussing the world of software engineering podcasting.
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Azure Container Service simplifies the deployment, management and operations of Kubernetes. You can continue to work with the tools you already know, such as Helm, and move applications to any Kubernetes deployment. Integrate with your choice of container registry, including Azure Container Registry. Also, quickly and efficiently scale to maximize your resource utilization without having to take your applications offline. Isolate your application from infrastructure failures and transparently scale the underlying infrastructure to meet growing demands—all while increasing the security, reliability, and availability of critical business workloads with Azure. Check out the Azure Container Service at aka.ms/sedaily.
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There’s a new open source project called Dremio that is designed to simplify analytics. It’s also designed to handle some of the hard work, like scaling performance of analytical jobs. Dremio is the team behind Apache Arrow, a new standard for in-memory columnar data analytics. Arrow has been adopted across dozens of projects – like Pandas – to improve the performance of analytical workloads on CPUs and GPUs. It’s free and open source, designed for everyone, from your laptop, to clusters of over 1,000 nodes. At dremio.com/sedaily you can find all the necessary resources to get started with Dremio for free. If you like it, be sure to tweet @dremiohq and let them know you heard about it from Software Engineering Daily. Thanks again to Dremio, and check out dremio.com/sedaily to learn more.