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Kubernetes for Big Data Workloads

Abhishek Tiwari

Kubernetes has emerged as go to container orchestration platform for data engineering teams. In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. Organisations are already using Kubernetes for a variety of workloads [1] [2] and data workloads are up next. Storage provisioning.

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Friends don't let friends build data pipelines

Abhishek Tiwari

Unfortunately, building data pipelines remains a daunting, time-consuming, and costly activity. Not everyone is operating at Netflix or Spotify scale data engineering function. Often companies underestimate the necessary effort and cost involved to build and maintain data pipelines.

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5 data integration trends that will define the future of ETL in 2018

Abhishek Tiwari

More importantly, UDM utilizes a single storage backend with benefits of multiple storage systems which avoids moving data across systems hence data duplication, and data consistency issues. Common in-memory data interfaces. It generally improves performance by placing frequently accessed data in memory.