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Back-to-Basics Weekend Reading - The 5 Minute Rule - All Things.

All Things Distributed

The AWS team launched this week Amazon Glacier , a cold storage archive service at the very low price point of $0.01 Which makes this week a good moment to read up on some of the historical work around the costs of data engineering. I am in the midst of my South America tour in the beautiful but very cold Santiago, Chile.

Storage 110
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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.

Latency 63
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Expanding the Cloud: Introducing Amazon QuickSight

All Things Distributed

However, the data infrastructure to collect, store and process data is geared toward developers (e.g., In AWS’ quest to enable the best data storage options for engineers, we have built several innovative database solutions like Amazon RDS, Amazon RDS for Aurora, Amazon DynamoDB, and Amazon Redshift.

Cloud 138
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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.