Data Engineering Hardware Processing Storage 
Abhishek Tiwari
DECEMBER 27, 2017
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. Custom schedulers.
All Things Distributed
AUGUST 24, 2012
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.
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Abhishek Tiwari
JULY 12, 2018
In recent times, in order to gain valuable insights or to develop the data-driven products companies such as Netflix, Spotify, Uber, AirBnB have built internal data pipelines. If built correctly, data pipelines can offer strategic advantages to the business. Depending on frameworks, data processing units (a.k.a
All Things Distributed
OCTOBER 7, 2015
In such a data intensive environment, making key business decisions such as running marketing and sales campaigns, logistic planning, financial analysis and ad targeting require deriving insights from these data. However, the data infrastructure to collect, store and process data is geared toward developers (e.g.,
Abhishek Tiwari
DECEMBER 27, 2017
A common theme across all these trends is to remove the complexity by simplifying data management as a whole. In 2018, we anticipate that ETL will either lose relevance or the ETL process will disintegrate and be consumed by new data architectures. Unified data management architecture. Common in-memory data interfaces.
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