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. Key challenges.
Dynatrace
JULY 6, 2022
Scripts and procedures usually focus on a particular task, such as deploying a new microservice to a Kubernetes cluster, implementing data retention policies on archived files in the cloud, or running a vulnerability scanner over code before it’s deployed. Big data automation tools. How does IT automation work?
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Abhishek Tiwari
DECEMBER 22, 2017
Cheap storage and on-demand compute in the cloud coupled with the emergence of new big data frameworks and tools are forcing us to rethink the whole ETL and data warehousing architecture. There is a strong argument for ELT i.e. extract, load, and transform model. Classic ETL. There is no capacity planning needed.
All Things Distributed
OCTOBER 7, 2015
Today, I am excited to share with you a brand new service called Amazon QuickSight that aims to simplify the process of deriving insights from a wide variety of data sources in a fast and affordable manner. QuickSight is a fast, cloud native, scalable, business intelligence service for the 1/10th the cost of old-guard BI solutions.
The Netflix TechBlog
MAY 26, 2020
Service Segmentation: The ease of the cloud deployments has led to the organic growth of multiple AWS accounts, deployment practices, interconnection practices, etc. Cloud Network Insight is a suite of solutions that provides both operational and analytical insight into the Cloud Network Infrastructure to address the identified problems.
The Netflix TechBlog
MARCH 25, 2019
We adopted the following mission statement to guide our investments: “Provide a complete and accurate data lineage system enabling decision-makers to win moments of truth.” Netflix’s diverse data landscape made it challenging to capture all the right data and conforming it to a common data model.
Abhishek Tiwari
DECEMBER 27, 2017
ETL refers to extract, transform, load and it is generally used for data warehousing and data integration. With the arrival of new cloud-native tools and platform, ETL is becoming obsolete. There are several emerging data trends that will define the future of ETL in 2018. Machine learning meets data integration.
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