Architecture Big Data Event Storage 
Highly Scalable
AUGUST 20, 2013
The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. This system has been designed to supplement and succeed the existing Hadoop-based system that had too high latency of data processing and too high maintenance costs.
Uber Engineering
OCTOBER 17, 2018
To accomplish this, Uber relies heavily on making data-driven decisions at every level, from forecasting rider demand during high traffic events to identifying and addressing bottlenecks … The post Uber’s Big Data Platform: 100+ Petabytes with Minute Latency appeared first on Uber Engineering Blog.
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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. Storage provisioning.
Dynatrace
MARCH 24, 2023
Problems include provisioning and deployment; load balancing; securing interactions between containers; configuration and allocation of resources such as networking and storage; and deprovisioning containers that are no longer needed. How does container orchestration work? The post What is container orchestration?
The Netflix TechBlog
OCTOBER 15, 2019
Beyond data synchronization, some applications also need to enrich their data by calling external services. Delta is an eventual consistent, event driven, data synchronization and enrichment platform. In Netflix the microservice architecture is widely adopted and each microservice typically handles only one type of data.
Dynatrace
JANUARY 16, 2023
Redis is an in-memory key-value store and cache that simplifies processing, storage, and interaction with data in Kubernetes environments. Messaging : RabbitMQ and Kafka are the two main messaging and event streaming systems used. Big data : To store, search, and analyze large datasets, 32% of organizations use Elasticsearch.
ScaleOut Software
JULY 19, 2021
Today’s streaming analytics architectures are not equipped to make sense of this rapidly changing information and react to it as it arrives. Incoming data is saved into data storage (historian database or log store) for query by operational managers who must attempt to find the highest priority issues that require their attention.
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