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In-Stream Big Data Processing

Highly Scalable

The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. The design of the in-stream processing engine itself was driven by the following requirements: SQL-like functionality. Strict fault-tolerance is a principal requirement for the engine.

Big Data 154
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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. Key challenges.

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Auto-Diagnosis and Remediation in Netflix Data Platform

The Netflix TechBlog

By Vikram Srivastava and Marcelo Mayworm Netflix has one of the most complex data platforms in the cloud on which our data scientists and engineers run batch and streaming workloads. Pensive collects logs for the failed jobs launched by the step from the relevant data platform components and then extracts the stack traces.

Big Data 238
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What is ITOps? Why IT operations is more crucial than ever in a multicloud world

Dynatrace

Besides the traditional system hardware, storage, routers, and software, ITOps also includes virtual components of the network and cloud infrastructure. Although modern cloud systems simplify tasks, such as deploying apps and provisioning new hardware and servers, hybrid cloud and multicloud environments are often complex.

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Kubernetes in the wild report 2023

Dynatrace

Most Kubernetes clusters in the cloud (73%) are built on top of managed distributions from the hyperscalers like AWS Elastic Kubernetes Service (EKS), Azure Kubernetes Service (AKS), or Google Kubernetes Engine (GKE). Big data : To store, search, and analyze large datasets, 32% of organizations use Elasticsearch.

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What is Greenplum Database? Intro to the Big Data Database

Scalegrid

Greenplum Database is an open-source , hardware-agnostic MPP database for analytics, based on PostgreSQL and developed by Pivotal who was later acquired by VMware. Greenplum’s high performance eliminates the challenge most RDBMS have scaling to petabtye levels of data, as they are able to scale linearly to efficiently process data.

Big Data 321
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Peloton: Uber’s Unified Resource Scheduler for Diverse Cluster Workloads

Uber Engineering

Cluster management, a common software infrastructure among technology companies, aggregates compute resources from a collection of physical hosts into a shared resource pool, amplifying compute power and allowing for the flexible use of data center hardware.

Hardware 103