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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. As a result, the input data typically goes from the data source to the in-stream pipeline via a persistent buffer that allows clients to move their reading pointers back and forth.

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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. Performance.

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What is container orchestration?

Dynatrace

This orchestration includes provisioning, scheduling, networking, ensuring availability, and monitoring container lifecycles. Part of its popularity owes to its availability as a managed service through the major cloud providers, such as Amazon Elastic Kubernetes Service , Google Kubernetes Engine , and Microsoft Azure Kubernetes Service.

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Kubernetes in the wild report 2023

Dynatrace

Through effortless provisioning, a larger number of small hosts provide a cost-effective and scalable platform. On-premises data centers invest in higher capacity servers since they provide more flexibility in the long run, while the procurement price of hardware is only one of many cost factors.

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How Netflix uses eBPF flow logs at scale for network insight

The Netflix TechBlog

Network Availability: The expected continued growth of our ecosystem makes it difficult to understand our network bottlenecks and potential limits we may be reaching. The data is also used by security and other partner teams for insight and incident analysis.

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Migrating Critical Traffic At Scale with No Downtime?—?Part 1

The Netflix TechBlog

The first phase involves validating functional correctness, scalability, and performance concerns and ensuring the new systems’ resilience before the migration. It provides a good read on the availability and latency ranges under different production conditions.

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What is Greenplum Database? Intro to the Big Data Database

Scalegrid

This feature-packed database provides powerful and rapid analytics on data that scales up to petabyte volumes. Greenplum uses an MPP database design that can help you develop a scalable, high performance deployment. High performance, query optimization, open source and polymorphic data storage are the major Greenplum advantages.

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