Remove Availability Remove Big Data Remove Latency
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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. 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.

Big Data 154
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Understanding gRPC Concepts, Use Cases, and Best Practices

DZone

Because with the advent of cloud providers, we are less worried about managing data centers. Everything is available within seconds on-demand. This leads to an increase in the size of data as well. Big data is generated and transported using various mediums in single requests.

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

The Netflix TechBlog

It provides a good read on the availability and latency ranges under different production conditions. The upstream service calls the existing and new replacement services concurrently to minimize any latency increase on the production path. Logging is selective to cases where the old and new responses do not match.

Traffic 347
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No need to compromise visibility in public clouds with the new Azure services supported by Dynatrace

Dynatrace

Our customers have frequently requested support for this first new batch of services, which cover databases, big data, networks, and computing. See the health of your big data resources at a glance. Azure Virtual Network Gateways. Azure Front Door. Azure Traffic Manager. Get a comprehensive view of your batch jobs.

Azure 227
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Bulldozer: Batch Data Moving from Data Warehouse to Online Key-Value Stores

The Netflix TechBlog

Data scientists and engineers collect this data from our subscribers and videos, and implement data analytics models to discover customer behaviour with the goal of maximizing user joy. The processed data is typically stored as data warehouse tables in AWS S3.

Latency 252
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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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Data Movement in Netflix Studio via Data Mesh

The Netflix TechBlog

From the moment a Netflix film or series is pitched and long before it becomes available on Netflix, it goes through many phases. Operational Reporting is a reporting paradigm specialized in covering high-resolution, low-latency data sets, serving detailed day-to-day activities¹ and processes of a business domain.

Big Data 257