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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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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 248
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What is a data lakehouse? Combining data lakes and warehouses for the best of both worlds

Dynatrace

Data lakehouses deliver the query response with minimal latency. While data lakehouses combine the flexibility and cost-efficiency of data lakes with the querying capabilities of data warehouses, it’s important to understand how these storage environments differ. Data warehouses.

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

Dynatrace

ITOps refers to the process of acquiring, designing, deploying, configuring, and maintaining equipment and services that support an organization’s desired business outcomes. This includes response time, accuracy, speed, throughput, uptime, CPU utilization, and latency. Performance. What does IT operations do? ITOps vs. AIOps.

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Optimizing data warehouse storage

The Netflix TechBlog

We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits. This article will list some of the use cases of AutoOptimize, discuss the design principles that help enhance efficiency, and present the high-level architecture.

Storage 208
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Incremental Processing using Netflix Maestro and Apache Iceberg

The Netflix TechBlog

Whether in analyzing A/B tests, optimizing studio production, training algorithms, investing in content acquisition, detecting security breaches, or optimizing payments, well structured and accurate data is foundational. Backfill: Backfilling datasets is a common operation in big data processing.

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What is a Distributed Storage System

Scalegrid

Their design emphasizes increasing availability by spreading out files among different nodes or servers — this approach significantly reduces risks associated with losing or corrupting data due to node failure. By implementing data replication strategies, distributed storage systems achieve greater.

Storage 130