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Experiences with approximating queries in Microsoft’s production big-data clusters

The Morning Paper

Experiences with approximating queries in Microsoft’s production big-data clusters Kandula et al., I’ve been excited about the potential for approximate query processing in analytic clusters for some time, and this paper describes its use at scale in production. ICDE’16 (PowerDrill is a Google internal system).

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Mastering Hybrid Cloud Strategy

Scalegrid

Workloads from web content, big data analytics, and artificial intelligence stand out as particularly well-suited for hybrid cloud infrastructure owing to their fluctuating computational needs and scalability demands.

Strategy 130
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Software Testing Trends 2021 – What can we expect?

Testsigma

Machine Learning (ML) and Artificial Intelligence (AI) programme testing and QA teams will develop their automatic research techniques, keeping track with recurring updates — with the assistance of analytics and monitoring. million Google Play Store applications, followed by 1.96 Automation to Enhance AI Security Defence.

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Data Movement in Netflix Studio via Data Mesh

The Netflix TechBlog

Netflix is known for its loosely coupled microservice architecture and with a global studio footprint, surfacing and connecting the data from microservices into a studio data catalog in real time has become more important than ever. We will deep dive into Data Delivery and Data Consumption in the following sections.

Big Data 253
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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 data warehouse is not designed to serve point requests from microservices with low latency.

Latency 243
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5 data integration trends that will define the future of ETL in 2018

Abhishek Tiwari

A unified data management (UDM) system combines the best of data warehouses, data lakes, and streaming without expensive and error-prone ETL. It offers reliability and performance of a data warehouse, real-time and low-latency characteristics of a streaming system, and scale and cost-efficiency of a data lake.