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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.

Big Data 321
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Write Optimized Spark Code for Big Data Applications

DZone

Apache Spark is a powerful open-source distributed computing framework that provides a variety of APIs to support big data processing. In addition, pySpark applications can be tuned to optimize performance and achieve better execution time, scalability, and resource utilization.

Big Data 161
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How Netflix uses eBPF flow logs at scale for network insight

The Netflix TechBlog

By Alok Tiagi , Hariharan Ananthakrishnan , Ivan Porto Carrero and Keerti Lakshminarayan Netflix has developed a network observability sidecar called Flow Exporter that uses eBPF tracepoints to capture TCP flows at near real time. Without having network visibility, it’s difficult to improve our reliability, security and capacity posture.

Network 325
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Hyper Scale VPC Flow Logs enrichment to provide Network Insight

The Netflix TechBlog

Without having network visibility, it’s not possible to improve our reliability, security and capacity posture. Network Availability: The expected continued growth of our ecosystem makes it difficult to understand our network bottlenecks and potential limits we may be reaching. 43416 5001 52.213.180.42

Network 150
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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 engine should be able to ingest both streaming data and data from Hadoop i.e. serve as a custom query engine atop of HDFS. High performance and mobility.

Big Data 154
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What is IT operations analytics? Extract more data insights from more sources

Dynatrace

This operational data could be gathered from live running infrastructures using software agents, hypervisors, or network logs, for example. ITOA collects operational data to identify patterns and anomalies for faster incident management and near-real-time insights. Clean data and optimize quality. Define core metrics.

Analytics 193
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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. Performance.