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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. Broadcast variables can be used to efficiently distribute large read-only data structures, such as lookup tables, to worker nodes. For example, to broadcast a lookup table named lookup_table :

Big Data 173
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What is Greenplum Database? Intro to the Big Data Database

Scalegrid

When handling large amounts of complex data, or big data, chances are that your main machine might start getting crushed by all of the data it has to process in order to produce your analytics results. Greenplum features a cost-based query optimizer for large-scale, big data workloads. Greenplum Advantages.

Big Data 321
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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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How Amazon is solving big-data challenges with data lakes

All Things Distributed

The team is constantly looking for ways to get more accurate data, faster. That's why, in 2019, they had an idea: Build a data lake that can support one of the largest logistics networks on the planet. It would later become known internally as the Galaxy data lake.

Big Data 209
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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. In the previous section, we noted that many distributed query processing algorithms resemble message passing networks. Towards Unified Big Data Processing. Pipelining.

Big Data 154
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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.

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

Dynatrace

IT operations analytics is the process of unifying, storing, and contextually analyzing operational data to understand the health of applications, infrastructure, and environments and streamline everyday operations. ITOA collects operational data to identify patterns and anomalies for faster incident management and near-real-time insights.

Analytics 182