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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 161
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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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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. These steps basically correspond to Map and Reduce operations.

Big Data 154
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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 197
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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 a Distributed Storage System

Scalegrid

Handling Large Volumes of Data Distributed storage systems employ the technique of data sharding or partitioning to handle immense quantities of information. By breaking up large datasets into more manageable pieces, each segment can be assigned to various network nodes for storage and management purposes.

Storage 130
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What is cloud monitoring? How to improve your full-stack visibility

Dynatrace

As cloud and big data complexity scales beyond the ability of traditional monitoring tools to handle, next-generation cloud monitoring and observability are becoming necessities for IT teams. With agent monitoring, third-party software collects data and reports from the component that’s attached to the agent.

Cloud 230