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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. Towards Unified Big Data Processing. Moreover, techniques like Lambda Architecture [6, 7] were developed and adopted to combine these solutions efficiently.

Big Data 154
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Auto-Diagnosis and Remediation in Netflix Data Platform

The Netflix TechBlog

The data platform is built on top of several distributed systems, and due to the inherent nature of these systems, it is inevitable that these workloads run into failures periodically. This blog will explore these two systems and how they perform auto-diagnosis and remediation across our Big Data Platform and Real-time infrastructure.

Big Data 242
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Migrating Critical Traffic At Scale with No Downtime?—?Part 1

The Netflix TechBlog

When undertaking system migrations, one of the main challenges is establishing confidence and seamlessly transitioning the traffic to the upgraded architecture without adversely impacting the customer experience. Provides a platform to ensure that relevant operational insights , metrics, logging, and alerting are in place before migration.

Traffic 347
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Any analysis, any time: Dynatrace Log Management and Analytics powered by Grail

Dynatrace

Limited data availability constrains value creation. Modern IT environments — whether multicloud, on-premises, or hybrid-cloud architectures — generate exponentially increasing data volumes. Even in cases where all data is available, new challenges can arise. Effective analytics with the Dynatrace Query Language.

Analytics 264
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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. These next-generation cloud monitoring tools present reports — including metrics, performance, and incident detection — visually via dashboards.

Cloud 295
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Data lakehouse innovations advance the three pillars of observability for more collaborative analytics

Dynatrace

How do you get more value from petabytes of exponentially exploding, increasingly heterogeneous data? The short answer: The three pillars of observability—logs, metrics, and traces—converging on a data lakehouse. To solve this problem, Dynatrace launched Grail, its causational data lakehouse , in 2022.

Analytics 246
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What is IT operations analytics? Extract more data insights from more sources

Dynatrace

Then, big data analytics technologies, such as Hadoop, NoSQL, Spark, or Grail, the Dynatrace data lakehouse technology, interpret this information. Here are the six steps of a typical ITOA process : Define the data infrastructure strategy. Choose a repository to collect data and define where to store data.

Analytics 246