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Batch Processing for Data Integration

DZone

In the labyrinth of data-driven architectures, the challenge of data integration—fusing data from disparate sources into a coherent, usable form — stands as one of the cornerstones. As businesses amass data at an unprecedented pace, the question of how to integrate this data effectively comes to the fore.

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2. Diving Deeper into Psyberg: Stateless vs Stateful Data Processing

The Netflix TechBlog

By Abhinaya Shetty , Bharath Mummadisetty In the inaugural blog post of this series, we introduced you to the state of our pipelines before Psyberg and the challenges with incremental processing that led us to create the Psyberg framework within Netflix’s Membership and Finance data engineering team.

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Unlock the observability value of log data with processing at scale

Dynatrace

The data locked in your log files can be a goldmine for your application developers, operations teams, and your enterprise as a whole. However, it can be complicated , expensive , or even impossible to set up robust observability that makes use of this data. Log format inconsistency makes it a challenge to access critical data.

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Enhance data collection with Dynatrace OpenTelemetry Collector distribution

Dynatrace

As organizations strive for observability and data democratization, OpenTelemetry emerges as a key technology to create and transfer observability data. Understanding OpenTelemetry OpenTelemetry is an open, vendor-neutral standard for creating, collecting, and transferring telemetry data, like traces, metrics, and logs.

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Privacy spotlight: Control compliance in Dynatrace with multiple layers of sensitive data masking

Dynatrace

Observing complex environments involves handling regulatory, compliance, and data governance requirements. This continuously evolving landscape requires careful management and clarity regarding how sensitive data is used. This is particularly important when dealing with large volumes of data.

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1. Streamlining Membership Data Engineering at Netflix with Psyberg

The Netflix TechBlog

By Abhinaya Shetty , Bharath Mummadisetty At Netflix, our Membership and Finance Data Engineering team harnesses diverse data related to plans, pricing, membership life cycle, and revenue to fuel analytics, power various dashboards, and make data-informed decisions. We expect complete and accurate data at the end of each run.

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Data Reprocessing Pipeline in Asset Management Platform @Netflix

The Netflix TechBlog

This platform has evolved from supporting studio applications to data science applications, machine-learning applications to discover the assets metadata, and build various data facts. Hence we built the data pipeline that can be used to extract the existing assets metadata and process it specifically to each new use case.

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