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What is Greenplum Database? Intro to the Big Data Database

Scalegrid

It can scale towards a multi-petabyte level data workload without a single issue, and it allows access to a cluster of powerful servers that will work together within a single SQL interface where you can view all of the data. This feature-packed database provides powerful and rapid analytics on data that scales up to petabyte volumes.

Big Data 321
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Uber’s Big Data Platform: 100+ Petabytes with Minute Latency

Uber Engineering

To accomplish this, Uber relies heavily on making data-driven decisions at every level, from forecasting rider demand during high traffic events to identifying and addressing bottlenecks … The post Uber’s Big Data Platform: 100+ Petabytes with Minute Latency appeared first on Uber Engineering Blog.

Big Data 109
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Databook: Turning Big Data into Knowledge with Metadata at Uber

Uber Engineering

Data powers Uber’s global marketplace, enabling more reliable and seamless user experiences across our products for riders, … The post Databook: Turning Big Data into Knowledge with Metadata at Uber appeared first on Uber Engineering Blog.

Big Data 110
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Auto-Diagnosis and Remediation in Netflix Data Platform

The Netflix TechBlog

By Vikram Srivastava and Marcelo Mayworm Netflix has one of the most complex data platforms in the cloud on which our data scientists and engineers run batch and streaming workloads. Pensive collects logs for the failed jobs launched by the step from the relevant data platform components and then extracts the stack traces.

Big Data 238
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Evolving from Rule-based Classifier: Machine Learning Powered Auto Remediation in Netflix Data…

The Netflix TechBlog

Operational automation–including but not limited to, auto diagnosis, auto remediation, auto configuration, auto tuning, auto scaling, auto debugging, and auto testing–is key to the success of modern data platforms. Rule Execution Engine is responsible for matching the collected logs against a set of predefined rules.

Tuning 210
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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. Grail addresses today’s challenges of big data and cloud everywhere: Grail is highly scalable, cost-effective, and super-fast.

Analytics 240
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Incremental Processing using Netflix Maestro and Apache Iceberg

The Netflix TechBlog

It also improves the engineering productivity by simplifying the existing pipelines and unlocking the new patterns. In this blog post, we talk about the landscape and the challenges in workflows at Netflix. Backfill: Backfilling datasets is a common operation in big data processing. data arrives too late to be useful).