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What is Greenplum Database? Intro to the Big Data Database

Scalegrid

It’s architecture was specially designed to manage large-scale data warehouses and business intelligence workloads by giving you the ability to spread your data out across a multitude of servers. This feature-packed database provides powerful and rapid analytics on data that scales up to petabyte volumes.

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. This system has been designed to supplement and succeed the existing Hadoop-based system that had too high latency of data processing and too high maintenance costs.

Big Data 154
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Data Engineers of Netflix?—?Interview with Pallavi Phadnis

The Netflix TechBlog

Netflix’s unique work culture and petabyte-scale data problems are what drew me to Netflix. During earlier years of my career, I primarily worked as a backend software engineer, designing and building the backend systems that enable big data analytics. What is your favorite project?

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Path to NoOps part 1: How modern AIOps brings NoOps within reach

Dynatrace

NoOps is an advanced transformation of DevOps where many of the functions needed to manage, optimize and secure IT services and applications are automated within the design. Traditional AIOps uses machine-learning models to deliver data to dashboards using aggregation and correlation.

DevOps 206
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Seven benefits of AIOps to transform your business operations

Dynatrace

AIOps combines big data and machine learning to automate key IT operations processes, including anomaly detection and identification, event correlation, and root-cause analysis. Improved time management and event prioritization. What is AIOps, and how does it work? Seven benefits of AIOps for operational transformation.

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Incremental Processing using Netflix Maestro and Apache Iceberg

The Netflix TechBlog

For example, a job would reprocess aggregates for the past 3 days because it assumes that there would be late arriving data, but data prior to 3 days isn’t worth the cost of reprocessing. Backfill: Backfilling datasets is a common operation in big data processing. ETL pipelines keep all the benefits of batch workflows.

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Building and Scaling Data Lineage at Netflix to Improve Data Infrastructure Reliability, and…

The Netflix TechBlog

Finally, imagine yourself in the role of a data platform reliability engineer tasked with providing advanced lead time to data pipeline (ETL) owners by proactively identifying issues upstream to their ETL jobs. Design a flexible data model ? —?Represent Enable seamless integration?—? push or pull.