Remove Big Data Remove Data Engineering Remove Processing Remove Strategy
article thumbnail

Incremental Processing using Netflix Maestro and Apache Iceberg

The Netflix TechBlog

by Jun He , Yingyi Zhang , and Pawan Dixit Incremental processing is an approach to process new or changed data in workflows. The key advantage is that it only incrementally processes data that are newly added or updated to a dataset, instead of re-processing the complete dataset.

article thumbnail

Offline Data Pipeline Best Practices Part 1:Optimizing Airflow Job Parameters for Apache Hive

DZone

Welcome to the first post in our exciting series on mastering offline data pipeline's best practices, focusing on the potent combination of Apache Airflow and data processing engines like Hive and Spark. Working together, they form the backbone of many modern data engineering solutions.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

What is IT automation?

Dynatrace

And what are the best strategies to reduce manual labor so your team can focus on more mission-critical issues? At its most basic, automating IT processes works by executing scripts or procedures either on a schedule or in response to particular events, such as checking a file into a code repository. So, what is IT automation?

article thumbnail

Formulating ‘Out of Memory Kill’ Prediction on the Netflix App as a Machine Learning Problem

The Netflix TechBlog

We at Netflix, as a streaming service running on millions of devices, have a tremendous amount of data about device capabilities/characteristics and runtime data in our big data platform. With large data, comes the opportunity to leverage the data for predictive and classification based analysis.

Big Data 179
article thumbnail

Optimizing data warehouse storage

The Netflix TechBlog

There are several benefits of such optimizations like saving on storage, faster query time, cheaper downstream processing, and an increase in developer productivity by removing additional ETLs written only for query performance improvement. Some of the optimizations are prerequisites for a high-performance data warehouse.

Storage 203
article thumbnail

A Day in the Life of an Experimentation and Causal Inference Scientist @ Netflix

The Netflix TechBlog

At Netflix, our data scientists span many areas of technical specialization, including experimentation, causal inference, machine learning, NLP, modeling, and optimization. Together with data analytics and data engineering, we comprise the larger, centralized Data Science and Engineering group.

Analytics 207
article thumbnail

Expanding the Cloud: Introducing Amazon QuickSight

All Things Distributed

In such a data intensive environment, making key business decisions such as running marketing and sales campaigns, logistic planning, financial analysis and ad targeting require deriving insights from these data. However, the data infrastructure to collect, store and process data is geared toward developers (e.g.,

Cloud 137