The Netflix TechBlog

DECEMBER 14, 2023

Engineers from across the company came together to share best practices on everything from Data Processing Patterns to Building Reliable Data Pipelines. The result was a series of talks which we are now sharing with the rest of the Data Engineering community! In this video, Sr. In this video, Sr.

Data Engineering 259

Data Engineering Engineering Software Engineering Best Practices 259

DZone

JANUARY 9, 2024

In the data-driven landscape of today, automation has become indispensable across industries, not just to maximize efficiency but, more importantly, to ensure quality. This holds true for the critical field of data engineering as well.

Data Engineering 130

Data Engineering Efficiency Engineering Testing 130

DZone

DECEMBER 27, 2023

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.

Best Practices 130

Best Practices Data Engineering Big Data Games 130

The Netflix TechBlog

JUNE 1, 2021

Data Engineers of Netflix?—?Interview Interview with Samuel Setegne Samuel Setegne This post is part of our “Data Engineers of Netflix” interview series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. What drew you to Netflix?

Data Engineering 130

Data Engineering Engineering Big Data Healthcare 130

The Netflix TechBlog

MARCH 5, 2019

As a micro-service owner, a Netflix engineer is responsible for its innovation as well as its operation, which includes making sure the service is reliable, secure, efficient and performant. In the Efficiency space, our data teams focus on transparency and optimization.

Infrastructure 182

Infrastructure Cloud Scalability AWS 182

Abhishek Tiwari

APRIL 14, 2018

Generally speaking, formal modeling can reduce implementation time and save money by adopting structural patterns and best-practices when implementing ETL workflows. First and foremost, modeling ETL process helps in designing an efficient, robust and evolvable ETL. Modeling of ETL workflow is important for several reasons.

Best Practices 40

Best Practices Database Design Efficiency 40

Abhishek Tiwari

JULY 12, 2018

Unfortunately, building data pipelines remains a daunting, time-consuming, and costly activity. Not everyone is operating at Netflix or Spotify scale data engineering function. Often companies underestimate the necessary effort and cost involved to build and maintain data pipelines.

Latency 63

Latency Analytics Scalability Engineering 63