The Netflix TechBlog

NOVEMBER 20, 2023

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.

Processing 214

Processing Big Data Efficiency Engineering 214

DZone

JULY 3, 2023

Data engineering projects often require the setup and management of complex infrastructures that support data processing, storage, and analysis. Traditionally, this process involved manual configuration, leading to potential inconsistencies, human errors, and time-consuming deployments.

Data Engineering 150

Data Engineering Infrastructure Engineering Code 150

The Netflix TechBlog

JUNE 1, 2021

Data Engineers of Netflix?—?Interview Interview with Dhevi Rajendran Dhevi Rajendran 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.

Data Engineering 138

Data Engineering Engineering Software Engineering Big Data 138

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

High Scalability

JUNE 15, 2020

This is a guest post by Eunice Do , Data Engineer at TripleLift , a technology company leading the next generation of programmatic advertising. The system is the data pipeline at TripleLift. TripleLift is an adtech company, and like most companies in this industry, we deal with high volumes of data on a daily basis.

Processing 206

Processing Data Engineering Engineering Efficiency 206

DZone

FEBRUARY 8, 2022

While increasing both the precision and the recall of our secrets detection engine, we felt the need to keep a close eye on speed. So it wasn’t a surprise to find that our engine had the same problem: more power, less speed. In a gearbox, if you want to increase torque, you need to decrease speed.

Processing 243

Processing Benchmarking Speed Engineering 243

DZone

JANUARY 9, 2024

This holds true for the critical field of data engineering as well. As organizations gather and process astronomical volumes of data, manual testing is no longer feasible or reliable. This comprehensive guide takes an in-depth look at automated testing in the data engineering domain.

Data Engineering 140

Data Engineering Efficiency Engineering Testing 140

DZone

MARCH 29, 2023

Data migration is the process of moving data from one location to another, which is an essential aspect of cloud migration. Data migration involves transferring data from on-premise storage to the cloud. With the rapid adoption of cloud computing , businesses are moving their IT infrastructure to the cloud.

Best Practices 315

Best Practices Cloud Data Engineering Storage 315

The Netflix TechBlog

FEBRUARY 14, 2025

It requires a state-of-the-art system that can track and process these impressions while maintaining a detailed history of each profiles exposure. This nuanced integration of data and technology empowers us to offer bespoke content recommendations. This queue ensures we are consistently capturing raw events from our global userbase.

Tuning 165

Tuning Latency Efficiency Storage 165

The Netflix TechBlog

DECEMBER 3, 2022

Obviously not all tools are made with the same use case in mind, so we are planning to add more code samples for other (than classical batch ETL) data processing purposes, e.g. Machine Learning model building and scoring. This allows other processes, consuming our table, to be notified and start their processing.

Best Practices 360

Best Practices Code Testing Data Engineering 360

Dynatrace

JULY 6, 2022

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. Adding AIOps to automation processes makes the volume of data that applications and multicloud environments generate much less overwhelming.

Artificial Intelligence 246

Artificial Intelligence Tuning Strategy Big Data 246

The Netflix TechBlog

NOVEMBER 14, 2023

In the previous installments of this series, we introduced Psyberg and delved into its core operational modes: Stateless and Stateful Data Processing. Pipelines After Psyberg Let’s explore how different modes of Psyberg could help with a multistep data pipeline. In this case, the minimum hour to process the data is hour 2.

Processing 248

Processing Tuning C++ Efficiency 248

The Netflix TechBlog

DECEMBER 21, 2020

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 208

Storage Latency Efficiency Data Engineering 208

The Netflix TechBlog

SEPTEMBER 18, 2020

The Engineer enjoys making data available by piping it in from new sources in optimal ways, building robust data models, prototyping systems, and doing project-specific engineering.

Analytics 242

Analytics Engineering Data Engineering Tuning 242

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 140

Best Practices Data Engineering Big Data Games 140

The Netflix TechBlog

MARCH 25, 2019

We adopted the following mission statement to guide our investments: “Provide a complete and accurate data lineage system enabling decision-makers to win moments of truth.” We are loading the lineage data to a graph database to enable seamless integration with a REST data lineage service to address business use cases.

Infrastructure 222

Infrastructure Big Data Transportation Architecture 222

InfoQ

NOVEMBER 29, 2023

HubSpot adopted routing messages over multiple Kafka topics (called swimlanes) for the same producer to avoid the build-up in the consumer group lag and prioritize the processing of real-time traffic.

Processing 84

Processing Traffic Data Engineering Scalability 84

DZone

AUGUST 16, 2023

From a data engineer's point of view, financial risk management is a series of data analysis activities on financial data. The financial sector imposes its unique requirements on data engineering. Before they adopted an OLAP engine, they were using Kettle to collect data.

FinTech 130

FinTech Engineering Data Engineering Latency 130

The Netflix TechBlog

MARCH 5, 2019

This operational component places some cognitive load on our engineers, requiring them to develop deep understanding of telemetry and alerting systems, capacity provisioning process, security and reliability best practices, and a vast amount of informal knowledge about the cloud infrastructure.

Infrastructure 184

Infrastructure Cloud Scalability AWS 184

DZone

JANUARY 31, 2024

SIEM platforms streamline incident response processes, allowing security teams to respond quickly and effectively to security incidents. Problem Statement In Data Engineering , the data/log collection is a challenging task for high-volume sources.

Data Engineering 145

Data Engineering Storage Network Infrastructure 145

VoltDB

DECEMBER 11, 2024

Edge computing has transformed how businesses and industries process and manage data. By bringing computation closer to the data source, edge-based deployments reduce latency, enhance real-time capabilities, and optimize network bandwidth. Leverage tiered storage systems that dynamically offload data based on priority.

IoT 52

IoT Energy Logistics Latency 52

The Netflix TechBlog

FEBRUARY 9, 2022

Let’s define some requirements that we are interested in delivering to the Netflix data engineers or anyone who would like to schedule a workflow with some external assets in it. By the end of the migration process our Jenkins configuration went from: Figure 4. The slightly improved approach is shown on the diagram below.

Storage 206

Storage Data Engineering Testing Code 206

O'Reilly

FEBRUARY 18, 2020

Usage specific to Python as a programming language grew by just 4% in 2019; by contrast, usage that had to do with Python and ML—be it in the context of AI, deep learning, and natural language processing, or in combination with any of several popular ML/AI frameworks—grew by 9%. In aggregate, data engineering usage declined 8% in 2019.

Software Architecture 142

Software Architecture DevOps Data Engineering Architecture 142

DZone

DECEMBER 21, 2020

Still, it is one of many that need to be taken before you can generate value from the data you gather. An important step in that chain of the process is data modeling and transformation. This is where data is extracted, transformed, and loaded (ETL) or extracted, loaded, and transformed (ELT).

Big Data 196

Big Data Google Scalability Processing 196

The Netflix TechBlog

MARCH 7, 2024

Data: Fast Data Our main data lake is hosted on S3, organized as Apache Iceberg tables. For ETL and other heavy lifting of data, we mainly rely on Apache Spark. In addition to Spark, we want to support last-mile data processing in Python, addressing use cases such as feature transformations, batch inference, and training.

Systems 231

Systems Media Cache Open Source 231

The Netflix TechBlog

JULY 21, 2022

Since memory management is not something one usually associates with classification problems, this blog focuses on formulating the problem as an ML problem and the data engineering that goes along with it. We now explore each of these components individually, while highlighting the nuances of the data pipeline and pre-processing.

Big Data 184

Big Data Cache Engineering Data Engineering 184

The Netflix TechBlog

MARCH 2, 2021

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 212

Analytics C++ Innovation Engineering 212

The Netflix TechBlog

SEPTEMBER 10, 2024

The voice service then constructs a message for the device and places it on the message queue, which is then processed and sent to Pushy to deliver to the device. The previous version of the message processor was a Mantis stream-processing job that processed messages from the message queue.

Latency 230

Latency Cache Tuning Efficiency 230

Simform

FEBRUARY 14, 2023

Stream processing has become a core part of enterprise data architecture today due to the explosive growth of data from sources such as IoT sensors, security logs, and web applications. This blog discusses the topic of stream processing in detail to help you navigate its landscape with ease.

Processing 52

Processing IoT Architecture Data Engineering 52

The Netflix TechBlog

OCTOBER 18, 2022

In the data domain, it is common to have a super large number of jobs within a single workflow. For example, a workflow to backfill hourly data for the past five years can lead to 43800 jobs (24 * 365 * 5), each of which processes data for an hour. All the requests are processed via distributed queues for message passing.

Java 207

Java Scalability Traffic Architecture 207

The Netflix TechBlog

NOVEMBER 22, 2019

This entertaining romp through the tech stack serves as an introduction to how we think about and design systems, the Netflix approach to operational challenges, and how other organizations can apply our thought processes and technologies.

AWS 38

AWS Entertainment Open Source Benchmarking 38

High Scalability

MARCH 3, 2020

Etleap is analyst-friendly , enterprise-grade ETL-as-a-service , built for Redshift and Snowflake data warehouses and S3/Glue data lakes. Our intuitive software allows data engineers to maintain pipelines without writing code, and lets analysts gain access to data in minutes instead of months.

Education 114

Education Engineering Games Java 114

High Scalability

FEBRUARY 18, 2020

Etleap is analyst-friendly , enterprise-grade ETL-as-a-service , built for Redshift and Snowflake data warehouses and S3/Glue data lakes. Our intuitive software allows data engineers to maintain pipelines without writing code, and lets analysts gain access to data in minutes instead of months.

Education 113

Education Engineering Games Java 113

High Scalability

MARCH 17, 2020

Etleap is analyst-friendly , enterprise-grade ETL-as-a-service , built for Redshift and Snowflake data warehouses and S3/Glue data lakes. Our intuitive software allows data engineers to maintain pipelines without writing code, and lets analysts gain access to data in minutes instead of months.

Education 105

Education Engineering Java Servers 105

High Scalability

FEBRUARY 9, 2020

Etleap is analyst-friendly , enterprise-grade ETL-as-a-service , built for Redshift and Snowflake data warehouses and S3/Glue data lakes. Our intuitive software allows data engineers to maintain pipelines without writing code, and lets analysts gain access to data in minutes instead of months.

Education 108

Education Engineering Games Java 108

High Scalability

MARCH 24, 2020

Etleap is analyst-friendly , enterprise-grade ETL-as-a-service , built for Redshift and Snowflake data warehouses and S3/Glue data lakes. Our intuitive software allows data engineers to maintain pipelines without writing code, and lets analysts gain access to data in minutes instead of months.

Education 105

Education Software Engineering Engineering Big Data 105

High Scalability

APRIL 14, 2020

Etleap is analyst-friendly , enterprise-grade ETL-as-a-service , built for Redshift and Snowflake data warehouses and S3/Glue data lakes. Our intuitive software allows data engineers to maintain pipelines without writing code, and lets analysts gain access to data in minutes instead of months.

Education 105

Education Software Engineering Scalability Engineering 105