DZone

NOVEMBER 4, 2024

In an era characterized by an exponential increase in data generation, organizations must effectively leverage this wealth of information to maintain their competitive edge. As data engineers, we are tasked with implementing these sophisticated solutions, ensuring organizations can derive actionable insights from vast datasets.

Retail 130

Retail Performance Best Practices Tuning 130

The Netflix TechBlog

FEBRUARY 14, 2025

This dual-path approach leverages Kafkas capability for low-latency streaming and Icebergs efficient management of large-scale, immutable datasets, ensuring both real-time responsiveness and comprehensive historical data availability. million impression events globally every second, with each event approximately 1.2KB in size.

Tuning 165

Tuning Latency Efficiency Storage 165

Uber Engineering

AUGUST 14, 2019

Maintaining Uber’s large-scale data warehouse comes with an operational cost in terms of ETL functions and storage. In our experience, optimizing for operational efficiency requires answering one key question: for which tables does the maintenance cost supersede utility?

Efficiency 110

Efficiency Engineering Design Storage 110

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

Dynatrace

JULY 6, 2022

Ultimately, IT automation can deliver consistency, efficiency, and better business outcomes for modern enterprises. Automating IT practices offers enterprises faster data centers and cloud operations, as well as increased flexibility and accuracy. IT automation tools can achieve enterprise-wide efficiency. Read eBook now!

Artificial Intelligence 246

Artificial Intelligence Tuning Strategy Big Data 246

The Netflix TechBlog

MARCH 25, 2019

Building and Scaling Data Lineage at Netflix to Improve Data Infrastructure Reliability, and Efficiency By: Di Lin , Girish Lingappa , Jitender Aswani Imagine yourself in the role of a data-inspired decision maker staring at a metric on a dashboard about to make a critical business decision but pausing to ask a question?—?“Can

Infrastructure 222

Infrastructure Big Data Transportation Architecture 222

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 184

Infrastructure Cloud Scalability AWS 184

The Netflix TechBlog

DECEMBER 21, 2020

We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits. This article will list some of the use cases of AutoOptimize, discuss the design principles that help enhance efficiency, and present the high-level architecture.

Storage 208

Storage Latency Efficiency Data Engineering 208

The Netflix TechBlog

NOVEMBER 14, 2023

By Abhinaya Shetty , Bharath Mummadisetty In the inaugural blog post of this series, we introduced you to the state of our pipelines before Psyberg and the challenges with incremental processing that led us to create the Psyberg framework within Netflix’s Membership and Finance data engineering team.

Processing 230

Processing Data Engineering Efficiency Analytics 230

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 4, 2024

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. the retry success probability) and compute cost efficiency (i.e., Multi-objective optimizations.

Tuning 213

Tuning Efficiency Big Data Engineering 213

The Netflix TechBlog

NOVEMBER 14, 2023

By focusing solely on updates and avoiding reprocessing of data based on a fixed lookback window, both Stateless and Stateful Data Processing maintain a minimal change footprint. This approach ensures data processing is both efficient and accurate.

Processing 248

Processing Tuning C++ Efficiency 248

The Netflix TechBlog

NOVEMBER 20, 2023

It also improves the engineering productivity by simplifying the existing pipelines and unlocking the new patterns. We will show how we are building a clean and efficient incremental processing solution (IPS) by using Netflix Maestro and Apache Iceberg. Users configure the workflow to read the data in a window (e.g.

Processing 214

Processing Big Data Efficiency Engineering 214

VoltDB

DECEMBER 11, 2024

Real-Time Data Processing Bottlenecks Edge computing is lauded for enabling real-time data processing, but scaling such systems without delays remains a hurdle. As data streams grow in complexity, processing efficiency can decline. Inconsistent network performance affecting data synchronization.

IoT 52

IoT Energy Logistics Latency 52

The Netflix TechBlog

SEPTEMBER 10, 2024

With these clear benefits, we continued to build out this functionality for more devices, enabling the same efficiency wins. It was very efficient, but it had a set job size, requiring manual intervention if we wanted to horizontally scale it, and it required manual intervention when rolling out a new version.

Latency 230

Latency Cache Tuning Efficiency 230

The Netflix TechBlog

NOVEMBER 22, 2019

This talk explores the journey, learnings, and improvements to performance analysis, efficiency, reliability, and security. Our data scientists are expected to develop and operate large machine learning workflows autonomously without the need to be deeply experienced with systems or data engineering.

AWS 38

AWS Entertainment Open Source Benchmarking 38

The Netflix TechBlog

SEPTEMBER 18, 2020

[Julie] Chris and I have the same primary stakeholders (or engineering team that we support): Encoding Technologies. They are continuously innovating compression algorithms to efficiently send high quality audio and video files to our customers over the internet. Is the benefit uniform, or do certain cohorts of members?—?such

Analytics 227

Analytics Education Innovation Engineering 227

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

MARCH 7, 2024

Under the hood, Titus is powered by Kubernetes , but it provides a thick layer of enhancements over off-the-shelf Kubernetes, to make it more observable , secure , scalable , and cost-efficient. Internally, we use a production workflow orchestrator called Maestro.

Systems 231

Systems Media Cache Open Source 231

The Netflix TechBlog

OCTOBER 18, 2022

It is a general-purpose workflow orchestrator that provides a fully managed workflow-as-a-service (WAAS) to the data platform at Netflix. It serves thousands of users, including data scientists, data engineers, machine learning engineers, software engineers, content producers, and business analysts, for various use cases.

Java 207

Java Scalability Traffic Architecture 207

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

High Scalability

MAY 12, 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 102

Education Software Engineering Scalability Engineering 102

High Scalability

APRIL 28, 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 102

Education Software Engineering Scalability Engineering 102

High Scalability

MARCH 30, 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

MAY 26, 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 100

Education AWS Software Engineering Scalability 100

The Netflix TechBlog

NOVEMBER 22, 2019

3:15pm-4:15pm OPN 209 Netflix’s application deployment at scale Andy Glover , Director Delivery Engineering & Paul Roberts, AWS Abstract : Spinnaker is an open-source continuous-delivery platform created by Netflix to improve its developers’ efficiency and reduce the time it takes to get an application into production.

AWS 100

AWS Entertainment Open Source Benchmarking 100

The Netflix TechBlog

NOVEMBER 22, 2019

3:15pm-4:15pm OPN 209 Netflix’s application deployment at scale Andy Glover , Director Delivery Engineering & Paul Roberts, AWS Abstract : Spinnaker is an open-source continuous-delivery platform created by Netflix to improve its developers’ efficiency and reduce the time it takes to get an application into production.

AWS 100

AWS Entertainment Open Source Benchmarking 100

Abhishek Tiwari

JULY 7, 2023

Airflow provides rich scheduling and execution semantics enabling data engineers to easily define complex pipelines, running at regular intervals. Workflow platforms have emerged as a crucial component for these companies, enabling them to orchestrate complex tasks, automate processes, and ensure efficient collaboration.

Logistics 52

Logistics Transportation Scalability Data Engineering 52

Abhishek Tiwari

DECEMBER 27, 2017

A unified data management (UDM) system combines the best of data warehouses, data lakes, and streaming without expensive and error-prone ETL. It offers reliability and performance of a data warehouse, real-time and low-latency characteristics of a streaming system, and scale and cost-efficiency of a data lake.

Big Data 49

Big Data Artificial Intelligence Storage Hardware 49

InfoQ

APRIL 7, 2021

According to the company, the A2 VMs will allow customers to run their NVIDIA CUDA-enabled machine learning (ML) and high-performance computing (HPC) scale-out and scale-up workloads efficiently at a lower cost. By Steef-Jan Wiggers.

Virtualization 57

Virtualization Google Availability Engineering 57

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

Abhishek Tiwari

APRIL 14, 2018

First and foremost, modeling ETL process helps in designing an efficient, robust and evolvable ETL. Second, modeling ETL workflow plays an important role to optimize the data warehousing. Modeling of ETL workflow is important for several reasons.

Best Practices 40

Best Practices Database Design Efficiency 40