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 Software Engineering 105

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 Games Engineering 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 Games Engineering Java 113

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

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 Games Engineering Java 108

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

The Netflix TechBlog

NOVEMBER 22, 2019

1pm-2pm NFX 207 Benchmarking stateful services in the cloud Vinay Chella , Data Platform Engineering Manager Abstract : AWS cloud services make it possible to achieve millions of operations per second in a scalable fashion across multiple regions. We explore all the systems necessary to make and stream content from Netflix.

AWS 100

AWS Entertainment Open Source Benchmarking 100

The Netflix TechBlog

NOVEMBER 22, 2019

1pm-2pm NFX 207 Benchmarking stateful services in the cloud Vinay Chella , Data Platform Engineering Manager Abstract : AWS cloud services make it possible to achieve millions of operations per second in a scalable fashion across multiple regions. We explore all the systems necessary to make and stream content from Netflix.

AWS 100

AWS Entertainment Open Source Benchmarking 100

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

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 55

Virtualization Google Availability Engineering 55

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 210

Processing Big Data Efficiency Engineering 210

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 37

AWS Entertainment Open Source Benchmarking 37

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 203

Storage Latency Efficiency Data Engineering 203

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 219

Infrastructure Big Data Transportation Architecture 219

Abhishek Tiwari

JULY 7, 2023

The DAG Model and the Misconception Data pipelines are commonly implemented as Directed Acyclic Graphs (DAGs), where data flows through a series of processing steps, with each step represented as a node and the dependencies between steps represented as edges. Let's take Uber as an example.

Logistics 52

Logistics Transportation Scalability Data Engineering 52

The Netflix TechBlog

OCTOBER 18, 2022

As Big data and ML became more prevalent and impactful, the scalability, reliability, and usability of the orchestrating ecosystem have increasingly become more important for our data scientists and the company. Another dimension of scalability to consider is the size of the workflow. But sometimes, it is not efficient.

Java 202

Java Scalability Traffic Architecture 202