Netflix Tech

NOVEMBER 20, 2023

by Jun He , Yingyi Zhang , and Pawan Dixit Incremental processing is an approach to process new or changed data in workflows. In this blog post, we talk about the landscape and the challenges in workflows at Netflix. IPS enables users to continue to use the data processing patterns with minimal changes.

Windows 84

Windows Software Review Data Engineering 84

Hacker Earth Developers Blog

MAY 22, 2020

So I spent an initial two years in the customer success part of the organization, building internal-facing Data Science and Machine Learning products to help drive our revenue, minimize attrition problems in that space. So when I think of algorithms making mistakes, it could either stem from mistakes in ETL pipelines. Alfred: Got it.

Machine Learning 130

Machine Learning Artificial Inteligence Software Engineering Engineering 130

The Programmer's Paradox

DECEMBER 30, 2017

Once people are committed, it isn’t unusual for them to turn a blind eye to the flaws; to stick to a bad path, even in the face of contradictory evidence. Thus, we can predict the impact of any change. It can’t be changed. For data that doesn’t change often, it is fine. There are good reasons for this.

Programming 40

Programming Construction Resources Performance 40

Netflix Tech

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. Orient: Gather tuning parameters for a particular table that changed.

Storage 81

Storage Data Resources Data Engineering 81

Confluent

MAY 15, 2019

The customer had traditional ETL tools on the table; we were in fact already providing them services around Oracle Data Integrator (ODI). They asked us to evaluate whether we thought an ETL tool was the appropriate choice to solve these two requirements. All disparate patterns are abstracted to either relational tables or REST APIs.

Analytics 91

Analytics Cloud Data System 91

Mobilunity

APRIL 7, 2023

Let’s take a look at the most popular ones: To increase adaptivity Data is crucial to a company’s ability to adapt to change and thrive in the face of adversity. As changes happen, you need to be aware of them immediately and acquire insight into the best way to react. Data migration is necessary for different reasons.

Data 52

Data Weak Development Team Technical Review Software Review 52

Netflix Tech

MARCH 25, 2019

Now, imagine yourself in the role of a software engineer responsible for a micro-service which publishes data consumed by few critical customer facing services (e.g. You are about to make structural changes to the data and want to know who and what downstream to your service will be impacted.

Infrastructure 87

Infrastructure Data Technical Review Systems Review 87

OpenCredo

JANUARY 26, 2023

Data models can be hard to evolve or change and migrate once terabytes of data are loaded, so it is important to do just enough at the start. Then, we need to perform some transformations such as changing data formats, adding any default values and filtering out some unnecessary fields, which will be the transformation stage.

Big Data 52

Big Data Data Software Engineering Data Engineering 52

Altexsoft

OCTOBER 21, 2022

In this article, we’ll explain why businesses choose Kafka and what problems they face when using it. Keep in mind that those vendors mitigate most challenges businesses commonly face when implementing the “raw” Kafka from the official website. Kafka vs ETL. It’s quite common to see Kafka as a faster ETL. What is Kafka?

Weak Development Team 93

Weak Development Team Technical Review Systems Review Software Review 93

Cloudera

SEPTEMBER 17, 2020

For modern data engineers using Apache Spark, DE offers an all-inclusive toolset that enables data pipeline orchestration, automation, advanced monitoring, visual troubleshooting, and a comprehensive management toolset for streamlining ETL processes and making complex data actionable across your analytic teams. Managed, Serverless Spark.

Data Engineering 104

Data Engineering Engineering Data Tools 104

TIBCO - Connected Intelligence

MAY 4, 2020

Bulk/batch (ETL, ELT). ? It provides a pragmatic integration approach that organizations are increasingly adopting to use along with their favorite ETL/ELT and replication tools. But let’s face it, most people try to get by with as few tools as possible. Replication. ? Messaging. ? Data services orchestration. ?

Virtualization 48

Virtualization Data Data Engineering Cloud 48

Perficient

APRIL 1, 2024

Before the introduction of Fabric, Power BI faced few limitations related to data ingestion, since Power Query offers limited ETL & data transformation functionality. Terminologies Used Below are few terms used in the blog. Blog intends to share few capabilities of Fabric for dealing real scenario.

Data 64

Data Azure Case Study Data Engineering 64

Altexsoft

NOVEMBER 7, 2022

The approach finds its application, for example, in ETL and ELT pipelines with many data sources or destinations. All these make Airflow capable of ensuring high performance and scaling to meet changing needs of your business. If you are interested in web development, take a look at our blog post on. No versioning of workflows.

Weak Development Team 52

Weak Development Team Technical Review Software Review Systems Review 52

OpenCredo

DECEMBER 6, 2022

This blog post will go through the challenges companies face in the modern data stack and the features that the newest tools offer to tackle those challenges. Some small ETL (Extract, transform, load) jobs to copy data into your data lake, and you run a small set of transformations and aggregations to generate a report.

Data 52

Data Microservices Architecture Infrastructure 52

Datavail

JUNE 24, 2021

Rather than adding servers on-site when you need more resources, you can quickly scale up capacity based on changing requirements. For example, you can use an Extract, Transform, Load (ETL) tool for getting data into a cloud-based data warehouse or data lake, then connecting that to a data visualization and dashboard creation tool.

Analytics 52

Analytics Cloud Business Intelligence Hardware 52

Actian

DECEMBER 19, 2018

Face the inevitable: Local persistent data at the Edge will happen. This pairing guarantees that data reformatting or ETL (Extract, Transform, Load) is a necessary evil. There are three major drawbacks of ETL that we find most data architects and developers struggle with: integration cost, performance and data security.

IoT 40

IoT Linux Mobile Open Source 40

Netflix Tech

OCTOBER 18, 2022

These include ETL pipelines, ML model training workflows, batch jobs, etc. In this blog post, we introduce and share learnings on Maestro, a workflow orchestrator that can schedule and manage workflows at a massive scale. It addresses the key challenges we face with Meson and achieves operational excellence.

Data 81

Data UI/UX Systems Review Software Review 81

Netflix Tech

FEBRUARY 4, 2019

Scaling Paid Media campaigns This is the third blog of the series on Marketing Technology at Netflix. This blog focuses on the marketing tech systems that are responsible for campaign setup and delivery of our paid media campaigns. The first blog focused on solving for creative development and localization at scale.

Media 54

Media Marketing Engineering Budget 54

Instaclustr

MAY 19, 2021

If you’re in database administration, chances are you’ve faced an age-old question: is it better to become slightly proficient in lots of different database tools, or become very proficient in one tool that can do a bit of everything reasonably well? . All changes are reflected across the system in coherence with one another.

Database Administration 36

Database Administration Storage Weak Development Team Open Source 36

Instaclustr

OCTOBER 14, 2019

This blog provides a brief of things I learnt during the 4-day event, including a summary of talks I attended and presented. We have talks in the Cassandra and Observability tracks, but I’m attending (and blogging about) talks in other tracks that have interesting or challenging use cases (e.g. Vinay Chella. Apache Kafka.

Technical Review 36

Technical Review Technology Report Open Source 36

Instaclustr

OCTOBER 14, 2019

This blog provides a brief of things I learnt during the 4-day event, including a summary of talks I attended and presented. . We have talks in the Cassandra and Observability tracks, but I’m attending (and blogging about) talks in other tracks that have interesting or challenging use cases (e.g. Vinay Chella. Apache Kafka.

Technical Review 28

Technical Review Technology Report Open Source 28

Cloudera

JUNE 16, 2022

Over the last seven years, Cloudera’s Stream Processing product has evolved to meet the changing streaming analytics needs of our 700+ enterprise customers and their diverse use cases. How is Cloudera Stream Processing (CSP) staying in lock-step with the changing needs of our customers? Conclusion.

Data 89

Data Disaster Recovery Analytics Microservices 89

Coforge

DECEMBER 3, 2018

Open Source Big Data technologies like Hadoop, Data Lakes, and NoSQL have made implementation of Big Data architectures more affordable, but organizations are continuing to face challenges in passing data to these systems. other data management tools performing data quality, ETL, or Master data management functions.

Virtualization 40

Virtualization Data Big Data Analytics 40

Datavail

SEPTEMBER 8, 2022

Modern data integration and ETL (extract, transform, load) solutions come packed with dozens or even hundreds of pre-built connectors and integrations for the most popular data sources. One of Datavail’s recent clients, an education nonprofit, faced this issue when looking to improve its existing data integration processes.

Data 40

Data Nonprofit Quality Assurance Education 40