Analytics at Netflix: Who we are and what we do

The Netflix TechBlog

But there is far less agreement on what that term “data analytics” actually means?—?or Even within Netflix, we have many groups that do some form of data analysis, including business strategy and consumer insights. When you think about data at Netflix, what comes to mind?

How Our Paths Brought Us to Data and Netflix

The Netflix TechBlog

and what the role entails by Julie Beckley & Chris Pham This Q&A provides insights into the diverse set of skills, projects, and culture within Data Science and Engineering (DSE) at Netflix through the eyes of two team members: Chris Pham and Julie Beckley.

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Optimizing dbt and Google’s BigQuery

DZone

Setting up a data warehouse is the first step towards fully utilizing big data analysis. 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).

Google 125

Mythbusting the Analytics Journey

The Netflix TechBlog

I wasn’t even entirely sure what the right role fit would be and originally applied for a different position, before being redirected to the Analytics Engineer role. Working in Studio Data Science & Engineering (“Studio DSE”) was basically a dream come true.

A Day in the Life of a Content Analytics Engineer

The Netflix TechBlog

I’m a Senior Analytics Engineer on the Content and Marketing Analytics Research team. Being an Analytics Engineer is like being a hybrid of a librarian ?? One of my favorite things about being an Analytics Engineer is the variety.

Hyper Scale VPC Flow Logs enrichment to provide Network Insight

The Netflix TechBlog

While we strive to keep the ecosystem simple, the inherent nature of leveraging a variety of technologies will lead us to complications and challenges such as: App Dependencies and Data Flow Mappings: Without understanding and having visibility into an application’s dependencies and data flows, it is difficult for both service owners and centralized teams to identify systemic issues. At Netflix we publish the Flow Log data to Amazon S3.

Less is More: Engineering Data Warehouse Efficiency with Minimalist Design

Uber Engineering

Maintaining Uber’s large-scale data warehouse comes with an operational cost in terms of ETL functions and storage. Once identified, … The post Less is More: Engineering Data Warehouse Efficiency with Minimalist Design appeared first on Uber Engineering Blog. Architecture Uber Data Big Data Data Engineering Data Infrastructure data science Data Warehouse Engineering Efficiency

Kubernetes for Big Data Workloads

Abhishek Tiwari

Kubernetes has emerged as go to container orchestration platform for data engineering teams. In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. Organisations are already using Kubernetes for a variety of workloads [1] [2] and data workloads are up next. Containerized data workloads running on Kubernetes offer several advantages over traditional virtual machine/bare metal based data workloads including but not limited to.

5 data integration trends that will define the future of ETL in 2018

Abhishek Tiwari

ETL refers to extract, transform, load and it is generally used for data warehousing and data integration. There are several emerging data trends that will define the future of ETL in 2018. A common theme across all these trends is to remove the complexity by simplifying data management as a whole. In 2018, we anticipate that ETL will either lose relevance or the ETL process will disintegrate and be consumed by new data architectures.

ETL Workflow Modeling

Abhishek Tiwari

Developing Extract–transform–load (ETL) workflow is a time-consuming activity yet a very important component of data warehousing process. It enables data warehouse teams to ask questions like how good is the current or proposed ETL workflow design, is the workflow resilient to occasional failures, what part of the workflow can be parallelized, are there any variants of ETL workflow, and if so is one variant is better than other. Query-based Data Warehousing Tool ??.

How machine learning is accelerating data integration?

Abhishek Tiwari

Data integration generally requires in-depth domain knowledge, a strong understanding of data schemas and underlying relationships. This can be time-consuming and bit challenging if you are dealing with hundreds of data sources and thousands of event types (see my recent article on ELT architecture ). Various data integration solution providers are trying to capitalize on this gap by offering various machine learning based features to overcome these challenges.

A case for ELT

Abhishek Tiwari

Cheap storage and on-demand compute in the cloud coupled with the emergence of new big data frameworks and tools are forcing us to rethink the whole ETL and data warehousing architecture. Then we perform frequent batch ETL from application databases to a data warehouse. Often post-extraction data is staged in intermediate tables which is followed by transformation and load steps to migrate data into a target database or data warehouse.

How TripleLift Built an Adtech Data Pipeline Processing Billions of Events Per Day

High Scalability

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. Over the last 3 years, the TripleLift data pipeline scaled from processing millions of events per day to processing billions.

Optimizing data warehouse storage

The Netflix TechBlog

By Anupom Syam Background At Netflix, our current data warehouse contains hundreds of Petabytes of data stored in AWS S3 , and each day we ingest and create additional Petabytes. Some of the optimizations are prerequisites for a high-performance data warehouse.

How Data Inspires Building a Scalable, Resilient and Secure Cloud Infrastructure At Netflix

The Netflix TechBlog

Netflix’s engineering culture is predicated on Freedom & Responsibility, the idea that everyone (and every team) at Netflix is entrusted with a core responsibility and they are free to operate with freedom to satisfy their mission. Central engineering teams enable this operational model by reducing the cognitive burden on innovation teams through solutions related to securing, scaling and strengthening (resilience) the infrastructure.

Reimagining Experimentation Analysis at Netflix

The Netflix TechBlog

After recreating the dataset, you can plot the raw numbers and perform custom analyses to understand the distribution of the data across test cells. However, the default reports only provide a summary view of the data with some powerful but limited filtering options. Our data scientists often want to apply their knowledge of the business and statistics to fully understand the outcome of an experiment. Getting Data with the Metrics Repo 2.

Building and Scaling Data Lineage at Netflix to Improve Data Infrastructure Reliability, and…

The Netflix TechBlog

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 Can I run a check myself to understand what data is behind this metric?” Let’s review a few of these principles: Ensure data integrity ?—?Accurately

Your technology architecture and engineering organization should coevolve as your startup grows

Abhishek Tiwari

The evolution of your technology architecture should depend on the size, culture, and skill set of your engineering organization. There are no hard-and-fast rules to figure out interdependency between technology architecture and engineering organization but below is what I think can really work well for product startup. Skills: Induct Full-stack engineers. Introduce site-reliability engineering best-practices (SLI/SLOs).

Post: Essilen Research, Fauna, Sisu, Educative, PA File Sight, Etleap, Triplebyte, Stream

High Scalability

Sisu Data is looking for machine learning engineers who are eager to deliver their features end-to-end, from Jupyter notebook to production, and provide actionable insights to businesses based on their first-party, streaming, and structured relational data. T riplebyte lets exceptional software engineers skip screening steps at hundreds of top tech companies like Apple, Dropbox, Mixpanel, and Instacart. Who's Hiring?

Post: Essilen Research, Fauna, Sisu, Educative, PA File Sight, Etleap, Triplebyte, Stream

High Scalability

Sisu Data is looking for machine learning engineers who are eager to deliver their features end-to-end, from Jupyter notebook to production, and provide actionable insights to businesses based on their first-party, streaming, and structured relational data. T riplebyte lets exceptional software engineers skip screening steps at hundreds of top tech companies like Apple, Dropbox, Mixpanel, and Instacart. Who's Hiring?

Post: Essilen Research, Fauna, Sisu, Educative, PA File Sight, Etleap, Triplebyte, Stream

High Scalability

Sisu Data is looking for machine learning engineers who are eager to deliver their features end-to-end, from Jupyter notebook to production, and provide actionable insights to businesses based on their first-party, streaming, and structured relational data. T riplebyte lets exceptional software engineers skip screening steps at hundreds of top tech companies like Apple, Dropbox, Mixpanel, and Instacart. Who's Hiring?

Post: InterviewCamp.io, Scrapinghub, Fauna, Sisu, Educative, PA File Sight, Etleap, Triplebyte, Stream

High Scalability

Scrapinghub is hiring a Senior Software Engineer (Big Data/AI). You will be designing and implementing distributed systems : large-scale web crawling platform, integrating Deep Learning based web data extraction components, working on queue algorithms, large datasets, creating a development platform for other company departments, etc. this is going to be a challenging journey for any backend engineer! Who's Hiring? InterviewCamp.io has hours of system design content.

Post: InterviewCamp.io, Scrapinghub, Fauna, Sisu, Educative, PA File Sight, Etleap, Triplebyte, Stream

High Scalability

Scrapinghub is hiring a Senior Software Engineer (Big Data/AI). You will be designing and implementing distributed systems : large-scale web crawling platform, integrating Deep Learning based web data extraction components, working on queue algorithms, large datasets, creating a development platform for other company departments, etc. this is going to be a challenging journey for any backend engineer! Who's Hiring? InterviewCamp.io has hours of system design content.

Post: Scrapinghub, Fauna, Sisu, Educative, PA File Sight, Etleap, Triplebyte, Stream

High Scalability

Scrapinghub is hiring a Senior Software Engineer (Big Data/AI). You will be designing and implementing distributed systems : large-scale web crawling platform, integrating Deep Learning based web data extraction components, working on queue algorithms, large datasets, creating a development platform for other company departments, etc. this is going to be a challenging journey for any backend engineer! Who's Hiring?

Post: Fauna, Sisu, Educative, PA File Sight, Etleap, Triplebyte, Stream

High Scalability

Sisu Data is looking for machine learning engineers who are eager to deliver their features end-to-end, from Jupyter notebook to production, and provide actionable insights to businesses based on their first-party, streaming, and structured relational data. T riplebyte lets exceptional software engineers skip screening steps at hundreds of top tech companies like Apple, Dropbox, Mixpanel, and Instacart. Who's Hiring?

Sponsored Post: InterviewCamp.io, Scrapinghub, Fauna, Sisu, Educative, PA File Sight, Etleap, Triplebyte, Stream

High Scalability

Scrapinghub is hiring a Senior Software Engineer (Big Data/AI). You will be designing and implementing distributed systems : large-scale web crawling platform, integrating Deep Learning based web data extraction components, working on queue algorithms, large datasets, creating a development platform for other company departments, etc. this is going to be a challenging journey for any backend engineer! Who's Hiring? InterviewCamp.io has hours of system design content.

Post: InterviewCamp.io, Scrapinghub, Fauna, Sisu, Educative, PA File Sight, Etleap, Triplebyte, Stream

High Scalability

Scrapinghub is hiring a Senior Software Engineer (Big Data/AI). You will be designing and implementing distributed systems : large-scale web crawling platform, integrating Deep Learning based web data extraction components, working on queue algorithms, large datasets, creating a development platform for other company departments, etc. this is going to be a challenging journey for any backend engineer! Who's Hiring? InterviewCamp.io has hours of system design content.

Sponsored Post: InterviewCamp.io, Scrapinghub, Fauna, Sisu, Educative, PA File Sight, Etleap, Triplebyte, Stream

High Scalability

Scrapinghub is hiring a Senior Software Engineer (Big Data/AI). You will be designing and implementing distributed systems : large-scale web crawling platform, integrating Deep Learning based web data extraction components, working on queue algorithms, large datasets, creating a development platform for other company departments, etc. this is going to be a challenging journey for any backend engineer! Created by former senior-level AWS engineers of 15 years.

Netflix at AWS re:Invent 2019

The Netflix TechBlog

by Shefali Vyas Dalal AWS re:Invent is a couple weeks away and our engineers & leaders are thrilled to be in attendance yet again this year! December 2 1pm-2pm CMP 326-R Capacity Management Made Easy with Amazon EC2 Auto Scaling Vadim Filanovsky , Senior Performance Engineer & Anoop Kapoor, AWS Abstract :Amazon EC2 Auto Scaling offers a hands-free capacity management experience to help customers maintain a healthy fleet, improve application availability, and reduce costs.

AWS 100

Netflix at AWS re:Invent 2019

The Netflix TechBlog

by Shefali Vyas Dalal AWS re:Invent is a couple weeks away and our engineers & leaders are thrilled to be in attendance yet again this year! December 2 1pm-2pm CMP 326-R Capacity Management Made Easy with Amazon EC2 Auto Scaling Vadim Filanovsky , Senior Performance Engineer & Anoop Kapoor, AWS Abstract :Amazon EC2 Auto Scaling offers a hands-free capacity management experience to help customers maintain a healthy fleet, improve application availability, and reduce costs.

AWS 100

Top 20 Websites For Online Automation Testing Courses and Certifications

Testsigma

Udacity Udacity provides nanodegree programs on all automation languages like C++, Machine Learning, Data engineer, Robotics and more. b) Advanced Level – has just the below two certifications: Security Tester Test Automation Engineer. Certifications, typically, are proof of the enhanced prowess in the stream for which the course has been taken. Certifications and Courses help validate as well as enhance our technical capability in a specific vertical.

Symphonia at Velocity 2018, and more Serverless Insights

The Symphonia

From data engineering, to cost management, via conversations about team dynamics and architecture, we like to get involved with all-things-cloud-and-DevOps related at our clients. In the talk I mentioned above from Lynn Langit, she described a process of using AWS Glue , S3, and various other tools to build a compelling Data Lake platform. Over the past 9 months John’s been helping our friends at Beyondsoft with their new open-source Serverless data lake project?—?

Organise your engineering teams around the work by reteaming

Abhishek Tiwari

When it comes to organising engineering teams, a popular view has been to organise your teams based on either Spotify's agile model (i.e. On a positive note, both organisational models focus on having small yet productive independent cross-functional teams and there is nothing wrong to draw inspiration from either of these models when building your engineering organisation. One thing stand-out to me is being intentional and practical about your engineering organisation design.

Microsoft Engineering loves SQLBits

SQL Server According to Bob

Microsoft engineering is actually sending quite a few folks over the Atlantic to come talk about SQL Server 2017, SQL Server on Linux, GDPR, Performance, Security, Azure Data Lake, Azure SQL Database, Azure SQL Data Warehouse, and Azure CosmosDB. If you want to know anything about Azure SQL Data Warehouse, you have to come listen to JRJ! Best practices on Building a Big Data Analytics Solution – Michael Rys. More great Azure Data Lake with Michael.

Back-to-Basics Weekend Reading - The 5 Minute Rule - All Things.

All Things Distributed

Which makes this week a good moment to read up on some of the historical work around the costs of data engineering. For this purpose I have picked work based on two papers by Jim Gray , the brilliant IBM / Tandem / Microsoft researcher, who won a Turing award for his contributions to data and transaction processing. Jim together with Gianfranco Putzolu explores the cost trade-offs between holding pages in memory versus doing IO every time the page data would be accessed.

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High Scalability

Sisu Data is looking for machine learning engineers who are eager to deliver their features end-to-end, from Jupyter notebook to production, and provide actionable insights to businesses based on their first-party, streaming, and structured relational data. T riplebyte lets exceptional software engineers skip screening steps at hundreds of top tech companies like Apple, Dropbox, Mixpanel, and Instacart. Who's Hiring?

Spice up your Analytics: Amazon QuickSight Now Generally Available in N. Virginia, Oregon, and Ireland.

All Things Distributed

Previously, I wrote about Amazon QuickSight , a new service targeted at business users that aims to simplify the process of deriving insights from a wide variety of data sources quickly, easily, and at a low cost. When we announced QuickSight last year, we set out to help all customers—regardless of their technical skills—make sense out of their ever-growing data. Put simply, data is not always readily available and accessible to organizational end users.

Sponsored Post: Fauna, Sisu, Educative, PA File Sight, Etleap, Triplebyte, Stream

High Scalability

Sisu Data is looking for machine learning engineers who are eager to deliver their features end-to-end, from Jupyter notebook to production, and provide actionable insights to businesses based on their first-party, streaming, and structured relational data. T riplebyte lets exceptional software engineers skip screening steps at hundreds of top tech companies like Apple, Dropbox, Mixpanel, and Instacart. Who's Hiring?

Netflix at AWS re:Invent 2019

The Netflix TechBlog

by Shefali Vyas Dalal AWS re:Invent is a couple weeks away and our engineers & leaders are thrilled to be in attendance yet again this year! December 2 1pm-2pm CMP 326-R Capacity Management Made Easy with Amazon EC2 Auto Scaling Vadim Filanovsky , Senior Performance Engineer & Anoop Kapoor, AWS Abstract :Amazon EC2 Auto Scaling offers a hands-free capacity management experience to help customers maintain a healthy fleet, improve application availability, and reduce costs.

Sponsored Post: Fauna, Sisu, Educative, PA File Sight, Etleap, PerfOps, Triplebyte, Stream

High Scalability

Sisu Data is looking for machine learning engineers who are eager to deliver their features end-to-end, from Jupyter notebook to production, and provide actionable insights to businesses based on their first-party, streaming, and structured relational data. T riplebyte lets exceptional software engineers skip screening steps at hundreds of top tech companies like Apple, Dropbox, Mixpanel, and Instacart. Who's Hiring?

Sponsored Post: Fauna, Sisu, Educative, PA File Sight, Etleap, PerfOps, Triplebyte, Stream

High Scalability

Sisu Data is looking for machine learning engineers who are eager to deliver their features end-to-end, from Jupyter notebook to production, and provide actionable insights to businesses based on their first-party, streaming, and structured relational data. T riplebyte lets exceptional software engineers skip screening steps at hundreds of top tech companies like Apple, Dropbox, Mixpanel, and Instacart. Who's Hiring?

Sponsored Post: Fauna, Sisu, Educative, PA File Sight, Etleap, PerfOps, Triplebyte, Stream

High Scalability

Sisu Data is looking for machine learning engineers who are eager to deliver their features end-to-end, from Jupyter notebook to production, and provide actionable insights to businesses based on their first-party, streaming, and structured relational data. T riplebyte lets exceptional software engineers skip screening steps at hundreds of top tech companies like Apple, Dropbox, Mixpanel, and Instacart. Who's Hiring?

Sponsored Post: Fauna, Sisu, Educative, PA File Sight, Etleap, PerfOps, Triplebyte, Stream

High Scalability

Sisu Data is looking for machine learning engineers who are eager to deliver their features end-to-end, from Jupyter notebook to production, and provide actionable insights to businesses based on their first-party, streaming, and structured relational data. T riplebyte lets exceptional software engineers skip screening steps at hundreds of top tech companies like Apple, Dropbox, Mixpanel, and Instacart. Who's Hiring?