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Chaos Data Engineering Manifesto: 5 Laws for Successful Failures

DZone

It's midnight in the dim and cluttered office of The New York Times, currently serving as the "situation room." A powerful surge of traffic is inevitable. During every major election, the wave would crest and crash against our overwhelmed systems before receding, allowing us to assess the damage

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Data Engineers of Netflix?—?Interview with Kevin Wylie

The Netflix TechBlog

Data Engineers of Netflix?—?Interview Interview with Kevin Wylie This post is part of our “Data Engineers of Netflix” series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. Data Engineers of Netflix?—?Interview

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Data Engineers of Netflix?—?Interview with Pallavi Phadnis

The Netflix TechBlog

Data Engineers of Netflix?—?Interview Interview with Pallavi Phadnis This post is part of our “ Data Engineers of Netflix ” series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix.

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Data Engineers of Netflix?—?Interview with Dhevi Rajendran

The Netflix TechBlog

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.

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Data Engineers of Netflix?—?Interview with Samuel Setegne

The Netflix TechBlog

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.

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Ready-to-go sample data pipelines with Dataflow

The Netflix TechBlog

by Jasmine Omeke , Obi-Ike Nwoke , Olek Gorajek Intro This post is for all data practitioners, who are interested in learning about bootstrapping, standardization and automation of batch data pipelines at Netflix. A large number of our data users employ SparkSQL, pyspark, and Scala.

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SQL Extensions for Time-Series Data in QuestDB

DZone

In this tutorial, you are going to learn about QuestDB SQL extensions which prove to be very useful with time-series data. Using some sample data sets, you will learn how designated timestamps work and how to use extended SQL syntax to write queries on time-series data. Introduction Traditionally, SQL has been used for relational databases and data warehouses. sql databases data engineering

IoT 178
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Secrets Detection: Optimizing Filter Processes

DZone

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. performance data engineering benchmarking scanning

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Data Ingestion: The First Step Towards a Flawless Data Pipeline

Simform

Data ingestion is the foremost layer in a data engineering pipeline, acting as a vital pillar in the overall analytics architecture. Thus, it is essential to implement data ingestion just right. Data Engineering

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Data pipeline asset management with Dataflow

The Netflix TechBlog

JAR) form to be executed as part of the user defined data pipeline. data pipeline ?—?a DAG) for the purpose of transforming data using some business logic. Netflix homegrown CLI tool for data pipeline management. by Sam Setegne, Jai Balani, Olek Gorajek Glossary asset ?—?any

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Stream Processing: How it Works, Use Cases & Popular Frameworks

Simform

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. Data Engineering

IoT 52
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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?

Analytics 244
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What is a Data Pipeline: Types, Architecture, Use Cases & more

Simform

Businesses can unlock the value of data only after it is transformed into actionable insights and when those insights are delivered promptly. But implementing such robust data pipelines can be complex and challenging. Data Engineering

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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 196
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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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Hyper Scale VPC Flow Logs enrichment to provide Network Insight

The Netflix TechBlog

By collecting, accessing and analyzing network data from a variety of sources like VPC Flow Logs, ELB Access Logs, Custom Exporter Agents, etc, we can provide Network Insight to users through multiple data visualization techniques like Lumen , Atlas , etc.

Network 155
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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.

Analytics 140
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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.

Analytics 138
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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

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Google Announces the General Availability of A2 Virtual Machines

InfoQ

Recently, Google announced A2 Virtual Machines (VMs)' general availability based on the NVIDIA Ampere A100 Tensor Core GPUs in Compute Engine.

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The 31 Flavors of Data Lineage and Why Vanilla Doesn’t Cut It

DZone

Data lineage, an automated visualization of the relationships for how data flows across tables and other data assets, is a must-have in the data engineering toolbox. Not only is it helpful for data governance and compliance use cases, but it also plays a starring role as one of the five pillars of data observability.

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Friends don't let friends build data pipelines

Abhishek Tiwari

Building data pipelines can offer strategic advantages to the business. Often companies underestimate the necessary effort and cost involved to build and maintain data pipelines. Data pipeline initiatives are generally unfinished projects. In this post, we will discuss why you should avoid building data pipelines in first place. If built correctly, data pipelines can offer strategic advantages to the business. Depending on frameworks, data processing units (a.k.a

Latency 63
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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.

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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.

Storage 49
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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 ??.

Design 40
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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.

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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.

Storage 40
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Bringing Software Engineering Rigor to Data

DZone

In software engineering, we've learned that building robust and stable applications has a direct correlation with overall organization performance. The data community is striving to incorporate the core concepts of engineering rigor found in software communities but still has further to go.

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5 key areas for tech leaders to watch in 2020

O'Reilly

It’s also the data source for our annual usage study, which examines the most-used topics and the top search terms. [1]. This year’s growth in Python usage was buoyed by its increasing popularity among data scientists and machine learning (ML) and artificial intelligence (AI) engineers.

Media 106
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Microservices Adoption in 2020

O'Reilly

Software engineers comprise the survey audience’s single largest cluster, over one quarter (27%) of respondents (Figure 1). Adding architects and engineers, we see that roughly 55% of the respondents are directly involved in software development. Microservices seem to be everywhere.

Media 126
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Engineering Data Reliably Using SLO Theory – Percona Live ONLINE Talk Preview

Percona Community

Percona Live Online Agenda Slot: Tue 20 Oct • New York 12:30 p.m. London 5:30 p.m. • New Delhi 10:00 p.m. Singapore 12:30 a.m.

DevOps 52
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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.

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AI meets operations

O'Reilly

First, the behavior of an AI application depends on a model , which is built from source code and training data. A model isn’t source code, and it isn’t data; it’s an artifact built from the two. You need a repository for models and for the training data.

Media 59
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Percona Live Europe Tutorial: Elasticsearch 101

Percona Community

For Percona Live Europe, I’ll be presenting the tutorial Elasticsearch 101 alongside my colleagues and fellow presenters from ObjectRocket Alex Cercel, DBA, and Mihai Aldoiu, Data Engineer.

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Formulating ‘Out of Memory Kill’ Prediction on the Netflix App as a Machine Learning Problem

The Netflix TechBlog

We at Netflix, as a streaming service running on millions of devices, have a tremendous amount of data about device capabilities/characteristics and runtime data in our big data platform. Secondly, and more importantly, the sheer volume of the runtime data is a lot.

Big Data 190
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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.

Storage 215
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Experimentation is a major focus of Data Science across Netflix

The Netflix TechBlog

Here we describe the role of Experimentation and A/B testing within the larger Data Science and Engineering organization at Netflix, including how our platform investments support running tests at scale while enabling innovation. What other data and intuition can I bring to the problem?”

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Scaling Appsec at Netflix (Part 2)

The Netflix TechBlog

Our customers are product and engineering teams at Netflix that build these software services and platforms. This became the foundation for our current org structure with teams focused on Appsec Partnerships and Appsec Engineering.

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A Day in the Life of an Experimentation and Causal Inference Scientist @ Netflix

The Netflix TechBlog

Stephanie Lane , Wenjing Zheng , Mihir Tendulkar Source credit: Netflix Within the rapid expansion of data-related roles in the last decade, the title Data Scientist has emerged as an umbrella term for myriad skills and areas of business focus. Learning through data is in Netflix’s DNA.

Analytics 219
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Sustainability at AWS re:Invent 2022 All the talks and videos I could find…

Adrian Cockcroft

SUS206 Sustainability and AWS silicon  — Kamran Khan AWS Senior Product Manager Inferential/Trainium/FPGA, David Chaiken Pinterest Chief Architect, and Paul Mazurkiewicz AWS Senior Principal Engineer. Building a data lake of detailed information about energy use of many physical devices.

Energy 64
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Orchestrating Data/ML Workflows at Scale With Netflix Maestro

The Netflix TechBlog

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. a data table or a spreadsheet) to another periodically.

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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. Skills: Induct Full-stack engineers. Introduce site-reliability engineering best-practices (SLI/SLOs).

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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.