Data Movement in Netflix Studio via Data Mesh

The Netflix TechBlog

This happens at an unprecedented scale and introduces many interesting challenges; one of the challenges is how to provide visibility of Studio data across multiple phases and systems to facilitate operational excellence and empower decision making.

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.

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Data Democratization and How to Get Started?

DZone

Today data is an important factor for business success. In every business, it has been observed that data is playing a game-changing moment to improve business performance. Data is important and necessary in this increasingly competitive world.

3 Performance Tricks for Dealing With Big Data Sets

DZone

This article describes 3 different tricks that I used in dealing with big data sets (order of 10 million records) and that proved to enhance performance dramatically. tips and tricks big data performace big data sets

What is Greenplum Database? Intro to the Big Data Database

Scalegrid

It can scale towards a multi-petabyte level data workload without a single issue, and it allows access to a cluster of powerful servers that will work together within a single SQL interface where you can view all of the data. Polymorphic Data Storage.

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. Kevin, what drew you to data engineering?

Exploring Data @ Netflix

The Netflix TechBlog

By Gim Mahasintunan on behalf of Data Platform Engineering. Supporting a rapidly growing base of engineers of varied backgrounds using different data stores can be challenging in any organization.

Bulldozer: Batch Data Moving from Data Warehouse to Online Key-Value Stores

The Netflix TechBlog

By Tianlong Chen and Ioannis Papapanagiotou Netflix has more than 195 million subscribers that generate petabytes of data everyday. The processed data is typically stored as data warehouse tables in AWS S3. Figure 1 shows how we use Bulldozer to move data at Netflix.

Bucketizing date and time data

SQL Performance

Bucketizing date and time data involves organizing data in groups representing fixed intervals of time for analytical purposes. Often the input is time series data stored in a table where the rows represent measurements taken at regular time intervals.

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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.

Byte Down: Making Netflix’s Data Infrastructure Cost-Effective

The Netflix TechBlog

cloud-storage data data-infrastructure aws netflixBy Torio Risianto, Bhargavi Reddy, Tanvi Sahni, Andrew Park Continue reading on Netflix TechBlog ».

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.

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. Data Engineers of Netflix?—?Interview

DBLog: A Generic Change-Data-Capture Framework

The Netflix TechBlog

Andreas Andreakis , Ioannis Papapanagiotou Overview Change-Data-Capture (CDC) allows capturing committed changes from a database in real-time and propagating those changes to downstream consumers [1][2]. This is crucial for repairs downstream when data has been lost or corrupted.

DBLog: A Generic Change-Data-Capture Framework

The Netflix TechBlog

Andreas Andreakis , Ioannis Papapanagiotou Overview Change-Data-Capture (CDC) allows capturing committed changes from a database in real-time and propagating those changes to downstream consumers [1][2]. This is crucial for repairs downstream when data has been lost or corrupted.

Delta: A Data Synchronization and Enrichment Platform

The Netflix TechBlog

Part I: Overview Andreas Andreakis , Falguni Jhaveri , Ioannis Papapanagiotou , Mark Cho , Poorna Reddy , Tongliang Liu Overview It is a commonly observed pattern for applications to utilize multiple datastores where each is used to serve a specific need such as storing the canonical form of data (MySQL etc.), Beyond data synchronization, some applications also need to enrich their data by calling external services.

Giving data a heartbeat

Dynatrace

I love data. I have spent virtually my entire career looking at data. Synthetic data, network data, system data, and the list goes on. In recent years, the amount of data we analyze has exploded as we look at the data collected by Real User Monitoring (RUM), meaning every session, every action, in every region and so on. As much as I love data, data is cold, it lacks emotion. Dynatrace news.

Scenarios when Data-Driven Testing is useful

Testsigma

In today’s world where ‘data is the new oil’ ( as said by Clive Humby), not giving proper attention to data-driven testing is not justified. If you have an application that needs data input in some form then it will require data-driven testing. Scenario 1: Tabular data .

How Amazon is solving big-data challenges with data lakes

All Things Distributed

Amazon's worldwide financial operations team has the incredible task of tracking all of that data (think petabytes). At Amazon's scale, a miscalculated metric, like cost per unit, or delayed data can have a huge impact (think millions of dollars).

Visualize Data Structures in VSCode

Addy Osmani

VSCode Debug Visualizer is a VSCode extension that allows you to visualize data structures in your editor

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NoSQL Data Modeling Techniques

Highly Scalable

At the same time, NoSQL data modeling is not so well studied and lacks the systematic theory found in relational databases. In this article I provide a short comparison of NoSQL system families from the data modeling point of view and digest several common modeling techniques. To explore data modeling techniques, we have to start with a more or less systematic view of NoSQL data models that preferably reveals trends and interconnections.

Top Redis Use Cases by Core Data Structure Types

Scalegrid

Redis , short for Remote Dictionary Server, is a BSD-licensed, open-source in-memory key-value data structure store written in C language by Salvatore Sanfillipo and was first released on May 10, 2009. This implies that unlike SQL (Structured Query Language) driven database systems like MySQL, PostgreSQL, and Oracle, Redis does not store data in well-defined database schemas which constitute tables, rows, and columns. Data Structures in Redis.

DBLog: A Generic Change-Data-Capture Framework

The Netflix TechBlog

database postgres data-synchronization change-data-capture mysqlAndreas Andreakis, Ioannis Papapanagiotou Continue reading on Netflix TechBlog ».

How Uber Achieves Operational Excellence in the Data Quality Experience

Uber Engineering

While growing rapidly, we’re also committed to maintaining data quality, as it can greatly … The post How Uber Achieves Operational Excellence in the Data Quality Experience appeared first on Uber Engineering Blog. Architecture General Engineering Uber Data

How to Improve Data Center Incident Reporting

DZone

More markets are depending on the cloud to deliver their technology services, which puts increasing pressure on data centers and their staff to keep things running smoothly. performance data center incident management data outage

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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

Data Compression for Large-Scale Streaming Experimentation

The Netflix TechBlog

To make this happen, we developed an effective data compression technique by cleverly bucketing our data. This reduced the volume of our data by up to 1,000 times, allowing us to compute statistics in just a few seconds while maintaining precise results.

Open-Sourcing Metaflow, a Human-Centric Framework for Data Science

The Netflix TechBlog

Netflix applies data science to hundreds of use cases across the company, including optimizing content delivery and video encoding. Data scientists at Netflix relish our culture that empowers them to work autonomously and use their judgment to solve problems independently.

Understand customer experience with Session Replay without compromising data privacy

Dynatrace

It helps you identify errors, analyze areas of struggle, and provides tons of analytical data for your testing teams. This allows you to capture your users’ experiences while remaining compliant with the data privacy regulations of your region. Dynatrace news.

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Data Compression for Large-Scale Streaming Experimentation

The Netflix TechBlog

To make this happen, we developed an effective data compression technique by cleverly bucketing our data. This reduced the volume of our data by up to 1,000 times, allowing us to compute statistics in just a few seconds while maintaining precise results.

Data Mining Problems in Retail

Highly Scalable

Retail is one of the most important business domains for data science and data mining applications because of its prolific data and numerous optimization problems such as optimal prices, discounts, recommendations, and stock levels that can be solved using data analysis methods. Although there are many books on data mining in general and its applications to marketing and customer relationship management in particular [BE11, AS14, PR13 etc.],

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Data Compression for Large-Scale Streaming Experimentation

The Netflix TechBlog

To make this happen, we developed an effective data compression technique by cleverly bucketing our data. This reduced the volume of our data by up to 1,000 times, allowing us to compute statistics in just a few seconds while maintaining precise results.

GraphQL, Data Sources, and Visual Testing

DZone

In the ever-changing world of development, new tools are constantly popping up to help developers create and manage complex solutions with the ultimate goal of building a great experience for their customers. Whether it's a tool to help that developer become more productive or an entirely new way of thinking about the architecture of the project, developers are able to spend more time focusing on the unique parts of our code and focus on that great experience.

Storage handling improvements increase retention of transaction data for Dynatrace Managed

Dynatrace

Using existing storage resources optimally is key to being able to capture the right data over time. In this blog post, we announce: Compression of transaction data that’s older than three days. Improvements to Adaptive Data Retention. Session Replay data.

DynatraceGo! APAC 2021: Lessons in thick data and keeping pace with the market

Dynatrace

This year’s conference agenda was packed full of choices, including: Keynotes : Topics included accelerating digital transformation, with Dynatrace CIO Mike Maciag, and Spatial Collapse: The Great Acceleration of Turning Data Into an Asset, with Tricia Wang from Sudden Compass. Dynatrace news.

Building a responsible data capture policy

Dynatrace

Data capture is a powerful way to add business context, but it must be used responsibly. Let’s step away from Dynatrace for a moment and learn about responsible data capture more generally. Louis on Leadership Perspectives: Data Responsibility and the Ethics of Analytics.

Create compelling insights into business and operational KPIs through metric calculations in the Data explorer

Dynatrace

As objective measurements, they allow us to make data-driven decisions. The Data explorer. With the update to Dynatrace version 1.222, you can now use the new Data explorer interface to interactively build, test, and create your own metric calculations—right on your dashboards.

Oracle Data Integrator (ODI): Executing A Load Plan

DZone

A Load Plan is an executable object in Oracle Data Integrator that can contain a sequence of several types of steps and each step can contain a child steps. performance odi 12c load plan oracle data oracle data integraterWhat Is Load Plan?

Mergeable replicated data types – Part I

The Morning Paper

Mergeable replicated data types Kaki et al., Mergeable Replicated Data Types (MRDTs) are in the same spirit as CRDTs but with the very interesting property that they compose. As the name suggests, the abstraction exposed to the programmer is of a replicated data type.

In-Stream Big Data Processing

Highly Scalable

The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. In recent years, this idea got a lot of traction and a whole bunch of solutions like Twitter’s Storm, Yahoo’s S4, Cloudera’s Impala, Apache Spark, and Apache Tez appeared and joined the army of Big Data and NoSQL systems. The engine can also involve relatively static data (admixtures) loaded from the stores of Aggregated Data.

Analyze all AWS data in minutes with Amazon CloudWatch Metric Streams available in Dynatrace

Dynatrace

Amazon CloudWatch gathers metric data from various services that run on AWS. Dynatrace ingests this data to perform root-cause analysis using the Dynatrace Davis® AI engine. This allows for fast and direct push of metric data from the source to Dynatrace. Dynatrace news.

ScyllaDB Trends – How Users Deploy The Real-Time Big Data Database

Scalegrid

ScyllaDB is an open-source distributed NoSQL data store, reimplemented from the popular Apache Cassandra database. ScyllaDB offers significantly lower latency which allows you to process a high volume of data with minimal delay. There are dozens of quality articles on ScyllaDB vs. Cassandra, so we’ll stop short here so we can get to the real purpose of this article, breaking down the ScyllaDB user data.

Mergeable replicated data types – Part II

The Morning Paper

Mergeable replicated data types – part II Kaki et al., Today we’re picking things up in §4 of the paper, starting with how to derive a merge function for an arbitrary data type. Not every element in a composite data type may itself be mergeable though.