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.

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.

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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.

Using JSONB in PostgreSQL: How to Effectively Store & Index JSON Data in PostgreSQL

Scalegrid

It is an open standard format which organizes data into key/value pairs and arrays detailed in RFC 7159. JSON is the most common format used by web services to exchange data, store documents, unstructured data, etc. JSONB Data Structures. JSONB Data Structures.

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).

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.

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 ».

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.

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.

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.

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.

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

Cloud 141

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.

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 ».

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.

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.

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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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.

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.

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.

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.

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 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.

The power of relationships in data

All Things Distributed

Having a deep understanding of the relationships in your data is powerful like that. With enough relationship data points, you can even make predictions about the future (like with a recommendation engine).

Ensure that sensitive customer data is protected while monitoring mobile apps

Dynatrace

But your applications often contain sensitive data that must be kept secure and in compliance with your company’s security policies. To remain compliant, you need to ensure protection for certain types of data and specific attributes that may contain personally identifiable information (PII).

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Use the Davis® AI to detect outages within your custom data streams

Dynatrace

In today’s complex IT environments, the sheer volume of data created makes it impossible for humans to monitor, comprehend, or troubleshoot problems before they impact the experience of your end users. Still, you might have use cases that rely on important custom data streams.

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.

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.

Probabilistic Data Structures for Web Analytics and Data Mining

Highly Scalable

Statistical analysis and mining of huge multi-terabyte data sets is a common task nowadays, especially in the areas like web analytics and Internet advertising. Analysis of such large data sets often requires powerful distributed data stores like Hadoop and heavy data processing with techniques like MapReduce. The picture above depicts the fact that this data set basically occupies 40MB of memory (10 million of 4-byte elements).

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.

Data validation for machine learning

The Morning Paper

Data validation for machine learning Breck et al., Last time out we looked at continuous integration testing of machine learning models , but arguably even more important than the model is the data. In this paper we focus on the problem of validation the input data fed to ML pipelines. Irrespective of the ML algorithms used, data errors can adversely affect the quality of the generated model. The model will now underperform for the affected slice of data.

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

The Netflix TechBlog

machine-learning data-science python open-source productivityby David Berg, Ravi Kiran Chirravuri, Romain Cledat, Savin Goyal, Ferras Hamad, Ville Tuulos Continue reading on Netflix TechBlog ».

Uber’s Data Platform in 2019: Transforming Information to Intelligence

Uber Engineering

Architecture Uber Data Big Data Data Analytics Data Platform EoY Infrastructure Product Platform Uber Engineering

View Execution Plans in Azure Data Studio

SQL Shack

This article gives an overview of viewing execution plans in the Azure Data Studio. Azure Data Studio Execution plans MonitoringIntroduction Database administrators use a query execution plan for troubleshooting query performance issues.

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Data Shapley: equitable valuation of data for machine learning

The Morning Paper

Data Shapley: equitable valuation of data for machine learning Ghorbani & Zou et al., The now somewhat tired phrase “data is the new oil” (something we can consume in great quantities to eventually destroy the world as we know it???) suggests that data has value. But pinning down that value can be tricky – how much is a given data point worth, and what framework can we use for thinking about that question? “ Data Shapley in action.

Games 77

Big / Bug Data: Analyzing the Apache Flink Source Code

DZone

Applications used in the field of Big Data process huge amounts of information, and this often happens in real time. Naturally, such applications must be highly reliable so that no error in the code can interfere with data processing. Today, the Apache Flink project developed by the Apache Software Foundation, one of the leaders in the Big Data software market, was chosen as a test subject for the analyzer.

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Top Benefits of Data-Driven Test Automation

Testsigma

If done manually, the process of data-driven testing is really time-consuming. It is usually used for regression testing or with test cases where the same test scripts are run by providing huge test data as input, sequentially. What is data-driven automation testing ?