Tableau Conference 2018: Binging on Data - Enabling Analytics at Netflix

People, Platform, Projects: these slides overview how Netflix works with Big Data. I share how our teams are organized, the roles we typically have on the teams, an overview of our Big Data Platform, and two example projects.

At Netflix, we try to provide the best personalized video recommendations to our members. To do this, we need to adapt our recommendations for each contextual situation, which depends on information such as time or device. In this talk, I will describe how state of the art Contextual Recommendations are used at Netflix. A first example of contextual adaptation is the model that powers the Continue Watching row. It uses a feature-based approach with a carefully constructed training set to learn how to adapt to the context of the member. Next, I will dive into more modern approaches such as Tensor Factorization and LSTMs and share some results from deployments of these methods. I will highlight lessons learned and some common pitfalls of using these powerful methods in industrial scale systems. Finally, I will touch upon system reliability, choice of optimization metrics, hidden costs, risks and benefits of using highly adaptive systems.

This document discusses making Netflix machine learning algorithms reliable. It describes how Netflix uses machine learning for tasks like personalized ranking and recommendation. The goals are to maximize member satisfaction and retention. The models and algorithms used include regression, matrix factorization, neural networks, and bandits. The key aspects of making the models reliable discussed are: automated retraining of models, testing training pipelines, checking models and inputs online for anomalies, responding gracefully to failures, and training models to be resilient to different conditions and failures.

At Netflix we take context of the member seriously. In this keynote talk we will see how modeling contextual factors such as time or device can help members to find the right content at the right moment At the end, the goal is to maximize member satisfaction and retention These slides will go through which contextual factors matters for the video service and why we choose to use them or not.

Invited talk at the Adaptive and Multi-Task Learning (AMTL) workshop at ICML 2019 on 2019-06-15 in Long Beach, CA.

In this talk, we talk about the algorithms for sequential decision making in recommendations and the challenges around it.

Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh? In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry. The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems. This session is targeted for architects, decision-makers, data-engineers, and system designers.

These are the slides from my talk presented at AI Next Con conference in Seattle in Jan 2019. Here I talk in a bit more detail about the intuition behind collaborative filtering and go a bit deeper into the details of non linear deep learned models.

Faisal Siddiqi presented on machine learning infrastructure for recommendations. He outlined Boson and AlgoCommons, two major ML infra components. Boson focuses on offline training for both ad-hoc exploration and production. It provides utilities for data preparation, feature engineering, training, metrics, and visualization. AlgoCommons provides common abstractions and building blocks for ML like data access, feature encoders, predictors, and metrics. It aims for composability, portability, and avoiding training-serving skew.

Slides from JupyterCon 2018 in NYC on 8/23/2018. Notebooks have moved beyond a niche solution at Netflix; they are now the critical path for how everyone runs jobs against the company’s data platform. From creating original content to delivering bufferless streaming, Netflix relies on notebooks to inform decisions and fuel experiments across the company. Netflix also uses notebooks to power its machine learning infrastructure and run over 150,000 jobs against its 100 PB cloud-based data warehouse every day. The goal is to deliver a compelling notebooks experience that simplifies end-to-end workflows for every type of user. To enable this, Netflix is investing deeply in notebook infrastructure and open source projects such as nteract. In this talk, Michelle Ufford and Kyle Kelley share interesting ways Netflix uses data and some of the big bets the company is making on notebooks. Topics will include architecture, kernels, UIs, and Netflix’s open source collaborations with projects such as Jupyter, nteract, pandas, and Spark.

In this talk, we will provide an overview of Deep Learning methods applied to personalization and search at Netflix. We will set the stage by describing the unique challenges faced at Netflix in the areas of recommendations and information retrieval. Then we will delve into how we leverage a blend of traditional algorithms and emergent deep learning methods and new types of embeddings, especially hyperbolic space embeddings, to address these challenges.

This document provides an overview of the data streaming ecosystem at Booking.com. It discusses how Booking.com uses Apache Kafka, Kafka Connect, and related tools across over 300 clusters containing over 350 brokers to handle large volumes of streaming data from its various services and applications. Key aspects of Booking.com's data streaming infrastructure are highlighted, including its use of multiple data centers, global and local clusters, monitoring and alerting systems, and operational best practices.

In this talk at the Netflix Machine Learning Platform Meetup on 12 Sep 2019, Fernando Amat and Elliot Chow from Netflix talk about the Bandit infrastructure for Personalized Recommendations

Talk from Data by the Bay 2016 on some recommendations for building machine learning software and putting machine learning into applications.

Neo4j GraphTour Europe 2019: Neo4j Graph Use Cases, Bruno Ungermann, Sales Director Germany & Switzerland, Neo4j

In their webinar "Big Data Fabric 2.0 Drives Data Democratization" Ben Szekley, Cambridge Semantics’ SVP of Field Operations, and guest speaker, Forrester’s Noel Yuhanna, author of the Forrester report: “Big Data Fabric 2.0 Drives Data Democratization”, explored why data-driven businesses are making a big data fabric part of their data strategy to minimize data complexity, integrate siloed data, deliver real-time trusted insights, and to create new business opportunities. These are the slides from that webinar.

Zipline is Airbnb’s data management platform specifically designed for ML use cases. Previously, ML practitioners at Airbnb spent roughly 60% of their time on collecting and writing transformations for machine learning tasks. Zipline reduces this task from months to days – by making the process declarative. It allows data scientists to easily define features in a simple configuration language. The framework then provides access to point-in-time correct features – for both – offline model training and online inference. In this talk we will describe the architecture of our system and the algorithm that makes the problem of efficient point-in-time correct feature generation, tractable. The attendee will learn Importance of point-in-time correct features for achieving better ML model performance Importance of using change data capture for generating feature views An algorithm – to efficiently generate features over change data. We use interval trees to efficiently compress time series features. The algorithm allows generating feature aggregates over this compressed representation. A lambda architecture – that enables using the above algorithm – for online feature generation. A framework, based on category theory, to understand how feature aggregations be distributed, and independently composed. While the talk if fairly technical – we will introduce all the concepts from first principles with examples. Basic understanding of data-parallel distributed computation and machine learning might help, but are not required.

The document discusses AlpineNow, a company that provides advanced analytics solutions for big data. It describes how AlpineNow allows for code-free, visual and collaborative analytics that reduce the time to insights from weeks/months to hours/days. Key features highlighted include automated data collection, self-serve visual exploration and analysis of entire datasets, and multi-user collaboration on models and projects.

This document provides an overview of business intelligence concepts and technologies. It summarizes the experience of Argelo Royce Bautista working in business intelligence at Analog Devices, where tools like Power BI and Excel helped improve warehouse operations by reducing cycle times, increasing capacity, and enabling more predictive decision making. The document also briefly reviews common BI tools and technologies from Microsoft and how to obtain them.

Mark Rittman presented at the UKOUG Tech'17 Conference in December 2017. He discussed how analytics has changed business models and driven disruption twice already. The first wave focused on using analytics for operational efficiency. The second wave saw companies like Amazon, Netflix, and Uber build entirely new data-driven business models. Now, a third wave is underway where analytics and machine learning are being embedded into all business applications and fueling new personalized, data-enriched offerings.

Lunch & Learn Presentation about Data & analytics industry, given @ IESE Business School, MBA program, in Barcelona

Join us live in Portland or over the wire for networking and two fantastic presentations! Data viz freelancer Desireé Abbott will demonstrate how adding interactivity to your dashboards will delight and spark curiosity in your users. Then, Charlotte Taft & Laurie Rugemer will reprise their TC24 presentation on the keys to building a successful analytics team.

Join us live in Portland or over the wire for networking and two fantastic presentations! Data viz freelancer Desireé Abbott will demonstrate how adding interactivity to your dashboards will delight and spark curiosity in your users. Then, Charlotte Taft & Laurie Rugemer will reprise their TC24 presentation on the keys to building a successful analytics team.

Delivered at Kristu Jayanti College, Feb 1, 2018 During IEEE International Conference on Current Trends in Advanced Computing. Github - https://github.com/raghu-icecraft/tech-talks/tree/master/Tableau_Feb%2018

In this talk, we will present how we used Spark, Databricks, Airflow and MLflow to process big data, and build a pipeline of both ML(XGBoost) and statistical models that maximizes our revenues in one of our core products, called the “Offer Wall”. The “Offer wall” is a mobile phone product that is integrated with existing apps, suggesting users to perform tasks in exchange for in-app currency. The problem gets even more interesting when considering the fact that some of the tasks users do take 15 minutes and some may take up to 2 to weeks, forcing us to make revenue determining decisions in an uncertain space all of the time. The solution we developed utilizes Databricks and Spark’s strengths and diversity in machine learning, big data, MLflow and Airflow integrations, allowing us to deliver a production-grade solution with short development time between experiments.

This document discusses FinancialForce's new Data Pipeline feature, which allows developers to process large amounts of data in parallel using Apache Pig scripts. Key points include: - Data Pipeline uses Pig Latin scripts to run Extract-Transform-Load processes on large datasets asynchronously and in parallel to improve performance. - It enables processing data faster than other asynchronous Apex options while avoiding governor limits. - Demo use cases show extracting specific fields from invoices and joining data from multiple objects. - FinancialForce also discussed their new "Big Objects" that allow storing very large amounts of data separately from standard objects to avoid storage limits.

Hear about insights gained and other benefits of leveraging SAP Process Mining by Celonis at two of the largest global enterprises in their respective industries: SAP SE and Schlumberger. Mark Saul, Head of Process Management at SAP SE has been spearheading the planning, introduction and successful implementation of SAP Process Mining at SAP. He will outline the benefits and use cases that are relevant for Europe’s largest software company by using SAP Process Mining with SAP S/4 HANA, SAP Data Hub and the positive outcomes for the company. Jim Brady, Vice President Architecture & Governance from Schlumberger will highlight the company’s SAP GoLive of one of the largest launches recent history. In particular, using SAP Process Mining during the vital hypercare period in that global SAP launch. The focus during that critical time is on adaption monitoring, conformance monitoring, de-bottlenecking, and in part design validation to ensure the SAP launch proves to be a big success. Presenters: Alex Marx, Global Partner Director, SAP James P. Brady, Vice President IT Architecture & Governance, Schlumberger Mark Saul, Head of Process Management, SAP

View this session from Reporting & Analytics 2014. Coming to Las Vegas in November! www.reporting2015.com In this session, Newell Rubbermaid guides you through the key elements that comprised its SAP HANA business case and proof of concept, including an emphasis on process improvement. Learn firsthand how Newell Rubbermaid: · Identified which business processes were most likely to realize significant improvement as a result of utilizing SAP HANA · Established a “current state” baseline and demonstrated a “projected state” that could be realized through the use of SAP HANA · Determined which SAP BI tools to use based on specific reporting scenarios and end user requirements

Conheça em detalhes toda a plataforma analítica da SAP e descubra quão poderosa ela pode ser para a gestão do seu negócio

Delivered at PSG College of Technology, Mar 24, 2018 Github - https://github.com/raghu-icecraft/tech-talks/tree/master/Tableau/Mar_18 Basics of BI, Data Visualization. Tableau Features and integration with R. Discussed about Tableau Public and Tableau Desktop. Additions Compared to ICCTAC 2018 session :- Some more emphasis added related to Data Science. Added slides related to Bi and Data science Gartner Magic Quadrant of year 2018. A slide dedicated to foremost Principles of Data Visualization; a note Edward Tufte and Gestalt laws. Audience are MSc Data Science students along with other Teaching Staff. Workshop happened in PSG College of Technology, Coimbatore (Department of MCA).

The document summarizes the new features and enhancements in Cartegraph's Fall 2018 release. Key additions include the ability to lock down the system and notify users, new record and dashboard gadget types for analysis, scenario budget categories to apply activities to specific budgets, and performance improvements to the Scenario Builder. The release also features updates to the user interface, support for newer operating systems, and an improved online campus.

Key Takeaways from this presentation include: - How data is used to run day to day operations - How data is used to influence product decisions and marketing strategies - Which skills are necessary to become self-serving in data tasks regardless of core responsibilities

Building an immersive Data Function in Large Scale Organizations. Data is hard, analytics is hard. Many challenges in both fields have been mastered, but many more lie ahead. One of them is how to establish the combination of both data and analytics as a company function in a large organization. In this talk, I shared insights from the ongoing journey to build a data function at Mercedes-Benz Cars Finance and to embed it into the company’s innermost workings.

The Briefing Room with Dr. Robin Bloor and Actian Live Webcast August 6, 2013 http://www.insideanalysis.com With all the innovations in compute power these days, one of the hardest hurdles to overcome is the tendency to think in old ways. By and large, the processing constraints of yesterday no longer apply. The new constraints revolve around the strategic management of data, and the effective use of business analytics. How can your organization take the helm in this new era of analysis? Register for this episode of The Briefing Room to find out! Veteran Analyst Wayne Eckerson of The BI Leadership Forum, will explain how a handful of key innovations has significantly changed the game for data processing and analytics. He'll be briefed by John Santaferraro of Actian, who will tout his company's unique position in "scale-up and scale-out" for analyzing data.

This document provides an overview of Visual Analytics Session 3. It discusses data joining and blending in Tableau. Specifically, it explains why joining or blending data is necessary when data comes from multiple sources. It then describes the different types of data joins in Tableau - inner joins, left joins, right joins, and outer joins. An example is provided to demonstrate an inner join using a primary key to connect related data between two tables. The goal is to understand how to connect different but related data sources in Tableau using common keys or variables.

This document provides an overview of a visual analytics course using Tableau. The course structure includes modules on Tableau Desktop foundations, exploratory data analysis, creating analytics dashboards, advanced predictive analytics with R programming, and completing visual analytics projects. Learners will gain skills in exploratory data analytics, building dashboards, predictive analytics, and developing a project portfolio. Case studies will cover topics like geo-data analysis, customer segmentation, and price index analysis. The importance of visual analytics and Tableau for industries like sales, marketing, and finance is also discussed.

http://bit.ly/askSAP_BigData_Insight - Moderated by SAP Mentor Tammy Powlas, you’ll hear from SAP experts Ty Miller, Angela Harvey, and Tammy Powlas on our BI product strategy for both Enterprise BI with SAP BusinessObjects BI 4.2 and Trusted Data Discovery with SAP Lumira. We’ll also have guest speaker Justin Sears from Hortonworks, who will share how companies can derive new insights thanks to the powerful combination of Hortonworks Data Platform and SAP Lumira. Other topics of discussion include: - Our latest roadmap on dashboarding innovations, including the 1.6 release of SAP Design Studio. - A deep-dive on how new data-wrangling capabilities in SAP Lumira bring the power of Big Data to Trusted Data Discovery, and a real-world demo of this new functionality. - See how customers are leveraging SAP Lumira and Hortonworks for big data analytics Learn more at SAPBI.com

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LSTM algorithm

LLM powered contract compliance application which uses Advanced RAG method Self-RAG and Knowledge Graph together for the first time. It provides highest accuracy for contract compliance recorded so far for Oil and Gas Industry.

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Aurora PostgreSQL에서 가장 일반적인 performance use case 들에 대해 Aurora PostreSQL의 모니터링 Tool들을 통해 어떤게 문제를 식별하고 분석하는지 그리고 이 문제를 해결해나가는 절차와 방법에 대한 Deep Dive입니다.

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Amazon Aurora 클러스터를 초당 수백만 건의 쓰기 트랜잭션으로 확장하고 페타바이트 규모의 데이터를 관리할 수 있으며, 사용자 지정 애플리케이션 로직을 생성하거나 여러 데이터베이스를 관리할 필요 없이 Aurora에서 관계형 데이터베이스 워크로드를 단일 Aurora 라이터 인스턴스의 한도 이상으로 확장할 수 있는 Amazon Aurora Limitless Database를 소개합니다.

Los sistemas distribuidos son difíciles. Los sistemas distribuidos de alto rendimiento, más. Latencias de red, mensajes sin confirmación de recibo, reinicios de servidores, fallos de hardware, bugs en el software, releases problemáticas, timeouts... hay un montón de motivos por los que es muy difícil saber si un mensaje que has enviado se ha recibido y procesado correctamente en destino. Así que para asegurar mandas el mensaje otra vez.. y otra... y cruzas los dedos para que el sistema del otro lado tenga tolerancia a los duplicados. QuestDB es una base de datos open source diseñada para alto rendimiento. Nos queríamos asegurar de poder ofrecer garantías de "exactly once", deduplicando mensajes en tiempo de ingestión. En esta charla, te cuento cómo diseñamos e implementamos la palabra clave DEDUP en QuestDB, permitiendo deduplicar y además permitiendo Upserts en datos en tiempo real, añadiendo solo un 8% de tiempo de proceso, incluso en flujos con millones de inserciones por segundo. Además, explicaré nuestra arquitectura de log de escrituras (WAL) paralelo y multithread. Por supuesto, todo esto te lo cuento con demos, para que veas cómo funciona en la práctica.

Amazon DocumentDB(MongoDB와 호환됨)는 빠르고 안정적이며 완전 관리형 데이터베이스 서비스입니다. Amazon DocumentDB를 사용하면 클라우드에서 MongoDB 호환 데이터베이스를 쉽게 설치, 운영 및 규모를 조정할 수 있습니다. Amazon DocumentDB를 사용하면 MongoDB에서 사용하는 것과 동일한 애플리케이션 코드를 실행하고 동일한 드라이버와 도구를 사용하는 것을 실습합니다.

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