SF Big Analytics 2019-06-12: Managing uber's data workflows at scale
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Talk 2. Managing Uber’s Data workflow at Scale. Uber microservices serving millions of rides a day, leading to 100+ PB of data. To democratize data pipelines, Uber needed a central tool that provides a way to author, manage, schedule, and deploy data workflows at scale. This talk details Uber’s journey toward a unified and scalable data workflow system used to manage this data and shares the challenges faced and how the company has rearchitected several components of the system—such as scheduling and serialization—to make them highly available and more scalable. Speaker Alex Kira (Uber) Alex Kira is an engineering tech lead at Uber, where he works on the data workflow management team. His team provides a data infrastructure platform. In 19-year, he’s had experience across several software disciplines, including distributed systems, data infrastructure, and full stack development.
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https://xtremej.dev/2023/schedule/
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Overview of the problem, the application (code walkthru and running), overview of FLaNK, introduction to NiFi, introduction to Kafka, and introduction to Flink.
Using Generative AI and Content Service Platforms together
Using Generative AI and Content Service Platforms togetherAngel Borroy López Slides for FOSDEM 2024 session: https://fosdem.org/2024/schedule/event/fosdem-2024-1858-using-generative-ai-and-content-service-platforms-together/
Describes a framework that provides GenAI operations for documents using a REST API. LLMs are stored locally, so no data is sent away.
It also includes a sample integration with a Content Service Platform (Alfresco), to enhance documents and pictures context information.
Session recording is available in https://ftp.fau.de/fosdem/2024/h2213/fosdem-2024-1858-using-generative-ai-and-content-service-platforms-together.av1.webm
Apache NiFi + Tensorflow + Hadoop： Big Data AI サンドイッチの作り方
Apache NiFi + Tensorflow + Hadoop： Big Data AI サンドイッチの作り方HortonworksJapan This document discusses using Apache NiFi, TensorFlow, and Hadoop together for big data AI applications. It provides an overview of each technology and how they can be combined. Specifically, it describes how NiFi can be used to ingest and transport data to Hadoop, TensorFlow can be used to build and deploy machine learning models for real-time insights, and both can leverage Hadoop for storage and processing of historical data. Examples of building basic and professional "AI sandwiches" by layering these components are also presented.
Python Software Engineer, AI at Upstaff
Python Software Engineer, AI at UpstaffUpstaff - 5 years of experience in the IT industry as a Python Engineer- Proficient in using technologies such as Django DRF, Flask, Pandas, BeautifulSoup, SQLAlchemy, Asyncio, Flask + Marshmallow, Apscheduler, Jinja, Quip API, Docker, Nginx, Amazon MWS API, Amazon SP-API, Google API, and Telegram API;
- Strong in refactoring, bug-fixing, and working with Python, JavaScript, Django, Django Rest Framework;
- Skilled in developing faceted search and matching algorithms using ElasticSearch;
- Experienced in PDF generation and importing from various formats;
- Developed web applications, API interfaces, and automated scripts for data transformation;
- Previous experience includes working as a Python developer focused on data scraping for Amazon, eBay, and Walmart products in dropshipping projects;
- Also worked as a C# developer on various projects, specializing in web development, scraping, and parsing data;
- Upper-Intermediate English;
- Available ASAP
B7 api management_enabling_digital_transformation
B7 api management_enabling_digital_transformationDr. Wilfred Lin (Ph.D.) The document discusses Oracle's API management platform. It enables digital transformation by allowing organizations to manage, secure, and monetize their APIs. The platform provides tools to build and deploy APIs, secure access through authentication and authorization, monitor API usage through analytics, and publish APIs to developers through a portal. It helps organizations improve agility, gain visibility into API usage, and ensure security of digital assets.
SAP & Open Souce - Give & Take
SAP & Open Souce - Give & TakeJan Penninkhof This document discusses the use of open source tools in software development. It outlines the typical toolstacks used, including programming languages, databases, and frameworks. It notes that open source tools have accelerated startup development by reducing costs and allowing developers greater flexibility in choosing technologies. The document also discusses SAP's embrace of open source, both in powering their own products and services with open source software, as well as contributing their own open source projects.
Overview of Apache Fink: the 4 G of Big Data Analytics Frameworks
Overview of Apache Fink: the 4 G of Big Data Analytics FrameworksSlim Baltagi Slides of my talk at the Hadoop Summit Europe in Dublin, Ireland on April 13th, 2016. The talk introduces Apache Flink as both a multi-purpose Big Data analytics framework and real-world streaming analytics framework. It is focusing on Flink's key differentiators and suitability for streaming analytics use cases. It also shows how Flink enables novel use cases such as distributed CEP (Complex Event Processing) and querying the state by behaving like a key value data store.
Overview of Apache Flink: the 4G of Big Data Analytics Frameworks
Overview of Apache Flink: the 4G of Big Data Analytics FrameworksDataWorks Summit/Hadoop Summit This document provides an overview of Apache Flink and discusses why it is suitable for real-world streaming analytics. The document contains an agenda that covers how Flink is a multi-purpose big data analytics framework, why streaming analytics are emerging, why Flink is suitable for real-world streaming analytics, novel use cases enabled by Flink, who is using Flink, and where to go from here. Key points include Flink innovations like custom memory management, its DataSet API, rich windowing semantics, and native iterative processing. Flink's streaming features that make it suitable for real-world use include its pipelined processing engine, stream abstraction, performance, windowing support, fault tolerance, and integration with Hadoop.
Overview of Apache Fink: The 4G of Big Data Analytics Frameworks
Overview of Apache Fink: The 4G of Big Data Analytics FrameworksSlim Baltagi This document provides an overview of Apache Flink and discusses why it is suitable for real-world streaming analytics. The document contains an agenda that covers how Flink is a multi-purpose big data analytics framework, why streaming analytics are emerging, why Flink is suitable for real-world streaming analytics, novel use cases enabled by Flink, who is using Flink, and where to go from here. Key points include Flink innovations like custom memory management, its DataSet API, rich windowing semantics, and native iterative processing. Flink's streaming features that make it suitable for real-world use include its pipelined processing engine, stream abstraction, performance, windowing support, fault tolerance, and integration with Hadoop.
Deep learning on HDP 2018 Prague
Deep learning on HDP 2018 PragueTimothy Spann This document provides an agenda and overview for a presentation on deep learning on Hortonworks Data Platform (HDP). The presentation will cover using TensorFlow with Apache NiFi, running TensorFlow on YARN, using pre-built models with Apache MXNet, running an MXNet model server with NiFi, and running MXNet in Zeppelin notebooks and on YARN. It recommends installing CPU and GPU versions of frameworks on appropriate nodes and discusses options like TensorFlow, MXNet, and PyTorch. The document also outlines integrating Apache MXNet with NiFi for tasks like image classification using models on edge nodes or a model server.
AIDEVDAY_ Data-in-Motion to Supercharge AI
AIDEVDAY_ Data-in-Motion to Supercharge AITimothy Spann AIDEVDAY_ Data-in-Motion to Supercharge AI
https://www.meetup.com/futureofdata-newyork/events/295376737/
Lightning Talk 2: Data-in-Motion to Supercharge AI
Speaker: Timothy Spann @Cloudera
Abstract: A quick look at the current state of real-time streaming for powering both data ingest and transformation to provide training and enhancement data to models. Also how to use streaming to feed a pipeline of data against your models or models hosted a HuggingFace or elsewhere.
https://huggingface.co/bigscience/bloom
https://www.aicamp.ai/event/eventdetails/W2023082314
Apache NiFi, Apache Kafka, Apache Flink, HuggingFace, WatsonX.AI, REST API, Cloudera Machine Learning (CML), Bloom, Deep Learning, AI
2024 Feb AI Meetup NYC GenAI_LLMs_ML_Data Codeless Generative AI Pipelines
2024 Feb AI Meetup NYC GenAI_LLMs_ML_Data Codeless Generative AI PipelinesTimothy Spann 2024 Feb AI Meetup NYC GenAI_LLMs_ML_Data Codeless Generative AI Pipelines
https://www.aicamp.ai/event/eventdetails/W2024022214
apache nifi
llm
generative ai
gen ai
ml
dl
machine learning
apache kafka
apache flink
postgresql
python
AI Meetup (NYC): GenAI, LLMs, ML and Data
Feb 22, 05:30 PM EST
Welcome to the monthly in-person AI meetup in New York City, in collaboration with Microsoft. Join us for deep dive tech talks on AI, GenAI, LLMs and machine learning, food/drink, networking with speakers and fellow developers
Agenda:
* 5:30pm~6:00pm: Checkin, Food/drink and networking
* 6:00pm~6:10pm: Welcome/community update
* 6:10pm~8:30pm: Tech talks
* 8:30pm: Q&A, Open discussion
Tech Talk: Searching and Reasoning Over Multimedia Data with Vector Databases and LMMs
Speaker: Zain Hasan (Weaviate LinkedIn)
Abstract: In this talk, Zain Hasan will discuss how we can use open-source multimodal embedding models in conjunction with large generative multimodal models that can that can see, hear, read, and feel data(!), to perform cross-modal search(searching audio with images, videos with text etc.) and multimodal retrieval augmented generation (MM-RAG) at the billion-object scale with the help of open source vector databases. I will also demonstrate, with live code demos, how being able to perform this cross-modal retrieval in real-time can enables users to use LLMs that can reason over their enterprise multimodal data. This talk will revolve around how we can scale the usage of multimodal embedding and generative models in production.
Tech Talk: Codeless Generative AI Pipelines
Speaker: Timothy Spann (Cloudera LinkedIn)
Abstract: Join us for an insightful talk on leveraging the power of real-time streaming tools, specifically Apache NiFi, to revolutionize GenAI data engineering. In this session, we’ll explore how the integration of Apache NiFi can automate the entire process of prompt building, making it a seamless and efficient task.
Speakers/Topics:
Stay tuned as we are updating speakers and schedules. If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: Submit Topics
Sponsors:
We are actively seeking sponsors to support our community. Whether it is by offering venue spaces, providing food/drink, or cash sponsorship. Sponsors will have the chance to speak at the meetups, receive prominent recognition, and gain exposure to our extensive membership base of 20,000+ local or 300K+ developers worldwide.
Venue:
Microsoft NYC - Times Square, 11 Times Square, New York, NY 10036
Room Name: Central Park West 6501
Community on Slack/Discord
- Event chat: chat and connect with speakers and attendees
- Sharing blogs, events, job openings, projects collaborations
Join Slack (search and join the #newyork channel) | Join Discord
apache solr web development.pdf
apache solr web development.pdfTasnim Jahan Welcome to
How can I develop for Apache Solr in 2023?
The capacity to search is a core element of most modern systems. They must incorporate enormous amounts of data while yet allowing the end user to get what they're looking for quickly. DevOps must go beyond conventional databases with difficult and unintuitive (even if brilliant and imaginative) SQL query-based solutions in order to integrate search functions.
A free, open-source search engine built on the Apache Lucene architecture is Searching On Lucene with Replication (Apache Solr). One of the most widely used search engines nowadays, it has been available since 2004. It is a part of the Apache Lucene project. Contrarily, Solr is more than just a search engine; it's also frequently utilized as a key-value store and a document-based NoSQL database with transactional capabilities.
What is the development scope of Apache Solr?
Open-source search platform Solr can be used to make search applications. It was built on top of the full-text search engine Lucene. A quick, scalable, and enterprise-ready search engine is Solr. Applications built with Solr are intelligent and perform very well.In order to enhance the search functionality of CNET Networks' corporate website, Yonik Seely created Solr in 2004. In January 2006, it was accepted as an open-source undertaking by the Apache Software Foundation. The most recent version, Solr 6.0, includes capability for parallel SQL query execution and was released in 2016. Solr and Hadoop might collaborate. Since Hadoop manages a large number of data, Solr helps us find the crucial information from such a vast source.
What functions and duties do developers for Apache Solr perform?
Apache Solr developers collaborate with a group of talented engineers to design and build the next iteration of a company's mobile apps. Other technical and app development teams work closely with the developers to generate the product.
A developer's main responsibilities after securing remote Apache Solr developer employment are as follows:
Develop, keep up with, and enhance new search functionality for the program.
Open-source search APIs and SDKs should be created, improved, and maintained.
Develop and keep up strong query rewriting capabilities.
Make unit test cases for the Solr search engine that are automated.
Design, develop, assess, and test the Solr search engine in collaboration with cross-functional teams.
How to become an Apache Solr developer?
Let's examine the procedures for training to become an Apache Solr developer. To start, keep in mind that no academic degree is necessary to work as an Apache Solr developer. You can learn Apache Solr programming and use it as a vocation, whether you have a degree or not, are smart or inexperienced. All that is needed is real-world experience and a grasp of the necessary non-technical and technical skills.
However, you may have heard that roles for remote Apache Solr developers call for a bachelor's or master's degree in
使用 Spring boot 快速打造 hateoas 應用
使用 Spring boot 快速打造 hateoas 應用Shin-Jan Wu This document discusses using Spring Boot to quickly build HATEOAS applications. It introduces HATEOAS and its implementation in Java using Spring Data REST and HAL. It then discusses using Spring Boot to create RESTful APIs and addresses common questions about preventing certain HTTP methods from being exported and how to address security issues using OAuth 2.
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More from Chester Chen (20)
SFBigAnalytics_SparkRapid_20220622.pdf
SFBigAnalytics_SparkRapid_20220622.pdfChester Chen This document summarizes a presentation about accelerating Apache Spark workloads using NVIDIA's RAPIDS accelerator. It notes that global data generation is expected to grow exponentially to 221 zettabytes by 2026. RAPIDS can provide significant speedups and cost savings for Spark workloads by leveraging GPUs. Benchmark results show a NVIDIA decision support benchmark running 5.7x faster and with 4.5x lower costs on a GPU cluster compared to CPU. The document outlines RAPIDS integration with Spark and provides information on qualification, configuration, and future developments.
zookeeer+raft-2.pdf
zookeeer+raft-2.pdfChester Chen Talk at SF Big Analytics https://www.meetup.com/sf-big-analytics/events/285731741/
Distributed systems are made up of many components such as authentication, a persistence layer, stateless services, load balancers, and stateful coordination services. These coordination services are central to the operation of the system, performing tasks such as maintaining system configuration state, ensuring service availability, name resolution, and storing other system metadata. Given their central role in the system it is essential that these systems remain available, fault tolerant and consistent. By providing a highly available file system-like abstraction as well as powerful recipes such as leader election, Apache Zookeeper is often used to implement these services. Although powerful, the Zookeeper interface may not be flexible enough or provide sufficient performance for all applications and many systems are replacing Zookeeper based solutions with Raft which provides a more generic interface to high availability and fault tolerance through the use of State Machine replication. This talk will go over a generic example of stateful coordination service moving from Zookeeper to Raft.
Speaker: Tyler Crain ( Alluxio)
Tyler Crain is a software engineer at Alluxio, working on distributed systems within the Alluxio core team. Before this, Tyler held Post-Doc positions at the University of Sydney and Sorbonne Universities where he performed research on topics including distributed key-value stores, distributed consensus and blockchain. Tyler received his PhD from the University of Rennes where he worked on Transactional Memory. He also holds a Masters degree in Computer Science from University of California Santa Barbara.
talk at SF Big Analytics:
Related Blog: https://www.alluxio.io/blog/from-zookeeper-to-raft-how-alluxio-stores-file-system-state-with-high-availability-and-fault-tolerance/
SF Big Analytics 2022-03-15: Persia: Scaling DL Based Recommenders up to 100 ...
SF Big Analytics 2022-03-15: Persia: Scaling DL Based Recommenders up to 100 ...Chester Chen Recent years have witnessed an exponential growth of the model scale in recommendation/Ads/search—from Google’s 2016 model with 1 billion parameters to the latest Facebook’s model with 12 trillion parameters. Significant quality boost has come with each jump of the model capacity, which makes people believe the era of 100 trillion parameters is around the corner. To prepare the exponential growth of the model size, an efficient distributed training system is in urgent need. However, the training of such huge models is challenging even within industrial scale data centers. In this talk, I will introduce Persia -- an open training system developed by my team -- to resolve this challenge by careful co-design of both the optimization algorithm and the distributed system architecture. Persia admits nearly linear speedup properties while scaling the number of workers and the model size. Beside the capability of training 100 trillion parameters, it also shows a clear advantage in efficiency over other open sourced engines.
paper link:
https://arxiv.org/pdf/2111.05897.pdf
Speaker: Ji Liu
Dr. Ji Liu received his Ph.D in computer science and his bachelor degree in automation from University of Wisconsin-Madison and University of Science and Technology of China, respectively. After graduation, he joined the University of Rochester as an assistant professor, conducting research in machine learning, optimization, and reinforcement learning. The developed asynchronous and decentralized algorithms were widely used in industry, such as IBM, Microsoft, etc. He left academia and joined Tencent in 2017, exploring AI’s boundary. The developing AI agent Tstarbot was considered to be a milestone for mastering the most challenging RTS game -- Starcraft II. His second stop in industry is Kwai - the second largest short video company in China. He founded and led multiple international teams with different functionalities: platform team, product team, and research team. His team Contributed to 15+% annual revenue growth in Ads. He published 100+ papers in top-tier CS conferences and journals, and received multiple best paper awards (e.g., SIGKDD 2010 and UAI 2015 Facebook best paper). He was an awardee of MIT TR 35 under 35 in China and IBM faculty award in 2017. He was nominated to be one of China top 5 AI innovators under 35 in 2018
SF Big Analytics talk: NVIDIA FLARE: Federated Learning Application Runtime E...
SF Big Analytics talk: NVIDIA FLARE: Federated Learning Application Runtime E...Chester Chen Topic:
NVIDIA FLARE: Federated Learning Application Runtime Environment for Developing Robust AI Models
Summary:
Federated learning (FL) enables building robust and generalizable AI models by leveraging diverse datasets from multiple collaborators without moving data. We created NVIDIA FLARE as an open-source SDK to make it easier for data scientists to use FL in their research. The SDK allows existing machine learning and deep learning workflows adapted for distributed learning across enterprises and enables platform developers to build a secure, privacy-preserving offering for multiparty collaboration utilizing homomorphic encryption or differential privacy. The SDK is a lightweight, flexible, and scalable Python package and allows researchers to bring their data science workflows implemented in any training libraries (PyTorch, TensorFlow, or even NumPy), and apply them in real-world FL settings. This talk will introduce the key design principles of NVIDIA FLARE and illustrate use cases (e.g., COVID analysis) with customizable FL workflows that implement different privacy-preserving algorithms.
Speaker: Dr. Holger Roth ( Nvidia)
Holger Roth is a Sr. Applied Research Scientist at NVIDIA focusing on deep learning for medical imaging. He has been working closely with clinicians and academics over the past several years to develop deep learning based medical image computing and computer-aided detection models for radiological applications. He is an Associate Editor for IEEE Transactions of Medical Imaging and holds a Ph.D. from University College London, UK. In 2018, he was awarded the MICCAI Young Scientist Publication Impact Award.
A missing link in the ML infrastructure stack?
A missing link in the ML infrastructure stack?Chester Chen Talk at SF Big Analytics
Machine learning is quickly becoming a product engineering discipline. Although several new categories of infrastructure and tools have emerged to help teams turn their models into production systems, doing so is still extremely challenging for most companies. In this talk, we survey the tooling landscape and point out several parts of the machine learning lifecycle that are still underserved. We propose a new category of tool that could help alleviate these challenges and connect the fragmented production ML tooling ecosystem. We conclude by discussing similarities and differences between our proposed system and those of a few top companies.
Bio: Josh Tobin is the founder and CEO of a stealth machine learning startup. Previously, Josh worked as a deep learning & robotics researcher at OpenAI and as a management consultant at McKinsey. He is also the creator of Full Stack Deep Learning (fullstackdeeplearning.com), the first course focused on the emerging engineering discipline of production machine learning. Josh did his PhD in Computer Science at UC Berkeley advised by Pieter Abbeel.
Shopify datadiscoverysf bigdata
Shopify datadiscoverysf bigdataChester Chen This document discusses the challenges of data discovery and management from the perspectives of frustrated data scientists and project managers. It explores three main problems with obtaining and working with data. While buying a solution was considered, there were also risks to mitigate. The document asks about the biggest flaw of Artifact and what is next for the company. It concludes by thanking the reader.
SF Big Analytics 20191112: How to performance-tune Spark applications in larg...
SF Big Analytics 20191112: How to performance-tune Spark applications in larg...Chester Chen Uber developed an new Spark ingestion system, Marmaray, for data ingestion from various sources. It’s designed to ingest billions of Kafka messages every 30 minutes. The amount of data handled by the pipeline is of the order hundreds of TBs. Omar details how to tackle such scale and insights into the optimizations techniques. Some key highlights are how to understand bottlenecks in Spark applications, to cache or not to cache your Spark DAG to avoid rereading your input data, how to effectively use accumulators to avoid unnecessary Spark actions, how to inspect your heap and nonheap memory usage across hundreds of executors, how you can change the layout of data to save long-term storage cost, how to effectively use serializers and compression to save network and disk traffic, and how to reduce amortize the cost of your application by multiplexing your jobs, different techniques for reducing memory footprint, runtime, and on-disk usage. CGI was able to significantly (~10%–40%) reduce memory footprint, runtime, and disk usage.
Speaker: Omkar Joshi (Uber)
Omkar Joshi is a senior software engineer on Uber’s Hadoop platform team, where he’s architecting Marmaray. Previously, he led object store and NFS solutions at Hedvig and was an initial contributor to Hadoop’s YARN scheduler.
SF Big Analytics 2019112: Uncovering performance regressions in the TCP SACK...
SF Big Analytics 2019112: Uncovering performance regressions in the TCP SACK...Chester Chen
Uncovering performance regressions in the TCP SACKs vulnerability fixes
In early July 2019, Databricks noticed some Apache Spark workloads regressing by as much as 6x. In this talk, we'll discuss how we traced these regressions back to the Linux kernel and the fixes for the TCP SACKs vulnerabilities. We will explain the symptoms we were seeing, walk through how we debugged the TCP connections, and dive into the Linux source to uncover the root cause.
Speaker: Chris Stevens (Databricks)
Chris Stevens is a software engineer at Databricks where he works on the reliability, scalability, and security of Apache Spark clusters. His work focuses on auto-scaling compute, auto-scaling storage, node initialization performance, and node health monitoring. Prior to Databricks, Chris founded the Minoca OS project, where he built a POSIX compliant, general purpose OS - from scratch - to run on resource constrained device. He got his start at Microsoft working on the Windows kernel team, porting the Windows boot environment from BIOS to UEFI.
SFBigAnalytics_20190724: Monitor kafka like a Pro
SFBigAnalytics_20190724: Monitor kafka like a ProChester Chen Kafka operators need to provide guarantees to the business that Kafka is working properly and delivering data in real time, and they need to identify and triage problems so they can solve them before end users notice them. This elevates the importance of Kafka monitoring from a nice-to-have to an operational necessity. In this talk, Kafka operations experts Xavier Léauté and Gwen Shapira share their best practices for monitoring Kafka and the streams of events flowing through it. How to detect duplicates, catch buggy clients, and triage performance issues – in short, how to keep the business’s central nervous system healthy and humming along, all like a Kafka pro.
Speakers: Gwen Shapira, Xavier Leaute (Confluence)
Gwen is a software engineer at Confluent working on core Apache Kafka. She has 15 years of experience working with code and customers to build scalable data architectures. She currently specializes in building real-time reliable data processing pipelines using Apache Kafka. Gwen is an author of “Kafka - the Definitive Guide”, "Hadoop Application Architectures", and a frequent presenter at industry conferences. Gwen is also a committer on the Apache Kafka and Apache Sqoop projects.
Xavier Leaute is One of the first engineers to Confluent team, Xavier is responsible for analytics infrastructure, including real-time analytics in KafkaStreams. He was previously a quantitative researcher at BlackRock. Prior to that, he held various research and analytics roles at Barclays Global Investors and MSCI.
SF Big Analytics 20190612: Building highly efficient data lakes using Apache ...
SF Big Analytics 20190612: Building highly efficient data lakes using Apache ...Chester Chen Building highly efficient data lakes using Apache Hudi (Incubating)
Even with the exponential growth in data volumes, ingesting/storing/managing big data remains unstandardized & in-efficient. Data lakes are a common architectural pattern to organize big data and democratize access to the organization. In this talk, we will discuss different aspects of building honest data lake architectures, pin pointing technical challenges and areas of inefficiency. We will then re-architect the data lake using Apache Hudi (Incubating), which provides streaming primitives right on top of big data. We will show how upserts & incremental change streams provided by Hudi help optimize data ingestion and ETL processing. Further, Apache Hudi manages growth, sizes files of the resulting data lake using purely open-source file formats, also providing for optimized query performance & file system listing. We will also provide hands-on tools and guides for trying this out on your own data lake.
Speaker: Vinoth Chandar (Uber)
Vinoth is Technical Lead at Uber Data Infrastructure Team
SF Big Analytics_20190612: Scaling Apache Spark on Kubernetes at Lyft
SF Big Analytics_20190612: Scaling Apache Spark on Kubernetes at LyftChester Chen Talk 1. Scaling Apache Spark on Kubernetes at Lyft
As part of this mission Lyft invests heavily in open source infrastructure and tooling. At Lyft Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark at Lyft has evolved to solve both Machine Learning and large scale ETL workloads. By combining the flexibility of Kubernetes with the data processing power of Apache Spark, Lyft is able to drive ETL data processing to a different level. In this talk, We will talk about challenges the Lyft team faced and solutions they developed to support Apache Spark on Kubernetes in production and at scale. Topics Include: - Key traits of Apache Spark on Kubernetes. - Deep dive into Lyft's multi-cluster setup and operationality to handle petabytes of production data. - How Lyft extends and enhances Apache Spark to support capabilities such as Spark pod life cycle metrics and state management, resource prioritization, and queuing and throttling. - Dynamic job scale estimation and runtime dynamic job configuration. - How Lyft powers internal Data Scientists, Business Analysts, and Data Engineers via a multi-cluster setup.
Speaker: Li Gao
Li Gao is the tech lead in the cloud native spark compute initiative at Lyft. Prior to Lyft, Li worked at Salesforce, Fitbit, Marin Software, and a few startups etc. on various technical leadership positions on cloud native and hybrid cloud data platforms at scale. Besides Spark, Li has scaled and productionized other open source projects, such as Presto, Apache HBase, Apache Phoenix, Apache Kafka, Apache Airflow, Apache Hive, and Apache Cassandra.
SFBigAnalytics- hybrid data management using cdap
SFBigAnalytics- hybrid data management using cdapChester Chen Cloud has emerged as a critical enabler of digital transformation, with the aim of reducing IT overheads and costs. However, cloud
migration is not instantaneous for a variety of reasons including data sensitivity, compliance and application performance. This results in the creation of diverse hybrid and multi-cloud environments and amplifies data management and integration challenges. This talk demonstrates how CDAP’s flexibility can allow you to utilize your existing on-premises infrastructure, as you evolve to the latest Big Data and Cloud services at your own pace, all while providing you a single, unified view of all your data, wherever it resides.
Speaker: Bhooshan Mogal, Google
Bhooshan Mogal is a Product Manager at Google, where he is focused on delivering best-in-class Data and Analytics services to GCP users. Prior to Google, he worked on data systems at Cask Data Inc, Pivotal and Yahoo.
Sf big analytics: bighead
Sf big analytics: bigheadChester Chen Bighead: Airbnb's end-to-end machine learning platform
Airbnb has a wide variety of ML problems ranging from models on traditional structured data to models built on unstructured data such as user reviews, messages and listing images. The ability to build, iterate on, and maintain healthy machine learning models is critical to Airbnb’s success. Bighead aims to tie together various open source and in-house projects to remove incidental complexity from ML workflows. Bighead is built on Python, Spark, and Kubernetes. The components include a lifecycle management service, an offline training and inference engine, an online inference service, a prototyping environment, and a Docker image customization tool. Each component can be used individually. In addition, Bighead includes a unified model building API that smoothly integrates popular libraries including TensorFlow, XGBoost, and PyTorch. Each model is reproducible and iterable through standardization of data collection and transformation, model training environments, and production deployment. This talk covers the architecture, the problems that each individual component and the overall system aims to solve, and a vision for the future of machine learning infrastructure. It’s widely adopted in Airbnb and we have variety of models running in production. We plan to open source Bighead to allow the wider community to benefit from our work.
Speaker: Andrew Hoh
Andrew Hoh is the Product Manager for the ML Infrastructure and Applied ML teams at Airbnb. Previously, he has spent time building and growing Microsoft Azure's NoSQL distributed database. He holds a degree in computer science from Dartmouth College.
Sf big analytics_2018_04_18: Evolution of the GoPro's data platform
Sf big analytics_2018_04_18: Evolution of the GoPro's data platformChester Chen Talk 1 : Evolution of the GoPro's data platform
In this talk, we will share GoPro’s experiences in building Data Analytics Cluster in Cloud. We will discuss: evolution of data platform from fixed-size Hadoop clusters to Cloud-based Spark Cluster with Centralized Hive Metastore +S3: Cost Benefits and DevOp Impact; Configurable, spark-based batch Ingestion/ETL framework;
Migration Streaming framework to Cloud + S3;
Analytics metrics delivery with Slack integration;
BedRock: Data Platform Management, Visualization & Self-Service Portal
Visualizing Machine learning Features via Google Facets + Spark
Speakers: Chester Chen
Chester Chen is the Head of Data Science & Engineering, GoPro. Previously, he was the Director of Engineering at Alpine Data Lab.
David Winters
David is an Architect in the Data Science and Engineering team at GoPro and the creator of their Spark-Kafka data ingestion pipeline. Previously He worked at Apple & Splice Machines.
Hao Zou
Hao is a Senior big data engineer at Data Science and Engineering team. Previously He worked as Alpine Data Labs and Pivotal
Analytics Metrics delivery and ML Feature visualization: Evolution of Data Pl...
Analytics Metrics delivery and ML Feature visualization: Evolution of Data Pl...Chester Chen GoPro’s camera, drone, mobile devices as well as web, desktop applications are generating billions of event logs. The analytics metrics and insights that inform product, engineering, and marketing team decisions need to be distributed quickly and efficiently. We need to visualize the metrics to find the trends or anomalies.
While trying to building up the features store for machine learning, we need to visualize the features, Google Facets is an excellent project for visualizing features. But can we visualize larger feature dataset?
These are issues we encounter at GoPro as part of the data platform evolution. In this talk, we will discuss few of the progress we made at GoPro. We will talk about how to use Slack + Plot.ly to delivery analytics metrics and visualization. And we will also discuss our work to visualize large feature set using Google Facets with Apache Spark.
2018 data warehouse features in spark
2018 data warehouse features in sparkChester Chen Spark can be enhanced with data warehouse capabilities to leverage both open source analytics and enterprise data warehouse strengths. This includes incorporating star schema detection and referential integrity constraints to optimize queries. Performance can be improved by pushing down operations like joins, filters, and projections from Spark to underlying data sources using heuristics like star schema patterns. Push downs allow exploiting database indexes and reducing data transfer. Star schema detection and join push downs have shown speedups of 2-31x on TPC-DS benchmark queries.
2018 02-08-what's-new-in-apache-spark-2.3 
2018 02-08-what's-new-in-apache-spark-2.3 Chester Chen This document summarizes new features in Apache Spark 2.3, including continuous processing mode for structured streaming, stream-stream joins, running Spark applications on Kubernetes, improved PySpark performance through vectorized UDFs and Pandas integration, and Databricks Delta for reliability and performance in data lakes. The author, an Apache Spark committer and PMC member, provides overviews and code examples of these features.
2018 02 20-jeg_index
2018 02 20-jeg_indexChester Chen The document discusses building an enterprise/cloud analytics platform using Jupyter notebooks and Apache Spark. It describes the challenges of deploying Jupyter notebooks at an enterprise scale, including collaboration, large-scale data analysis, security, and authentication. It outlines various approaches taken to address these challenges, such as running the entire Jupyter stack on a single large machine or giving each user their own container. However, these approaches have limitations. The document then introduces the Jupyter Enterprise Gateway as a solution developed by IBM to optimize resource allocation, support multi-users securely through impersonation, and enhance security overall when deploying Jupyter at an enterprise scale.
Index conf sparkml-feb20-n-pentreath
Index conf sparkml-feb20-n-pentreathChester Chen The document summarizes new features and improvements in Apache Spark 2.3 for machine learning. Key highlights include first-class support for loading image data, enhanced scalability of feature transformers by supporting multiple columns, parallelizing cross-validation for faster hyperparameter tuning, and a new scalable feature hashing transformer. Performance tests demonstrate that the multi-column transformers provide up to 2.7x speedup over the single-column approach. Parallel cross-validation also provides a 2-2.7x speedup using 3 threads. Future areas of focus include completing multi-column support, improving Python APIs, and enhancing techniques like gradient boosted trees.
Index conf sparkai-feb20-n-pentreath
Index conf sparkai-feb20-n-pentreathChester Chen The document discusses major deep learning frameworks that can be used with Spark, including Deeplearning4J, BigDL, Deep Learning Pipelines, TensorFlowOnSpark, and Microsoft Machine Learning on Spark. It provides brief overviews of each framework's capabilities and support for distributed GPU/CPU training. The document also lists some other frameworks and raises questions about real-world uses of deep learning with Spark, common problems being solved, and challenges integrating Spark with deep learning frameworks.
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