(BDT303) Running Spark and Presto on the Netflix Big Data PlatformAmazon Web Services
In this session, we discuss how Spark and Presto complement the Netflix big data platform stack that started with Hadoop, and the use cases that Spark and Presto address. Also, we discuss how we run Spark and Presto on top of the Amazon EMR infrastructure; specifically, how we use Amazon S3 as our data warehouse and how we leverage Amazon EMR as a generic framework for data-processing cluster management.
Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...Amazon Web Services
Learn how to deploy a managed Presto environment to interactively query log data on AWS
Organizations often need to quickly analyze large amounts of data, such as logs, generated from a wide variety of sources and formats. However, traditional approaches require a lot of time and effort designing complex data transformation and loading processes; and configuring data warehouses. Using AWS, you can start querying your datasets within minutes
In this webinar you will learn how you can deploy a managed Presto environment in minutes to interactively query log data using plain ANSI SQL. Presto is a popular open source SQL engine for running interactive analytic queries against data sources of all sizes. We will talk about common use cases and best practices for running Presto on Amazon EMR.
Learning Objectives:
• Learn how to deploy a managed Presto environment running on Amazon EMR
• Understand best practices for running Presto on Amazon EMR, including use of Amazon EC2 Spot instances
• Learn how other customers are using Presto to analyze large data sets
Building Data Product Based on Apache Spark at Airbnb with Jingwei Lu and Liy...Databricks
Building data product requires having Lambda Architecture to bridge the batch and streaming processing. AirStream is a framework built on top of Apache Spark to allow users to easily build data products at Airbnb. It proved Spark is impactful and useful in the production for mission-critical data products.
On the streaming side, hear how AirStream integrates multiple ecosystems with Spark Streaming, such as HBase, Elasticsearch, MySQL, DynamoDB, Memcache and Redis. On the batch side, learn how to apply the same computation logic in Spark over large data sets from Hive and S3. The speakers will also go through a few production use cases, and share several best practices on how to manage Spark jobs in production.
SF Big Analytics: Machine Learning with Presto by Christopher BernerChester Chen
Talk 1: Machine Learning in Presto
Presto is an open source distributed SQL query engine used by Facebook, in our Hadoop warehouse. It's typically about 10x faster than Hive, and can be extended to a number of other use cases. One of these extensions adds SQL functions to create and make predictions with machine learning models. The aim of this is to significantly reduce the time it takes to prototype a model, by moving the construction and testing of the model to the database.
Bio:
Christopher Berner works as a software engineer at Facebook on the Presto team. He wrote the ML functionality, and has worked on the query planner, type system, bytecode generator, and many other pieces of Presto. Before Presto he worked on the newsfeed ranking team developing machine learning models.
Flink Forward SF 2017: Bill Liu & Haohui Mai - AthenaX : Uber’s streaming pro...Flink Forward
The mission of Uber is to make transportation as reliable as running water. The business is fundamentally driven by real-time data -- more than half of the employees in Uber, many of whom are non-technical, use SQL on a regular basis to analyze data and power their business decisions. We are building AthenaX, a stream processing platform built on top of Apache Flink to enable our users to write SQL to process real-time data efficiently and reliably at Uber's scale. Using Apache Calcite as query parser, AthenaX compiles the SQL down to Flink jobs. Leveraging Flink's unique streaming capabilities, AthenaX supports (1) consistent computations reliably thanks to at-least-once guarantees, (2) nontrivial analytics (e.g., windowing and joins) on multiple data sources, and (3) efficient and cost-effective executions in production through code generation and elastic scaling.
(BDT303) Running Spark and Presto on the Netflix Big Data PlatformAmazon Web Services
In this session, we discuss how Spark and Presto complement the Netflix big data platform stack that started with Hadoop, and the use cases that Spark and Presto address. Also, we discuss how we run Spark and Presto on top of the Amazon EMR infrastructure; specifically, how we use Amazon S3 as our data warehouse and how we leverage Amazon EMR as a generic framework for data-processing cluster management.
Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...Amazon Web Services
Learn how to deploy a managed Presto environment to interactively query log data on AWS
Organizations often need to quickly analyze large amounts of data, such as logs, generated from a wide variety of sources and formats. However, traditional approaches require a lot of time and effort designing complex data transformation and loading processes; and configuring data warehouses. Using AWS, you can start querying your datasets within minutes
In this webinar you will learn how you can deploy a managed Presto environment in minutes to interactively query log data using plain ANSI SQL. Presto is a popular open source SQL engine for running interactive analytic queries against data sources of all sizes. We will talk about common use cases and best practices for running Presto on Amazon EMR.
Learning Objectives:
• Learn how to deploy a managed Presto environment running on Amazon EMR
• Understand best practices for running Presto on Amazon EMR, including use of Amazon EC2 Spot instances
• Learn how other customers are using Presto to analyze large data sets
Building Data Product Based on Apache Spark at Airbnb with Jingwei Lu and Liy...Databricks
Building data product requires having Lambda Architecture to bridge the batch and streaming processing. AirStream is a framework built on top of Apache Spark to allow users to easily build data products at Airbnb. It proved Spark is impactful and useful in the production for mission-critical data products.
On the streaming side, hear how AirStream integrates multiple ecosystems with Spark Streaming, such as HBase, Elasticsearch, MySQL, DynamoDB, Memcache and Redis. On the batch side, learn how to apply the same computation logic in Spark over large data sets from Hive and S3. The speakers will also go through a few production use cases, and share several best practices on how to manage Spark jobs in production.
SF Big Analytics: Machine Learning with Presto by Christopher BernerChester Chen
Talk 1: Machine Learning in Presto
Presto is an open source distributed SQL query engine used by Facebook, in our Hadoop warehouse. It's typically about 10x faster than Hive, and can be extended to a number of other use cases. One of these extensions adds SQL functions to create and make predictions with machine learning models. The aim of this is to significantly reduce the time it takes to prototype a model, by moving the construction and testing of the model to the database.
Bio:
Christopher Berner works as a software engineer at Facebook on the Presto team. He wrote the ML functionality, and has worked on the query planner, type system, bytecode generator, and many other pieces of Presto. Before Presto he worked on the newsfeed ranking team developing machine learning models.
Flink Forward SF 2017: Bill Liu & Haohui Mai - AthenaX : Uber’s streaming pro...Flink Forward
The mission of Uber is to make transportation as reliable as running water. The business is fundamentally driven by real-time data -- more than half of the employees in Uber, many of whom are non-technical, use SQL on a regular basis to analyze data and power their business decisions. We are building AthenaX, a stream processing platform built on top of Apache Flink to enable our users to write SQL to process real-time data efficiently and reliably at Uber's scale. Using Apache Calcite as query parser, AthenaX compiles the SQL down to Flink jobs. Leveraging Flink's unique streaming capabilities, AthenaX supports (1) consistent computations reliably thanks to at-least-once guarantees, (2) nontrivial analytics (e.g., windowing and joins) on multiple data sources, and (3) efficient and cost-effective executions in production through code generation and elastic scaling.
BDT303 Data Science with Elastic MapReduce - AWS re: Invent 2012Amazon Web Services
In this talk, we dive into the Netflix Data Science & Engineering architecture. Not just the what, but also the why. Some key topics include the big data technologies we leverage (Cassandra, Hadoop, Pig + Python, and Hive), our use of Amazon S3 as our central data hub, our use of multiple persistent Amazon Elastic MapReduce (EMR) clusters, how we leverage the elasticity of AWS, our data science as a service approach, how we make our hybrid AWS / data center setup work well, and more.
The analysis of large amounts of data equires database
NoSQL, software framework that supports distributed computing and search engine. On these two fronts Amazon Web Services provides us the services DynamoDB, Elastic MapReduce and Cloud Search
Deep Learning to Production with MLflow & RedisAIDatabricks
Taking deep learning models to production and doing so reliably is one of the next frontiers of MLOps. With the advent of Redis modules and the availability of C APIs for the major deep learning frameworks, it is now possible to turn Redis into a reliable runtime for deep learning workloads, providing a simple solution for a model serving microservice. RedisAI is shipped with several cool features such as support for multiple frameworks, CPU and GPU backend, auto batching, DAGing, and soon will be with automatic monitoring abilities. In this talk, we'll explore some of these features of RedisAI and see how easy it is to integrate MLflow and RedisAI to build an efficient productionization pipeline.
Slides from the Big Data Gurus meetup at Samsung R&D, August 14, 2013
This presentation covers the high level architecture of the Netflix Data Platform with a deep dive into the architecture, implementation, use cases, and future of Lipstick (https://github.com/Netflix/Lipstick) - our open source tool for graphically analyzing and monitoring the execution of Apache Pig scripts.
Netflix uses Apache Pig to express many complex data manipulation and analytics workflows. While Pig provides a great level of abstraction between MapReduce and data flow logic, once scripts reach a sufficient level of complexity, it becomes very difficult to understand how data is being transformed and manipulated across MapReduce jobs. To address this problem, we created (and open sourced) a tool named Lipstick that visualizes and monitors the progress and performance of Pig scripts.
Analytics at Scale with Apache Spark on AWS with Jonathan FritzDatabricks
Organizations from small startups to large enterprises are rapidly adopting Apache Spark on Amazon EMR in Amazon Web Services (AWS) to run streaming analytics, data science, machine learning, and batch processing workloads. These customers can quickly create big data architectures within minutes, and decouple compute and storage with Amazon S3 as a highly scalable, durable, and secure data lake, lower costs using Amazon EC2 Spot Instances and Auto Scaling, and utilize a wide range of encryption and access control features. In this session, we discuss how customers are using Spark on AWS and common architectures for easily running performant Spark clusters at scale and low cost with Amazon EMR.
Presto & differences between popular SQL engines (Spark, Redshift, and Hive)Holden Ackerman
This is a presentation given at a Big Data Boulder / Denver Meetup event by Ashish Dubey, a Senior Solutions Architect at Qubole.
The following slides cover a background of Presto and its architecture, and how it differs in both performance and cost from traditional Hadoop / Hive for Adhoc queries as well as SparkSQL, Impala, Tez, and Redshift.
There are also several slides about how Qubole has been involved with the open-source Apache Presto project, along with performance optimizing contributions.
Qubole is a big data analytics software that has solved many headaches around the traditional model of big data (Hadoop, Spark, Presto) and cloud computing in popular IaaS providers: AWS, Google Cloud, Microsoft Azure, and Oracle BMC.
Powering Interactive Data Analysis at Pinterest by Amazon RedshiftJie Li
In the last six month, we have set up Amazon Redshift to power our interactive data analysis at Pinterest. It has tremendously improved the speed of analyzing our data.
(BDT404) Large-Scale ETL Data Flows w/AWS Data Pipeline & DataductAmazon Web Services
"As data volumes grow, managing and scaling data pipelines for ETL and batch processing can be daunting. With more than 13.5 million learners worldwide, hundreds of courses, and thousands of instructors, Coursera manages over a hundred data pipelines for ETL, batch processing, and new product development.
In this session, we dive deep into AWS Data Pipeline and Dataduct, an open source framework built at Coursera to manage pipelines and create reusable patterns to expedite developer productivity. We share the lessons learned during our journey: from basic ETL processes, such as loading data from Amazon RDS to Amazon Redshift, to more sophisticated pipelines to power recommendation engines and search services.
Attendees learn:
Do's and don’ts of Data Pipeline
Using Dataduct to streamline your data pipelines
How to use Data Pipeline to power other data products, such as recommendation systems
What’s next for Dataduct"
(ISM303) Migrating Your Enterprise Data Warehouse To Amazon RedshiftAmazon Web Services
Learn how Boingo Wireless and online media provider Edmunds gained substantial business insights and saved money and time by migrating to Amazon Redshift. Get an inside look into how they accomplished their migration from on-premises solutions. Learn how they tuned their schema and queries to take full advantage of the columnar MPP architecture in Amazon Redshift, how they leveraged third party solutions, and how they met their business intelligence needs in record time.
What do you talk about to a hall full of database gurus? Instead of science - my talk focused on the art. What made Hadoop successful? What can we learn from it? What principles work well in building software for large scale services? What are some interesting unsolved problems in a world overrun by open-source (and VC investments :-))
(BDT403) Netflix's Next Generation Big Data Platform | AWS re:Invent 2014Amazon Web Services
As Netflix expands their services to more countries, devices, and content, they continue to evolve their big data analytics platform to accommodate the increasing needs of product and consumer insights. This year, Netflix re-innovated their big data platform: they upgraded to Hadoop 2, transitioned to the Parquet file format, experimented with Pig on Tez for the ETL workload, and adopted Presto as their interactive querying engine. In this session, Netflix discusses their latest architecture, how they built it on the Amazon EMR infrastructure, the contributions put into the open source community, as well as some performance numbers for running a big data warehouse with Amazon S3.
BDT303 Data Science with Elastic MapReduce - AWS re: Invent 2012Amazon Web Services
In this talk, we dive into the Netflix Data Science & Engineering architecture. Not just the what, but also the why. Some key topics include the big data technologies we leverage (Cassandra, Hadoop, Pig + Python, and Hive), our use of Amazon S3 as our central data hub, our use of multiple persistent Amazon Elastic MapReduce (EMR) clusters, how we leverage the elasticity of AWS, our data science as a service approach, how we make our hybrid AWS / data center setup work well, and more.
The analysis of large amounts of data equires database
NoSQL, software framework that supports distributed computing and search engine. On these two fronts Amazon Web Services provides us the services DynamoDB, Elastic MapReduce and Cloud Search
Deep Learning to Production with MLflow & RedisAIDatabricks
Taking deep learning models to production and doing so reliably is one of the next frontiers of MLOps. With the advent of Redis modules and the availability of C APIs for the major deep learning frameworks, it is now possible to turn Redis into a reliable runtime for deep learning workloads, providing a simple solution for a model serving microservice. RedisAI is shipped with several cool features such as support for multiple frameworks, CPU and GPU backend, auto batching, DAGing, and soon will be with automatic monitoring abilities. In this talk, we'll explore some of these features of RedisAI and see how easy it is to integrate MLflow and RedisAI to build an efficient productionization pipeline.
Slides from the Big Data Gurus meetup at Samsung R&D, August 14, 2013
This presentation covers the high level architecture of the Netflix Data Platform with a deep dive into the architecture, implementation, use cases, and future of Lipstick (https://github.com/Netflix/Lipstick) - our open source tool for graphically analyzing and monitoring the execution of Apache Pig scripts.
Netflix uses Apache Pig to express many complex data manipulation and analytics workflows. While Pig provides a great level of abstraction between MapReduce and data flow logic, once scripts reach a sufficient level of complexity, it becomes very difficult to understand how data is being transformed and manipulated across MapReduce jobs. To address this problem, we created (and open sourced) a tool named Lipstick that visualizes and monitors the progress and performance of Pig scripts.
Analytics at Scale with Apache Spark on AWS with Jonathan FritzDatabricks
Organizations from small startups to large enterprises are rapidly adopting Apache Spark on Amazon EMR in Amazon Web Services (AWS) to run streaming analytics, data science, machine learning, and batch processing workloads. These customers can quickly create big data architectures within minutes, and decouple compute and storage with Amazon S3 as a highly scalable, durable, and secure data lake, lower costs using Amazon EC2 Spot Instances and Auto Scaling, and utilize a wide range of encryption and access control features. In this session, we discuss how customers are using Spark on AWS and common architectures for easily running performant Spark clusters at scale and low cost with Amazon EMR.
Presto & differences between popular SQL engines (Spark, Redshift, and Hive)Holden Ackerman
This is a presentation given at a Big Data Boulder / Denver Meetup event by Ashish Dubey, a Senior Solutions Architect at Qubole.
The following slides cover a background of Presto and its architecture, and how it differs in both performance and cost from traditional Hadoop / Hive for Adhoc queries as well as SparkSQL, Impala, Tez, and Redshift.
There are also several slides about how Qubole has been involved with the open-source Apache Presto project, along with performance optimizing contributions.
Qubole is a big data analytics software that has solved many headaches around the traditional model of big data (Hadoop, Spark, Presto) and cloud computing in popular IaaS providers: AWS, Google Cloud, Microsoft Azure, and Oracle BMC.
Powering Interactive Data Analysis at Pinterest by Amazon RedshiftJie Li
In the last six month, we have set up Amazon Redshift to power our interactive data analysis at Pinterest. It has tremendously improved the speed of analyzing our data.
(BDT404) Large-Scale ETL Data Flows w/AWS Data Pipeline & DataductAmazon Web Services
"As data volumes grow, managing and scaling data pipelines for ETL and batch processing can be daunting. With more than 13.5 million learners worldwide, hundreds of courses, and thousands of instructors, Coursera manages over a hundred data pipelines for ETL, batch processing, and new product development.
In this session, we dive deep into AWS Data Pipeline and Dataduct, an open source framework built at Coursera to manage pipelines and create reusable patterns to expedite developer productivity. We share the lessons learned during our journey: from basic ETL processes, such as loading data from Amazon RDS to Amazon Redshift, to more sophisticated pipelines to power recommendation engines and search services.
Attendees learn:
Do's and don’ts of Data Pipeline
Using Dataduct to streamline your data pipelines
How to use Data Pipeline to power other data products, such as recommendation systems
What’s next for Dataduct"
(ISM303) Migrating Your Enterprise Data Warehouse To Amazon RedshiftAmazon Web Services
Learn how Boingo Wireless and online media provider Edmunds gained substantial business insights and saved money and time by migrating to Amazon Redshift. Get an inside look into how they accomplished their migration from on-premises solutions. Learn how they tuned their schema and queries to take full advantage of the columnar MPP architecture in Amazon Redshift, how they leveraged third party solutions, and how they met their business intelligence needs in record time.
What do you talk about to a hall full of database gurus? Instead of science - my talk focused on the art. What made Hadoop successful? What can we learn from it? What principles work well in building software for large scale services? What are some interesting unsolved problems in a world overrun by open-source (and VC investments :-))
(BDT403) Netflix's Next Generation Big Data Platform | AWS re:Invent 2014Amazon Web Services
As Netflix expands their services to more countries, devices, and content, they continue to evolve their big data analytics platform to accommodate the increasing needs of product and consumer insights. This year, Netflix re-innovated their big data platform: they upgraded to Hadoop 2, transitioned to the Parquet file format, experimented with Pig on Tez for the ETL workload, and adopted Presto as their interactive querying engine. In this session, Netflix discusses their latest architecture, how they built it on the Amazon EMR infrastructure, the contributions put into the open source community, as well as some performance numbers for running a big data warehouse with Amazon S3.
(BDT208) A Technical Introduction to Amazon Elastic MapReduceAmazon Web Services
"Amazon EMR provides a managed framework which makes it easy, cost effective, and secure to run data processing frameworks such as Apache Hadoop, Apache Spark, and Presto on AWS. In this session, you learn the key design principles behind running these frameworks on the cloud and the feature set that Amazon EMR offers. We discuss the benefits of decoupling compute and storage and strategies to take advantage of the scale and the parallelism that the cloud offers, while lowering costs. Additionally, you hear from AOL’s Senior Software Engineer on how they used these strategies to migrate their Hadoop workloads to the AWS cloud and lessons learned along the way.
In this session, you learn the benefits of decoupling storage and compute and allowing them to scale independently; how to run Hadoop, Spark, Presto and other supported Hadoop Applications on Amazon EMR; how to use Amazon S3 as a persistent data-store and process data directly from Amazon S3; dDeployment strategies and how to avoid common mistakes when deploying at scale; and how to use Spot instances to scale your transient infrastructure effectively."
Introducing Amazon EMR Release 5.0 - August 2016 Monthly Webinar SeriesAmazon Web Services
Amazon EMR is a managed Hadoop service that makes it easy for customers to use big data frameworks and applications like Hadoop, Spark, and Presto to analyze data stored in HDFS or on Amazon S3 , Amazon’s highly scalable object storage service. In this webinar, we will introduce the latest release of Amazon EMR. With Amazon EMR release 5.0, customers can now launch the latest versions of popular open source frameworks including Apache Spark 2.0, Hive 2.1, Presto 0.151, Tez 0.8.4, and Apache Hadoop 2.7.2. We will walk through a demo to show you how to deploy a Hadoop environment within minutes. We will cover common use cases and best practices to lower costs using Amazon S3 as your data store and Amazon EC2 Spot Instances, which allow you to bid on space Amazon computing capacity.
Learning Objectives:
• Describe the new features and updated frameworks in Amazon EMR 5.0
• Learn best practices and real-world applications for Amazon EMR
• Understand how to use EC2 Spot pricing to save costs
• Explain the advantages of decoupling storage and compute with Amazon S3 as storage layer for EMR workloads
For organizations looking to glean insights from their data, it is essential to deploy the right environment to successfully support analytics workloads. Learn about the different storage options from AWS, and discuss with our experts how to select the best option for your big data analytics workloads. Hear how one customer, ViaSat, used Amazon EBS for their Apache Kafka and Apache Hadoop workload to improve cost and performance. We also describe best practices and share tips for success throughout.
BDA402 Deep Dive: Log Analytics with Amazon Elasticsearch ServiceAmazon Web Services
Everything generates logs. Applications, infrastructure, security ... everything. Keeping track of the flood of log data is a big challenge, yet critical to your ability to understand your systems and troubleshoot (or prevent) issues. In this session, we will use both Amazon CloudWatch and application logs to show you how to build an end-to-end log analytics solution. First, we cover how to configure an Amazon Elaticsearch Service domain and ingest data into it using Amazon Kinesis Firehose, demonstrating how easy it is to transform data with Firehose. We look at best practices for choosing instance types, storage options, shard counts, and index rotations based on the throughput of incoming data and configure a secure analytics environment. We demonstrate how to set up a Kibana dashboard and build custom dashboard widgets. Finally, we dive deep into the Elasticsearch query DSL and review approaches for generating custom, ad-hoc reports.
Gestione gerarchica dei dati con SUSE Enterprise Storage e HPE DMFSUSE Italy
In questa sessione HPE e SUSE illustrano con casi reali come HPE Data Management Framework e SUSE Enterprise Storage permettano di risolvere i problemi di gestione della crescita esponenziale dei dati realizzando un’architettura software-defined flessibile, scalabile ed economica. (Alberto Galli, HPE Italia e SUSE)
Modernizing upstream workflows with aws storage - john malloryAmazon Web Services
Modernizing Upstream Workflows with AWS Storage
Accelerating seismic data retrieval, getting better data protection and reliability, and providing a common AWS data platform for compute and graphic intensive processing, simulation and visualization workloads.
Modernizing and transforming exploration and production workflows with AWS Storage services
Accelerating seismic data retrieval, getting better data protection and reliability, and providing a common AWS data platform for compute and graphic intensive processing, simulation and visualization workloads.
Capturing and processing streaming sensor data from remote oil rigs with Snowball Edge
Providing a Data Lake foundation for a next generation Digital Oilfield IoT analytics platform with Amazon S3
Speaker: John Mallory - AWS Storage Business Development Manager
Organizations often need to quickly analyze large amounts of data, such as logs generated from a wide variety of sources and formats. However, traditional approaches require a lot of time and effort designing complex data transformation and loading processes; and configuring data warehouses. Using AWS, you can start querying your datasets within minutes. In this session you will learn how you can deploy a managed Presto environment in minutes to interactively query log data using standard ANSI SQL. Presto is a popular open source SQL engine for running interactive analytic queries against data sources of all sizes. We will talk about common use cases and best practices for running Presto on Amazon EMR.
Hopsworks in the cloud Berlin Buzzwords 2019 Jim Dowling
This talk, given at Berlin Buzzwords 2019, describes the recent progress in making Hopsworks a cloud-native platform, with HA data-center support added for HopsFS.
Startup Case Study: Leveraging the Broad Hadoop Ecosystem to Develop World-Fi...DataWorks Summit
Back in 2014, our team set out to change the way the world exchanges and collaborates with data. Our vision was to build a single tenant environment for multiple organisations to securely share and consume data. And we did just that, leveraging multiple Hadoop technologies to help our infrastructure scale quickly and securely.
Today Data Republic’s technology delivers a trusted platform for hundreds of enterprise level companies to securely exchange, commercialise and collaborate with large datasets.
Join Head of Engineering, Juan Delard de Rigoulières and Senior Solutions Architect, Amin Abbaspour as they share key lessons from their team’s journey with Hadoop:
* How a startup leveraged a clever combination of Hadoop technologies to build a secure data exchange platform
* How Hadoop technologies helped us deliver key solutions around governance, security and controls of data and metadata
* An evaluation on the maturity and usefulness of some Hadoop technologies in our environment: Hive, HDFS, Spark, Ranger, Atlas, Knox, Kylin: we've use them all extensively.
* Our bold approach to expose APIs directly to end users; as well as the challenges, learning and code we created in the process
* Learnings from the front-line: How our team coped with code changes, performance tuning, issues and solutions while building our data exchange
Whether you’re an enterprise level business or a start-up looking to scale - this case study discussion offers behind-the-scenes lessons and key tips when using Hadoop technologies to manage data governance and collaboration in the cloud.
Speakers:
Juan Delard De Rigoulieres, Head of Engineering, Data Republic Pty Ltd
Amin Abbaspour, Senior Solutions Architect, Data Republic
by Avijit Goswami, Sr. Solutions Architect, AWS
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
Voldemort & Hadoop @ Linkedin, Hadoop User Group Jan 2010Bhupesh Bansal
Jan 22nd, 2010 Hadoop meetup presentation on project voldemort and how it plays well with Hadoop at linkedin. The talk focus on Linkedin Hadoop ecosystem. How linkedin manage complex workflows, data ETL , data storage and online serving of 100GB to TB of data.
Similar to Dataflow in 104corp - AWS UserGroup TW 2018 (20)
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
4. Routine works
• Data processing
• Data mining (Marketing)
• Machine learning (Matching)
• Item-Item: Users who liked this
item also liked
• User-Item: Users who are similar
to you also liked
4Captain monkey - Sean.Chang
14. Full-Controllable or not
Amazon
Kinesis
Apache
Kafka
14
Unit Stream Topic
Distribution Shards Partitions
Thoughtput
2 MB read/shards
1 MB write/shards
Based on cluster size
Fault tolerance Handled by AWS Replica
Transformer AWS Lambda Connectors/Processors
Framework Support Spark and Flink
16. Cost / Performance
Amazon
S3
Amazon
EMR
Type Objects Block device
Throughput Middle High
Cost 0.025 USD/GB 0.12x3 USD/GB (EBS gp2)
Maintenance
No
(Policy / Lifecycle)
Yes
Libraries
Hadoop-aws
(3.x.x is better)
ALL
16
Storing Apache Hadoop Data on the Cloud - HDFS vs. S3
Top 5 Reasons for Choosing S3 over HDFS
29. • “XXX as a Service” first - why not
• Full-Controllable or not
• Cost / Performance (pay-as-you-go)
• Computing environment depends on scenario.
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