Apache Flink® is an open source platform for scalable stream and batch data processing. It offers expressive APIs to define batch and streaming data flow programs and a robust and scalable engine to execute these jobs.
This presentation was inspired post read of "TimeSeries Databases" -- Ted Dunning & Ellen Friedman.
I have tried to summarize a lot of the previous bench marks. Hope others find it useful. The slides were compiled early 2015 so some of the results might have changed but the core literature should still hold.
Are OLAP cubes "large monters" that deliver quick data retrieval at the expense of long upload time? This presentation shows one way to kill this myth.
Apache Flink® is an open source platform for scalable stream and batch data processing. It offers expressive APIs to define batch and streaming data flow programs and a robust and scalable engine to execute these jobs.
This presentation was inspired post read of "TimeSeries Databases" -- Ted Dunning & Ellen Friedman.
I have tried to summarize a lot of the previous bench marks. Hope others find it useful. The slides were compiled early 2015 so some of the results might have changed but the core literature should still hold.
Are OLAP cubes "large monters" that deliver quick data retrieval at the expense of long upload time? This presentation shows one way to kill this myth.
(Berkeley CS186 guest lecture) Big Data Analytics Systems: What Goes Around C...Reynold Xin
(Berkeley CS186 guest lecture)
Big Data Analytics Systems: What Goes Around Comes Around
Introduction to MapReduce, GFS, HDFS, Spark, and differences between "Big Data" and database systems.
Databricks Spark Chief Architect Reynold Xin's keynote at Spark Summit East 2016, discussing streaming, continuous applications, and DataFrames in Spark.
The design and implementation of modern column oriented databasesTilak Patidar
An attempt to break down the paper on the design of column-oriented databases into simpler terms.
https://stratos.seas.harvard.edu/files/stratos/files/columnstoresfntdbs.pdf
https://blog.acolyer.org/2018/09/26/the-design-and-implementation-of-modern-column-oriented-database-systems/
Course "Machine Learning and Data Mining" for the degree of Computer Engineering at the Politecnico di Milano. In in this lecture we overview the mining of data streams
Presentation for Pervasive Systems class lectured by prof. Ioannis Chatzigiannakis, a.y. 2015-16, about the No-SQL database InfluxDB. The course is intended for students of MS in Engineering in Computer Science at Sapienza - University of Rome.
The complete code for the demo is available on Github:
https://github.com/RobGaud/PervasiveSystemsPersonal
You can also find me on LinkedIn:
https://www.linkedin.com/in/roberto-gaudenzi-4b0422116
This presentation is an attempt do demystify the practice of building reliable data processing pipelines. We go through the necessary pieces needed to build a stable processing platform: data ingestion, processing engines, workflow management, schemas, and pipeline development processes. The presentation also includes component choice considerations and recommendations, as well as best practices and pitfalls to avoid, most learnt through expensive mistakes.
Design of a lightweight set of data pipelines to scrub PII information.
Scrubbing PII information from data brings ease of sharing data.
It also helps organisations to confidently push data outside organisation for large scale analytics on the cloud.
The millions of people that use Spotify each day generate a lot of data, roughly a few terabytes per day. What does it take to handle datasets of that scale, and what can be done with it? I will briefly cover how Spotify uses data to provide a better music listening experience, and to strengthen their busineess. Most of the talk will be spent on our data processing architecture, and how we leverage state of the art data processing and storage tools, such as Hadoop, Cassandra, Kafka, Storm, Hive, and Crunch. Last, I'll present observations and thoughts on innovation in the data processing aka Big Data field.
The functional paradigm is not only applicable to programming. There is even more reason for using functional patterns at an architectural level. MapReduce is the most famous example of such a pattern. In this talk, we will go through a few other architectural patterns, and their corresponding stateful anti-patterns.
PipelineDB is an open-source relational database that runs SQL queries continuously on streaming data, incrementally storing results in tables. Our talk will include an overview of PipelineDB’s architecture, the use cases for continuous SQL queries on streams, user case studies, and outline how PipelineDB can used to easily build scalable and highly available streaming and realtime analytics applications using only SQL with no external dependencies.
http://bit.ly/1ALVcwR – MapR Director of Architecture and Enterprise Strategy Jim Scott presented a session titled “Time Series Data in a Time Series World.” His session focused on working with time series data including single-value, geospatial and log time series data. By focusing on enterprise applications and the data center, OpenTSDB will be used as an example to explain some of the key time series core concepts including when to use different storage models.
Things Expo | San Jose, California - November 2014
Test strategies for data processing pipelinesLars Albertsson
This talk will present recommended patterns and corresponding anti-patterns for testing data processing pipelines. We will suggest technology and architecture to improve testability, both for batch and streaming processing pipelines. We will primarily focus on testing for the purpose of development productivity and product iteration speed, but briefly also cover data quality testing.
Presented at highloadstrategy.com 2016 by Lars Albertsson (independent, www.mapflat.com), joint work with Øyvind Løkling (Schibsted Products & Technology).
Chicago Data Summit: Flume: An IntroductionCloudera, Inc.
Flume is an open-source, distributed, streaming log collection system designed for ingesting large quantities of data into large-scale data storage and analytics platforms such as Apache Hadoop. It has four goals in mind: Reliability, Scalability, Extensibility, and Manageability. Its horizontal scalable architecture offers fault-tolerant end-to-end delivery guarantees, support for low-latency event processing, provides a centralized management interface , and exposes metrics for ingest monitoring and reporting. It natively supports writing data to Hadoop's HDFS but also has a simple extension interface that allows it to write to other scalable data systems such as low-latency datastores or incremental search indexers.
Hadoop World 2011: Storing and Indexing Social Media Content in the Hadoop Ec...Cloudera, Inc.
Jive is using Flume to deliver the content of a social web (250M messages/day) to HDFS and HBase. Flume's flexible architecture allows us to stream data to our production data center as well as Amazon's Web Services datacenter. We periodically build and merge Lucene indices with Hadoop jobs and deploy these to Katta to provide near real time search results. This talk will explore our infrastructure and decisions we've made to handle a fast growing set of real time data feeds. We will further explore other uses for Flume throughout Jive including log collection and our distributed event bus.
(Berkeley CS186 guest lecture) Big Data Analytics Systems: What Goes Around C...Reynold Xin
(Berkeley CS186 guest lecture)
Big Data Analytics Systems: What Goes Around Comes Around
Introduction to MapReduce, GFS, HDFS, Spark, and differences between "Big Data" and database systems.
Databricks Spark Chief Architect Reynold Xin's keynote at Spark Summit East 2016, discussing streaming, continuous applications, and DataFrames in Spark.
The design and implementation of modern column oriented databasesTilak Patidar
An attempt to break down the paper on the design of column-oriented databases into simpler terms.
https://stratos.seas.harvard.edu/files/stratos/files/columnstoresfntdbs.pdf
https://blog.acolyer.org/2018/09/26/the-design-and-implementation-of-modern-column-oriented-database-systems/
Course "Machine Learning and Data Mining" for the degree of Computer Engineering at the Politecnico di Milano. In in this lecture we overview the mining of data streams
Presentation for Pervasive Systems class lectured by prof. Ioannis Chatzigiannakis, a.y. 2015-16, about the No-SQL database InfluxDB. The course is intended for students of MS in Engineering in Computer Science at Sapienza - University of Rome.
The complete code for the demo is available on Github:
https://github.com/RobGaud/PervasiveSystemsPersonal
You can also find me on LinkedIn:
https://www.linkedin.com/in/roberto-gaudenzi-4b0422116
This presentation is an attempt do demystify the practice of building reliable data processing pipelines. We go through the necessary pieces needed to build a stable processing platform: data ingestion, processing engines, workflow management, schemas, and pipeline development processes. The presentation also includes component choice considerations and recommendations, as well as best practices and pitfalls to avoid, most learnt through expensive mistakes.
Design of a lightweight set of data pipelines to scrub PII information.
Scrubbing PII information from data brings ease of sharing data.
It also helps organisations to confidently push data outside organisation for large scale analytics on the cloud.
The millions of people that use Spotify each day generate a lot of data, roughly a few terabytes per day. What does it take to handle datasets of that scale, and what can be done with it? I will briefly cover how Spotify uses data to provide a better music listening experience, and to strengthen their busineess. Most of the talk will be spent on our data processing architecture, and how we leverage state of the art data processing and storage tools, such as Hadoop, Cassandra, Kafka, Storm, Hive, and Crunch. Last, I'll present observations and thoughts on innovation in the data processing aka Big Data field.
The functional paradigm is not only applicable to programming. There is even more reason for using functional patterns at an architectural level. MapReduce is the most famous example of such a pattern. In this talk, we will go through a few other architectural patterns, and their corresponding stateful anti-patterns.
PipelineDB is an open-source relational database that runs SQL queries continuously on streaming data, incrementally storing results in tables. Our talk will include an overview of PipelineDB’s architecture, the use cases for continuous SQL queries on streams, user case studies, and outline how PipelineDB can used to easily build scalable and highly available streaming and realtime analytics applications using only SQL with no external dependencies.
http://bit.ly/1ALVcwR – MapR Director of Architecture and Enterprise Strategy Jim Scott presented a session titled “Time Series Data in a Time Series World.” His session focused on working with time series data including single-value, geospatial and log time series data. By focusing on enterprise applications and the data center, OpenTSDB will be used as an example to explain some of the key time series core concepts including when to use different storage models.
Things Expo | San Jose, California - November 2014
Test strategies for data processing pipelinesLars Albertsson
This talk will present recommended patterns and corresponding anti-patterns for testing data processing pipelines. We will suggest technology and architecture to improve testability, both for batch and streaming processing pipelines. We will primarily focus on testing for the purpose of development productivity and product iteration speed, but briefly also cover data quality testing.
Presented at highloadstrategy.com 2016 by Lars Albertsson (independent, www.mapflat.com), joint work with Øyvind Løkling (Schibsted Products & Technology).
Chicago Data Summit: Flume: An IntroductionCloudera, Inc.
Flume is an open-source, distributed, streaming log collection system designed for ingesting large quantities of data into large-scale data storage and analytics platforms such as Apache Hadoop. It has four goals in mind: Reliability, Scalability, Extensibility, and Manageability. Its horizontal scalable architecture offers fault-tolerant end-to-end delivery guarantees, support for low-latency event processing, provides a centralized management interface , and exposes metrics for ingest monitoring and reporting. It natively supports writing data to Hadoop's HDFS but also has a simple extension interface that allows it to write to other scalable data systems such as low-latency datastores or incremental search indexers.
Hadoop World 2011: Storing and Indexing Social Media Content in the Hadoop Ec...Cloudera, Inc.
Jive is using Flume to deliver the content of a social web (250M messages/day) to HDFS and HBase. Flume's flexible architecture allows us to stream data to our production data center as well as Amazon's Web Services datacenter. We periodically build and merge Lucene indices with Hadoop jobs and deploy these to Katta to provide near real time search results. This talk will explore our infrastructure and decisions we've made to handle a fast growing set of real time data feeds. We will further explore other uses for Flume throughout Jive including log collection and our distributed event bus.
Designing a reactive data platform: Challenges, patterns, and anti-patterns Alex Silva
Presentation given at the O'Reilly Software Architecture Conference in NYC, April 2016.
Covers the key architectural decisions made behind the design of a reactive self-service data ingestion analytics platform that is able to fulfill several business use cases at massive scale, both at real-time and batch scopes, while leveraging and integrating Kafka and Spark in an efficient, easy to use way.
The presentation describes a message-driven, reactive and distributed design that leverages REST and Hypermedia protocols, and several open source frameworks and platforms, including Akka, Kafka, Hadoop and Spark.
How to develop Big Data Pipelines for Hadoop, by Costin LeauCodemotion
Hadoop is not an island. To deliver a complete Big Data solution, a data pipeline needs to be developed that incorporates and orchestrates many diverse technologies. In this session we will demonstrate how the open source Spring Batch, Spring Integration and Spring Hadoop projects can be used to build manageable and robust pipeline solutions to coordinate the running of multiple Hadoop jobs (MapReduce, Hive, or Pig), but also encompass real-time data acquisition and analysis.
Spark Streaming & Kafka-The Future of Stream ProcessingJack Gudenkauf
Hari Shreedharan/Cloudera @Playtika. With its easy to use interfaces and native integration with some of the most popular ingest tools, such as Kafka, Flume, Kinesis etc, Spark Streaming has become go-to tool for stream processing. Code sharing with Spark also makes it attractive. In this talk, we will discuss the latest features in Spark Streaming and how it integrates with Kafka natively with no data loss, and even do exactly once processing!
Part 2 - Hadoop Data Loading using Hadoop Tools and ODI12cMark Rittman
Delivered as a one-day seminar at the SIOUG and HROUG Oracle User Group Conferences, October 2014.
There are many ways to ingest (load) data into a Hadoop cluster, from file copying using the Hadoop Filesystem (FS) shell through to real-time streaming using technologies such as Flume and Hadoop streaming. In this session we’ll take a high-level look at the data ingestion options for Hadoop, and then show how Oracle Data Integrator and Oracle GoldenGate leverage these technologies to load and process data within your Hadoop cluster. We’ll also consider the updated Oracle Information Management Reference Architecture and look at the best places to land and process your enterprise data, using Hadoop’s schema-on-read approach to hold low-value, low-density raw data, and then use the concept of a “data factory” to load and process your data into more traditional Oracle relational storage, where we hold high-density, high-value data.
Building Continuously Curated Ingestion PipelinesArvind Prabhakar
Data ingestion is a critical piece of infrastructure for any Big Data project. Learn about the key challenges in building Ingestion infrastructure and how enterprises are solving them using low level frameworks like Apache Flume, Kafka, and high level systems such as StreamSets.
Open Source Big Data Ingestion - Without the Heartburn!Pat Patterson
Big Data tools such as Hadoop and Spark allow you to process data at unprecedented scale, but keeping your processing engine fed can be a challenge. Upstream data sources can 'drift' due to infrastructure, OS and application changes, causing ETL tools and hand-coded solutions to fail, inducing heartburn in even the most resilient data scientist. This session will survey the big data ingestion landscape, focusing on how open source tools such as Sqoop, Flume, Nifi and StreamSets can keep the data pipeline flowing.
How to Build Continuous Ingestion for the Internet of ThingsCloudera, Inc.
The Internet of Things is moving into the mainstream and this new world of data-driven products is transforming a vast number of industry sectors and technologies.
However, IoT creates a new challenge: how to build and operationalize continual data ingestion from such a wide and ever-changing array of endpoints so that the data arrives consumption-ready and can drive analysis and action within the business.
In this webinar, Sean Anderson from Cloudera and Kirit Busu, Director of Product Management at StreamSets, will discuss Hadoop's ecosystem and IoT capabilities and provide advice about common patterns and best practices. Using specific examples, they will demonstrate how to build and run end-to-end IOT data flows using StreamSets and Cloudera infrastructure.
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.
At improve digital we collect and store large volumes of machine generated and behavioural data from our fleet of ad servers. For some time we have performed mostly batch processing through a data warehouse that combines traditional RDBMs (MySQL), columnar stores (Infobright, impala+parquet) and Hadoop.
We wish to share our experiences in enhancing this capability with systems and techniques that process the data as streams in near-realtime. In particular we will cover:
• The architectural need for an approach to data collection and distribution as a first-class capability
• The different needs of the ingest pipeline required by streamed realtime data, the challenges faced in building these pipelines and how they forced us to start thinking about the concept of production-ready data.
• The tools we used, in particular Apache Kafka as the message broker, Apache Samza for stream processing and Apache Avro to allow schema evolution; an essential element to handle data whose formats will change over time.
• The unexpected capabilities enabled by this approach, including the value in using realtime alerting as a strong adjunct to data validation and testing.
• What this has meant for our approach to analytics and how we are moving to online learning and realtime simulation.
This is still a work in progress at Improve Digital with differing levels of production-deployed capability across the topics above. We feel our experiences can help inform others embarking on a similar journey and hopefully allow them to learn from our initiative in this space.
UnConference for Georgia Southern Computer Science March 31, 2015Christopher Curtin
I presented to the Georgia Southern Computer Science ACM group. Rather than one topic for 90 minutes, I decided to do an UnConference. I presented them a list of 8-9 topics, let them vote on what to talk about, then repeated.
Each presentation was ~8 minutes, (Except Career) and was by no means an attempt to explain the full concept or technology. Only to wake up their interest.
Vikram Andem Big Data Strategy @ IATA Technology Roadmap IT Strategy Group
Vikram Andem, Senior Manager, United Airlines, A case for Bigdata Program and Strategy @ IATA Technology Roadmap 2014, October 13th, 2014, Montréal, Canada
introduction to data processing using Hadoop and PigRicardo Varela
In this talk we make an introduction to data processing with big data and review the basic concepts in MapReduce programming with Hadoop. We also comment about the use of Pig to simplify the development of data processing applications
YDN Tuesdays are geek meetups organized the first Tuesday of each month by YDN in London
HDFS is a Java-based file system that provides scalable and reliable data storage, and it was designed to span large clusters of commodity servers. HDFS has demonstrated production scalability of up to 200 PB of storage and a single cluster of 4500 servers, supporting close to a billion files and blocks.
The presentation aims to demystify the practice of building reliable data processing pipelines. It includes a brief overview of the pieces needed to build a stable processing platform: data ingestion,processing engines, workflow management, and schemas. For each component, suitable components are suggested, as well as best practices and pitfalls to avoid, most learnt through expensive mistakes.
Original document: https://goo.gl/rmKxZM
Integration Patterns for Big Data ApplicationsMichael Häusler
Big Data technologies like distributed databases, queues, batch processors, and stream processors are fun and exciting to play with. Making them play nicely together can be challenging. Keeping it fun for engineers to continuously improve and operate them is hard. At ResearchGate, we run thousands of YARN applications every day to gain insights and to power user facing features. Of course, there are numerous integration challenges on the way:
* integrating batch and stream processors with operational systems
* ingesting data and playing back results while controlling performance crosstalk
* rolling out new versions of synchronous, stream, and batch applications and their respective data schemas
* controlling the amount of glue and adapter code between different technologies
* modeling cross-flow dependencies while handling failures gracefully and limiting their repercussions
We describe our ongoing journey in identifying patterns and principles to make our big data stack integrate well. Technologies to be covered will include MongoDB, Kafka, Hadoop (YARN), Hive (TEZ), Flink Batch, and Flink Streaming.
Big Data: Architecture and Performance Considerations in Logical Data LakesDenodo
This presentation explains in detail what a Data Lake Architecture looks like, how data virtualization fits into the Logical Data Lake, and goes over some performance tips. Also it includes an example demonstrating this model's performance.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/9Jwfu6.
United Airlines is leveraging big data at the enterprise level to help drive revenue, improve the customer experience, optimize operations, and support our employees in their day-to-day activities. At the center of our big data stack is Apache Hadoop, supported by many other emerging open source frameworks that must be integrated with the myriad of operational systems that support a 90-year-old transportation company with worldwide operations. In addition, learn how streaming data and streaming data analytics are helping to drive operational decisions in real time and how this is being architected to scale horizontally to take advantage of high availability and parallel processing. With the rapidly evolving Hadoop ecosystem, and so many new open source technologies at our disposal, the options for solving long-standing industry problems such as modeling how customers make decisions, making timely and meaningful real-time offers, and optimizing logistical operations have never been better. JOE OLSON, Senior Manager, Big Data Analytics, United Airlines and JONATHAN INGALLS, Sr. Solutions Engineer, Hortonworks
Similar to Hadoop - Integration Patterns and Practices__HadoopSummit2010 (20)
Presented at the SPIFFE Meetup in Tokyo.
Athenz (www.athenz.io) is an open source platform for X.509 certificate-based service authentication and fine-grained access control in dynamic infrastructures.
Athenz with Istio - Single Access Control Model in Cloud Infrastructures, Tat...Yahoo Developer Network
Athenz (www.athenz.io) is an open source platform for X.509 certificate-based service authentication and fine-grained access control in dynamic infrastructures that provides options to run multi-environments with a single access control model.
Jithin Emmanuel, Sr. Software Development Manager, Developer Platform Services, provides an overview of Screwdriver (http://www.screwdriver.cd), and shares how it’s used at scale for CI/CD at Oath. Jithin leads the product development and operations of Screwdriver, which is a flagship CI/CD product used at scale in Oath.
Big Data Serving with Vespa - Jon Bratseth, Distinguished Architect, OathYahoo Developer Network
Offline and stream processing of big data sets can be done with tools such as Hadoop, Spark, and Storm, but what if you need to process big data at the time a user is making a request? Vespa (http://www.vespa.ai) allows you to search, organize and evaluate machine-learned models from e.g TensorFlow over large, evolving data sets with latencies in the tens of milliseconds. Vespa is behind the recommendation, ad targeting, and search at Yahoo where it handles billions of daily queries over billions of documents.
Introduction to Vespa – The Open Source Big Data Serving Engine, Jon Bratseth...Yahoo Developer Network
Offline and stream processing of big data sets can be done with tools such as Hadoop, Spark, and Storm, but what if you need to process big data at the time a user is making a request?
This presentation introduces Vespa (http://vespa.ai) – the open source big data serving engine.
Vespa allows you to search, organize, and evaluate machine-learned models from e.g TensorFlow over large, evolving data sets with latencies in the tens of milliseconds. Vespa is behind the recommendation, ad targeting, and search at Yahoo where it handles billions of daily queries over billions of documents and was recently open sourced at http://vespa.ai.
In recent times, YARN Capacity Scheduler has improved a lot in terms of some critical features and refactoring. Here is a quick look into some of the recent changes in scheduler:
Global Scheduling Support
General placement support
Better preemption model to handle resource anomalies across and within queue.
Absolute resources’ configuration support
Priority support between Queues and Applications
In this talk, we will deep dive into each of these new features to give a better picture of their usage and performance comparison. We will also provide some more brief overview about the ongoing efforts and how they can help to solve some of the core issues we face today.
Speakers:
Sunil Govind (Hortonworks), Jian He (Hortonworks)
Jun 2017 HUG: Large-Scale Machine Learning: Use Cases and Technologies Yahoo Developer Network
In recent years, Yahoo has brought the big data ecosystem and machine learning together to discover mathematical models for search ranking, online advertising, content recommendation, and mobile applications. We use distributed computing clusters with CPUs and GPUs to train these models from 100’s of petabytes of data.
A collection of distributed algorithms have been developed to achieve 10-1000x the scale and speed of alternative solutions. Our algorithms construct regression/classification models and semantic vectors within hours, even for billions of training examples and parameters. We have made our distributed deep learning solutions, CaffeOnSpark and TensorFlowOnSpark, available as open source.
In this talk, we highlight Yahoo use cases where big data and machine learning technologies are best exemplified. We explain algorithm/system challenges to scale ML algorithms for massive datasets. We provide a technical overview of CaffeOnSpark and TensorFlowOnSpark to jumpstart your journey of large-scale machine learning.
Speakers:
Andy Feng is a VP of Architecture at Yahoo, leading the architecture and design of big data and machine learning initiatives. He has architected large-scale systems for personalization, ad serving, NoSQL, and cloud infrastructure. Prior to Yahoo, he was a Chief Architect at Netscape/AOL, and Principal Scientist at Xerox. He received a Ph.D. degree in computer science from Osaka University, Japan.
February 2017 HUG: Slow, Stuck, or Runaway Apps? Learn How to Quickly Fix Pro...Yahoo Developer Network
Spark and SQL-on-Hadoop have made it easier than ever for enterprises to create or migrate apps to the big data stack. Thousands of apps are being generated every day in the form of ETL and modeling pipelines, business intelligence and data cubes, deep machine learning, graph analytics, and real-time data streaming. However, the task of reliably operationalizing these big data apps involves many painpoints. Developers may not have the experience in distributed systems to tune apps for efficiency and performance. Diagnosing failures or unpredictable performance of apps can be a laborious process that involves multiple people. Apps may get stuck or steal resources and cause mission-critical apps to miss SLAs.
This talk with introduce the audience to these problems and their common causes. We will also demonstrate how to find and fix these problems quickly, as well as prevent such problems from happening in the first place.
Speakers:
Dr. Shivnath Babu is a Co-founder and CTO of Unravel and Associate Professor of Computer Science at Duke University. With more than a decade of experience researching the ease of use and manageability of data-intensive systems, he leads the Starfish project at Duke, which pioneered the automation of Hadoop application tuning, problem diagnosis, and resource management. Shivnath has more than 80 peer-reviewed publications to his credit and has received the U.S. National Science Foundation CAREER Award, the HP Labs Innovation Award, and three IBM Faculty Awards.
February 2017 HUG: Exactly-once end-to-end processing with Apache ApexYahoo Developer Network
Apache Apex (http://apex.apache.org/) is a stream processing platform that helps organizations to build processing pipelines with fault tolerance and strong processing guarantees. It was built to support low processing latency, high throughput, scalability, interoperability, high availability and security. The platform comes with Malhar library - an extensive collection of processing operators and a wide range of input and output connectors for out-of-the-box integration with an existing infrastructure. In the talk I am going to describe how connectors together with the distributed checkpointing (a mechanism used by the Apex to support fault tolerance and high availability) provide exactly-once end-to-end processing guarantees.
Speakers:
Vlad Rozov is Apache Apex PMC member and back-end engineer at DataTorrent where he focuses on the buffer server, Apex platform network layer, benchmarks and optimizing the core components for low latency and high throughput. Prior to DataTorrent Vlad worked on distributed BI platform at Huawei and on multi-dimensional database (OLAP) at Hyperion Solutions and Oracle.
February 2017 HUG: Data Sketches: A required toolkit for Big Data AnalyticsYahoo Developer Network
In the analysis of big data there are problematic queries that don’t scale because they require huge compute resources and time to generate exact results. Examples include count distinct, quantiles, most frequent items, joins, matrix computations, and graph analysis. If approximate results are acceptable, there is a class of sub-linear, stochastic streaming algorithms, called "sketches", that can produce results orders-of magnitude faster and with mathematically proven error bounds. For interactive queries there may not be other viable alternatives, and in the case of extracting results for these problem queries in real-time, sketches are the only known solution. For any analysis system that requires these problematic queries from big data, sketches are a required toolkit that should be tightly integrated into the system's analysis capabilities. This technology has helped Yahoo successfully reduce data processing times from days to hours, or minutes to seconds on a number of its internal platforms. This talk covers the current state of our Open Source DataSketches.github.io library, which includes adaptations and example code for Pig, Hive, Spark and Druid and gives architectural examples of use and a case study.
Speakers:
Jon Malkin is a scientist at Yahoo working to extend the DataSketches library. His previous roles have involved large scale data processing for sponsored search, display advertising, user counting, ad targeting, and cross-device user identity modeling.
Alexander Saydakov is a senior software engineer at Yahoo working on the open source Data Sketches project. In his previous roles he has been involved in building large-scale back-end data processing systems and frameworks for data analytics and experimentation based on Torque, Hadoop, Pig, Hive and Druid. Alexander’s education background is in the field of applied mathematics.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
2. The Problem Data Streams Databases Job Invocation and Workflow 2 Session Agenda
3. Tons of data (otherwise you’re in the wrong room) Tons of existing systems RDBMS Caches EDW Messaging Reporting Scheduling and job control Management and Monitoring 3 The Problem
4. Tons of data (otherwise you’re in the wrong room) Tons of existing systems RDBMS Caches EDW Messaging Reporting Scheduling and job control Management and Monitoring We’ll focus on streams, databases, and job control 4 The Problem
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7. Other uses of streams Streams are more than just logs JMS, AMQP messages: Wire tap and send to Flume Turn incremental updates into stream data to avoid DBMS “middleman” (or send to both) Many existing problems can be turned into asynchronous streams 7
10. Basic approach: queries (out), inserts (in) Works, but slow Beware the Hadoop -> RDBMS DDOS attack Manage transactions on inserts Smarter: Lower level export / import tools Go to text formats Think in batches (MR to convert text <-> SequenceFiles) Use Sqoop! 10 Relational Databases
11. Batch incoming edits Perform a MR job to apply updates Input: Original dataset + incoming batch(es) Group by record ID Secondary sort on timestamp descending Reducer selects the newest record or merges changes over time Represent delete operations using a DELETE surrogate record i.e. <timestamp>, <record id>, DELETE 11 Pattern: Incremental Merge
12. Jobs are usually triggered based on Time Data arrival Service Interface An external event Production systems must monitor for successful completion Jobs can fail (just like tasks) Build for job atomicity and clean recovery 12 Job Invocation
13. For complex chains of jobs use a workflow engine Most systems support different types of steps (e.g. Java MR, Pig, Hive, HDFS commands, shell scripts) Don’t write your own Hadoop specific: Oozie, Cascading, Azkaban, … General ETL: Spring Batch, Kettle, … 13 Workflow
14. “What was most interesting is that of the people using a homegrown system, only one said they were at all happy with it, and none would recommend their system.” Kevin D. Peterson http://kdpeterson.net/blog/2009/11/hadoop-workflow-tools-survey.html 14 Really, don’t write your own!
15. Every N minutes process new data from /incoming Move data into a working directory based on timestamp Transform records Split input data by day into separate outputs 15 Example: Ingestion
16. Every N minutes process new data from /incoming Move data into a working directory based on timestamp Transform records Split input data by day into separate outputs What’s so special here? 16 Example: Ingestion
17. Every N minutes process new data from /incoming Move data into a working directory based on timestamp Allows jobs to run concurrently Input is isolated; no duplicate processing On failure, move back into /incoming Transform records Split input data by day into separate outputs 17 Example: Ingestion
18. Every N minutes process new data from /incoming Move data into a working directory based on timestamp Transform records Nothing special. This is your logic. OK, your logic is special… you know what I mean. Split input data by day into separate outputs 18 Example: Ingestion
19. Every N minutes process new data from /incoming Move data into a working directory based on timestamp Transform records Split input data by day into separate outputs If we make no assumptions about input, we recover from previous failures Daily rollover no longer matters 19 Example: Ingestion
20. Data streams are everywhere; use Flume For bulk relational database import and export, use Sqoop Consider asynchronous updates to large data stores Incremental merges are possible Use a workflow system for complex jobs Job atomicity is critical ETL best practices still apply 20 Recap