Keynote of HadoopCon 2014 Taiwan:
* Data analytics platform architecture & designs
* Lambda architecture overview
* Using SQL as DSL for stream processing
* Lambda architecture using SQL
Lambda Architecture with Spark, Spark Streaming, Kafka, Cassandra, Akka and S...Helena Edelson
Regardless of the meaning we are searching for over our vast amounts of data, whether we are in science, finance, technology, energy, health care…, we all share the same problems that must be solved: How do we achieve that? What technologies best support the requirements? This talk is about how to leverage fast access to historical data with real time streaming data for predictive modeling for lambda architecture with Spark Streaming, Kafka, Cassandra, Akka and Scala. Efficient Stream Computation, Composable Data Pipelines, Data Locality, Cassandra data model and low latency, Kafka producers and HTTP endpoints as akka actors...
Big Data visualization with Apache Spark and Zeppelinprajods
This presentation gives an overview of Apache Spark and explains the features of Apache Zeppelin(incubator). Zeppelin is the open source tool for data discovery, exploration and visualization. It supports REPLs for shell, SparkSQL, Spark(scala), python and angular. This presentation was made on the Big Data Day, at the Great Indian Developer Summit, Bangalore, April 2015
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.
Lambda Architecture with Spark, Spark Streaming, Kafka, Cassandra, Akka and S...Helena Edelson
Regardless of the meaning we are searching for over our vast amounts of data, whether we are in science, finance, technology, energy, health care…, we all share the same problems that must be solved: How do we achieve that? What technologies best support the requirements? This talk is about how to leverage fast access to historical data with real time streaming data for predictive modeling for lambda architecture with Spark Streaming, Kafka, Cassandra, Akka and Scala. Efficient Stream Computation, Composable Data Pipelines, Data Locality, Cassandra data model and low latency, Kafka producers and HTTP endpoints as akka actors...
Big Data visualization with Apache Spark and Zeppelinprajods
This presentation gives an overview of Apache Spark and explains the features of Apache Zeppelin(incubator). Zeppelin is the open source tool for data discovery, exploration and visualization. It supports REPLs for shell, SparkSQL, Spark(scala), python and angular. This presentation was made on the Big Data Day, at the Great Indian Developer Summit, Bangalore, April 2015
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.
Data processing platforms architectures with Spark, Mesos, Akka, Cassandra an...Anton Kirillov
This talk is about architecture designs for data processing platforms based on SMACK stack which stands for Spark, Mesos, Akka, Cassandra and Kafka. The main topics of the talk are:
- SMACK stack overview
- storage layer layout
- fixing NoSQL limitations (joins and group by)
- cluster resource management and dynamic allocation
- reliable scheduling and execution at scale
- different options for getting the data into your system
- preparing for failures with proper backup and patching strategies
Fast and Simplified Streaming, Ad-Hoc and Batch Analytics with FiloDB and Spa...Helena Edelson
O'Reilly Webcast with Myself and Evan Chan on the new SNACK Stack (playoff of SMACK) with FIloDB: Scala, Spark Streaming, Akka, Cassandra, FiloDB and Kafka.
NoLambda: Combining Streaming, Ad-Hoc, Machine Learning and Batch AnalysisHelena Edelson
Slides from my talk with Evan Chan at Strata San Jose: NoLambda: Combining Streaming, Ad-Hoc, Machine Learning and Batch Analysis. Streaming analytics architecture in big data for fast streaming, ad hoc and batch, with Kafka, Spark Streaming, Akka, Mesos, Cassandra and FiloDB. Simplifying to a unified architecture.
Hoodie: How (And Why) We built an analytical datastore on SparkVinoth Chandar
Exploring a specific problem of ingesting petabytes of data in Uber and why they ended up building an analytical datastore from scratch using Spark. Then, discuss design choices and implementation approaches in building Hoodie to provide near-real-time data ingestion and querying using Spark and HDFS.
https://spark-summit.org/2017/events/incremental-processing-on-large-analytical-datasets/
Lessons Learned from Managing Thousands of Production Apache Spark Clusters w...Databricks
At Databricks, we have a unique view into hundreds different companies using Apache Spark for development and production use-cases, from their support tickets and forum posts. Having seen so many different workflows and applications, some discernible patterns emerge when looking at common manageability, debugging, and visibility issues that our users run into. This talk will first show some representatives of these common issues. Then, we will show you what we have done and have been working on in Databricks to make Spark clusters easier to manage, monitor, and debug.
Bellevue Big Data meetup: Dive Deep into Spark StreamingSantosh Sahoo
Discuss the code and architecture about building realtime streaming application using Spark and Kafka. This demo presents some use cases and patterns of different streaming frameworks.
Reactive dashboard’s using apache sparkRahul Kumar
Apache Spark's Tutorial talk, In this talk i explained how to start working with Apache spark, feature of apache spark and how to compose data platform with spark. This talk also explains about reactive platform, tools and framework like Play, akka.
Jump Start on Apache® Spark™ 2.x with Databricks Databricks
Apache Spark 2.0 and subsequent releases of Spark 2.1 and 2.2 have laid the foundation for many new features and functionality. Its main three themes—easier, faster, and smarter—are pervasive in its unified and simplified high-level APIs for Structured data.
In this introductory part lecture and part hands-on workshop, you’ll learn how to apply some of these new APIs using Databricks Community Edition. In particular, we will cover the following areas:
Agenda:
• Overview of Spark Fundamentals & Architecture
• What’s new in Spark 2.x
• Unified APIs: SparkSessions, SQL, DataFrames, Datasets
• Introduction to DataFrames, Datasets and Spark SQL
• Introduction to Structured Streaming Concepts
• Four Hands On Labs
You will use Databricks Community Edition, which will give you unlimited free access to a ~6 GB Spark 2.x local mode cluster. And in the process, you will learn how to create a cluster, navigate in Databricks, explore a couple of datasets, perform transformations and ETL, save your data as tables and parquet files, read from these sources, and analyze datasets using DataFrames/Datasets API and Spark SQL.
Level: Beginner to intermediate, not for advanced Spark users.
Prerequisite: You will need a laptop with Chrome or Firefox browser installed with at least 8 GB. Introductory or basic knowledge Scala or Python is required, since the Notebooks will be in Scala; Python is optional.
Bio:
Jules S. Damji is an Apache Spark Community Evangelist with Databricks. He is a hands-on developer with over 15 years of experience and has worked at leading companies, such as Sun Microsystems, Netscape, LoudCloud/Opsware, VeriSign, Scalix, and ProQuest, building large-scale distributed systems. Before joining Databricks, he was a Developer Advocate at Hortonworks.
SSR: Structured Streaming for R and Machine Learningfelixcss
Stepping beyond ETL in batches, large enterprises are looking at ways to generate more up-to-date insights. As we step into the age of Continuous Application, this session will explore the ever more popular Structure Streaming API in Apache Spark, its application to R, and building examples of machine learning use cases.
Starting with an introduction to the high-level concepts, the session will dive into the core of the execution plan internals and examine how SparkR extends the existing system to add the streaming capability. Learn how to build various data science applications on data streams integrating with R packages to leverage the rich R ecosystem of 10k+ packages.
Session hashtag: #SFdev2
NOTE: This was converted to Powerpoint from Keynote. Slideshare does not play the embedded videos. You can download the powerpoint from slideshare and import it into keynote. The videos should work in the keynote.
Abstract:
In this presentation, we will describe the "Spark Kernel" which enables applications, such as end-user facing and interactive applications, to interface with Spark clusters. It provides a gateway to define and run Spark tasks and to collect results from a cluster without the friction associated with shipping jars and reading results from peripheral systems. Using the Spark Kernel as a proxy, applications can be hosted remotely from Spark.
Lambda architecture for real time big dataTrieu Nguyen
Lambda Architecture in Real-time Big Data Project
Concepts & Techniques “Thinking with Lambda”
Case study in some real projects
Why lambda architecture is correct solution for big data?
Data processing platforms architectures with Spark, Mesos, Akka, Cassandra an...Anton Kirillov
This talk is about architecture designs for data processing platforms based on SMACK stack which stands for Spark, Mesos, Akka, Cassandra and Kafka. The main topics of the talk are:
- SMACK stack overview
- storage layer layout
- fixing NoSQL limitations (joins and group by)
- cluster resource management and dynamic allocation
- reliable scheduling and execution at scale
- different options for getting the data into your system
- preparing for failures with proper backup and patching strategies
Fast and Simplified Streaming, Ad-Hoc and Batch Analytics with FiloDB and Spa...Helena Edelson
O'Reilly Webcast with Myself and Evan Chan on the new SNACK Stack (playoff of SMACK) with FIloDB: Scala, Spark Streaming, Akka, Cassandra, FiloDB and Kafka.
NoLambda: Combining Streaming, Ad-Hoc, Machine Learning and Batch AnalysisHelena Edelson
Slides from my talk with Evan Chan at Strata San Jose: NoLambda: Combining Streaming, Ad-Hoc, Machine Learning and Batch Analysis. Streaming analytics architecture in big data for fast streaming, ad hoc and batch, with Kafka, Spark Streaming, Akka, Mesos, Cassandra and FiloDB. Simplifying to a unified architecture.
Hoodie: How (And Why) We built an analytical datastore on SparkVinoth Chandar
Exploring a specific problem of ingesting petabytes of data in Uber and why they ended up building an analytical datastore from scratch using Spark. Then, discuss design choices and implementation approaches in building Hoodie to provide near-real-time data ingestion and querying using Spark and HDFS.
https://spark-summit.org/2017/events/incremental-processing-on-large-analytical-datasets/
Lessons Learned from Managing Thousands of Production Apache Spark Clusters w...Databricks
At Databricks, we have a unique view into hundreds different companies using Apache Spark for development and production use-cases, from their support tickets and forum posts. Having seen so many different workflows and applications, some discernible patterns emerge when looking at common manageability, debugging, and visibility issues that our users run into. This talk will first show some representatives of these common issues. Then, we will show you what we have done and have been working on in Databricks to make Spark clusters easier to manage, monitor, and debug.
Bellevue Big Data meetup: Dive Deep into Spark StreamingSantosh Sahoo
Discuss the code and architecture about building realtime streaming application using Spark and Kafka. This demo presents some use cases and patterns of different streaming frameworks.
Reactive dashboard’s using apache sparkRahul Kumar
Apache Spark's Tutorial talk, In this talk i explained how to start working with Apache spark, feature of apache spark and how to compose data platform with spark. This talk also explains about reactive platform, tools and framework like Play, akka.
Jump Start on Apache® Spark™ 2.x with Databricks Databricks
Apache Spark 2.0 and subsequent releases of Spark 2.1 and 2.2 have laid the foundation for many new features and functionality. Its main three themes—easier, faster, and smarter—are pervasive in its unified and simplified high-level APIs for Structured data.
In this introductory part lecture and part hands-on workshop, you’ll learn how to apply some of these new APIs using Databricks Community Edition. In particular, we will cover the following areas:
Agenda:
• Overview of Spark Fundamentals & Architecture
• What’s new in Spark 2.x
• Unified APIs: SparkSessions, SQL, DataFrames, Datasets
• Introduction to DataFrames, Datasets and Spark SQL
• Introduction to Structured Streaming Concepts
• Four Hands On Labs
You will use Databricks Community Edition, which will give you unlimited free access to a ~6 GB Spark 2.x local mode cluster. And in the process, you will learn how to create a cluster, navigate in Databricks, explore a couple of datasets, perform transformations and ETL, save your data as tables and parquet files, read from these sources, and analyze datasets using DataFrames/Datasets API and Spark SQL.
Level: Beginner to intermediate, not for advanced Spark users.
Prerequisite: You will need a laptop with Chrome or Firefox browser installed with at least 8 GB. Introductory or basic knowledge Scala or Python is required, since the Notebooks will be in Scala; Python is optional.
Bio:
Jules S. Damji is an Apache Spark Community Evangelist with Databricks. He is a hands-on developer with over 15 years of experience and has worked at leading companies, such as Sun Microsystems, Netscape, LoudCloud/Opsware, VeriSign, Scalix, and ProQuest, building large-scale distributed systems. Before joining Databricks, he was a Developer Advocate at Hortonworks.
SSR: Structured Streaming for R and Machine Learningfelixcss
Stepping beyond ETL in batches, large enterprises are looking at ways to generate more up-to-date insights. As we step into the age of Continuous Application, this session will explore the ever more popular Structure Streaming API in Apache Spark, its application to R, and building examples of machine learning use cases.
Starting with an introduction to the high-level concepts, the session will dive into the core of the execution plan internals and examine how SparkR extends the existing system to add the streaming capability. Learn how to build various data science applications on data streams integrating with R packages to leverage the rich R ecosystem of 10k+ packages.
Session hashtag: #SFdev2
NOTE: This was converted to Powerpoint from Keynote. Slideshare does not play the embedded videos. You can download the powerpoint from slideshare and import it into keynote. The videos should work in the keynote.
Abstract:
In this presentation, we will describe the "Spark Kernel" which enables applications, such as end-user facing and interactive applications, to interface with Spark clusters. It provides a gateway to define and run Spark tasks and to collect results from a cluster without the friction associated with shipping jars and reading results from peripheral systems. Using the Spark Kernel as a proxy, applications can be hosted remotely from Spark.
Lambda architecture for real time big dataTrieu Nguyen
Lambda Architecture in Real-time Big Data Project
Concepts & Techniques “Thinking with Lambda”
Case study in some real projects
Why lambda architecture is correct solution for big data?
Big Data and Fast Data - Lambda Architecture in ActionGuido Schmutz
Big Data (volume) and real-time information processing (velocity) are two important aspects of Big Data systems. At first sight, these two aspects seem to be incompatible. Are traditional software architectures still the right choice? Do we need new, revolutionary architectures to tackle the requirements of Big Data?
This presentation discusses the idea of the so-called lambda architecture for Big Data, which acts on the assumption of a bisection of the data-processing: in a batch-phase a temporally bounded, large dataset is processed either through traditional ETL or MapReduce. In parallel, a real-time, online processing is constantly calculating the values of the new data coming in during the batch phase. The combination of the two results, batch and online processing is giving the constantly up-to-date view.
This talk presents how such an architecture can be implemented using Oracle products such as Oracle NoSQL, Hadoop and Oracle Event Processing as well as some selected products from the Open Source Software community. While this session mostly focuses on the software architecture of BigData and FastData systems, some lessons learned in the implementation of such a system are presented as well.
Conda is a cross-platform package manager that lets you quickly and easily build environments containing complicated software stacks. It was built to manage the NumPy stack in Python but can be used to manage any complex software dependencies.
Slides zur Session "Kostenlose Social Media Monitoring Tools" von Christine Heller (@punktefrau) u. Tim Krischak (@t_krischak) am 10.11.2012 auf dem Monitoring Camp in Hamburg.
http://monitoringcamp.de
http://punktefrau.de
http://kommunikation-zweinull.de
Building an Effective Data Warehouse ArchitectureJames Serra
Why use a data warehouse? What is the best methodology to use when creating a data warehouse? Should I use a normalized or dimensional approach? What is the difference between the Kimball and Inmon methodologies? Does the new Tabular model in SQL Server 2012 change things? What is the difference between a data warehouse and a data mart? Is there hardware that is optimized for a data warehouse? What if I have a ton of data? During this session James will help you to answer these questions.
Introduction to apache kafka, confluent and why they matterPaolo Castagna
This is a short and introductory presentation on Apache Kafka (including Kafka Connect APIs, Kafka Streams APIs, both part of Apache Kafka) and other open source components part of the Confluent platform (such as KSQL).
This was the first Kafka Meetup in South Africa.
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
In this talk at 2015 Spark Summit East, the lead developer of Spark streaming, @tathadas, talks about the state of Spark streaming:
Spark Streaming extends the core Apache Spark API to perform large-scale stream processing, which is revolutionizing the way Big “Streaming” Data application are being written. It is rapidly adopted by companies spread across various business verticals – ad and social network monitoring, real-time analysis of machine data, fraud and anomaly detections, etc. These companies are mainly adopting Spark Streaming because – Its simple, declarative batch-like API makes large-scale stream processing accessible to non-scientists. – Its unified API and a single processing engine (i.e. Spark core engine) allows a single cluster and a single set of operational processes to cover the full spectrum of uses cases – batch, interactive and stream processing. – Its stronger, exactly-once semantics makes it easier to express and debug complex business logic. In this talk, I am going to elaborate on such adoption stories, highlighting interesting use cases of Spark Streaming in the wild. In addition, this presentation will also showcase the exciting new developments in Spark Streaming and the potential future roadmap.
My talk at Data Science Labs conference in Odessa.
Training a model in Apache Spark while having it automatically available for real-time serving is an essential feature for end-to-end solutions.
There is an option to export the model into PMML and then import it into a separated scoring engine. The idea of interoperability is great but it has multiple challenges, such as code duplication, limited extensibility, inconsistency, extra moving parts. In this talk we discussed an alternative solution that does not introduce custom model formats and new standards, not based on export/import workflow and shares Apache Spark API.
OpenSource API Server based on Node.js API framework built on supported Node.js platform with Tooling and DevOps. Use cases are Omni-channel API Server, Mobile Backend as a Service (mBaaS) or Next Generation Enterprise Service Bus. Key functionality include built in enterprise connectors, ORM, Offline Sync, Mobile and JS SDKs, Isomorphic JavaScript and Graphical API creation tool.
AWS Lambda allows any Node.js app to be run at scale in a massively parallel environment with no up-front costs or planning. This session shows how to use Lambda to build dynamic analytic data flows that can be tuned as they execute, based on initial results, to provide real-time output streamed to web clients. This process enables a cost-effective and responsive user experience for ad hoc big data jobs and lets developers focus on how data is consumed and presented, instead of how it is obtained.
Webinar: Serverless Architectures with AWS Lambda and MongoDB AtlasMongoDB
It’s easier than ever to power serverless architectures with our managed MongoDB as a service, MongoDB Atlas. In this session, we will explore the rise of serverless architectures and how they’ve rapidly integrated into public and private cloud offerings.
Serverless Architectural Patterns & Best PracticesDaniel Zivkovic
This ServerlessTO meetup covered various Serverless design patterns and best practices for building apps using the full #AWS #Serverless stack - not just Lambda. Event recording (including 25min long Q&A!) is at https://youtu.be/gsILTMXPUeU
Unified Big Data Processing with Apache Spark (QCON 2014)Databricks
While early big data systems, such as MapReduce, focused on batch processing, the demands on these systems have quickly grown. Users quickly needed to run (1) more interactive ad-hoc queries, (2) sophisticated multi-pass algorithms (e.g. machine learning), and (3) real-time stream processing. The result has been an explosion of specialized systems to tackle these new workloads. Unfortunately, this means more systems to learn, manage, and stitch together into pipelines. Spark is unique in taking a step back and trying to provide a *unified* post-MapReduce programming model that tackles all these workloads. By generalizing MapReduce to support fast data sharing and low-latency jobs, we achieve best-in-class performance in a variety of workloads, while providing a simple programming model that lets users easily and efficiently combine them.
Today, Spark is the most active open source project in big data, with high activity in both the core engine and a growing array of standard libraries built on top (e.g. machine learning, stream processing, SQL). I'm going to talk about the latest developments in Spark and show examples of how it can combine processing algorithms to build rich data pipelines in just a few lines of code.
Talk by Databricks CTO and Apache Spark creator Matei Zaharia at QCON San Francisco 2014.
Unified Big Data Processing with Apache SparkC4Media
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1yNuLGF.
Matei Zaharia talks about the latest developments in Spark and shows examples of how it can combine processing algorithms to build rich data pipelines in just a few lines of code. Filmed at qconsf.com.
Matei Zaharia is an assistant professor of computer science at MIT, and CTO of Databricks, the company commercializing Apache Spark.
Posons-nous et profitons de ce talk pour prendre un peu de hauteur sur l’état de l’industrie tech autour de la création d’API de persistence (CRUD).
D’où venons-nous, ou allons-nous ? Pourquoi le choix entre RPC, SOAP, REST et GraphQL n’est peut-être qu’un sujet de surface qui cache un problème bien plus profond…
Youtube: https://www.youtube.com/watch?v=IskE3m3VjRY
Spark (Structured) Streaming vs. Kafka StreamsGuido Schmutz
Independent of the source of data, the integration and analysis of event streams gets more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analyzed, often with many consumers or systems interested in all or part of the events. In this session we compare two popular Streaming Analytics solutions: Spark Streaming and Kafka Streams.
Spark is fast and general engine for large-scale data processing and has been designed to provide a more efficient alternative to Hadoop MapReduce. Spark Streaming brings Spark's language-integrated API to stream processing, letting you write streaming applications the same way you write batch jobs. It supports both Java and Scala.
Kafka Streams is the stream processing solution which is part of Kafka. It is provided as a Java library and by that can be easily integrated with any Java application.
This presentation shows how you can implement stream processing solutions with each of the two frameworks, discusses how they compare and highlights the differences and similarities.
Strata NYC 2015: What's new in Spark StreamingDatabricks
As the adoption of Spark Streaming in the industry is increasing, so is the community’s demand for more features. Since the beginning of this year, we have made significant improvements in performance, usability, and semantic guarantees. In particular, some of these features are:
- New Kafka integration for exactly-once guarantees
- Improved Kinesis integration for stronger guarantees
- Addition of more sources to the Python API
Significantly improved UI for greater monitoring and debuggability.
In this talk, I am going to discuss these improvements as well as the plethora of features we plan to add in the near future.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
3. Topics
About Me & LINE
Data analytics workloads
Batch processing
Stream processing
Lambda architecture
Lambda architecture using SQL
Norikra: Stream processing with SQL
13:30-14:20 4F
11. Various Data Analytics Workload
Reports
Monthly/Daily reports
Hourly (or shorter) news
Real-time metrics
Automatically updated reports/graphs
Alerts for abuse of services, overload, ...
12.
13. Batch Processing
Hadoop
MapReduce (or Spark, Tez) & DSLs (Hive, Pig, ...)
For reports
MPP Engines
Cloudera Impala, Apache Drill, Facebook Presto, ...
For interactive analysis
For reports of shorter window
14. Stream Processing
Apache Storm
Incubator project
“Distributed and fault-tolerant realtime computation”
Norikra
by tagomoris
Non-distributed “Stream processing with SQL”
15. Why Stream Processing?
Less latency
Realtime metrics
Short-term prompt reports
Less computing power
10Mbps for batch processing: 100GB/day
10Mbps for stream processing: 1 Server
No query schedule management
Once query registered, it runs forever
16. Disadvantage of Stream Processing
Queries must be written before data
There should be another way to query past data
Queries cannot be run twice
All results will be lost when any error occurs
All data have gone when bugs found
Disorders of events break results
Recorded time based queries? Or arrival time based queries?
18. Lambda Architecture
“The Lambda-architecture aims to satisfy the needs for a
robust system that is fault-tolerant, both against hardware
failures and human mistakes, being able to serve a wide
range of workloads and use cases, and in which low-latency
reads and updates are required. The resulting system should
be linearly scalable, and it should scale out rather than up.”
http://lambda-architecture.net/
19. Lambda Architecture: Overview
new data
batch layer
master dataset
serving layer
view
speed layer
real-time view
query
21. What Lambda Architecture Provides
Replayable queries
Redo queries anytime if results of speed layer are broken
Accurate results on demand
Prompt reports in speed layer with arrival time
Fixed reports in batch layer with recorded time
... And many more benefits of stream processing
22. Why All of Us Don’t Use It?
Storm doesn’t fit well with many uses
Storm requires computer resources too big to deploy
Summingbird requires many steps to deploy
Many directors/analysts don’t write Scala/Java
Summingbird DSL is not enough easy for non-professional
people
25. Norikra
Schema-less stream processing with SQL
“Norikra is a open source server software provides "Stream
Processing" with SQL, written in JRuby, runs on JVM, licensed
under GPLv2.”
SELECT
path,
COUNT(1, status=200) AS success_count,
COUNT(1, status=500) AS server_error_count,
COUNT(*) AS count
FROM AccessLog.win:time_batch(10 min, 0L)
WHERE service='myservice' AND path LIKE '/api/%'
GROUP BY path
http://norikra.github.io/
27. “Pseudo Lambda” Architecture Using SQL
Lambda architecture platform
with almost same queries
SELECT path,
COUNT(IF(status=200,1,NULL)) AS success_count,
COUNT(IF(status=500,1,NULL)) AS server_error_count,
COUNT(*) AS count
FROM AccessLog
WHERE service='myservice' AND path LIKE '/api/%'
AND timestamp >= ‘2014-09-13 10:40:00’
AND timestamp < ‘2014-09-13 10:50:00’
GROUP BY path
SELECT path,
COUNT(1, status=200) AS success_count,
COUNT(1, status=500) AS server_error_count,
COUNT(*) AS count
FROM AccessLog.win:time_batch(10 min, 0L)
WHERE service='myservice' AND path LIKE '/api/%'
GROUP BY path
28. “Pseudo Lambda” Architecture Using SQL
SQL dialects are easy to learn!
Standard SQL, Hive, Presto, Impala, Drill, ...
+ Norikra
For non-professional people too!
SQL queries are very easy to write twice!
29. Use Cases in LINE
Prompt reports for Ads service
Short-term prompt reports by Norikra
Daily fixed reports by Hive
Summary of application server error log
Aggregate error log for alerting by Norikra
Check details with Hive, Presto (or grep!)
See you later for details!