An Engine to process big data in faster(than MR), easy and extremely scalable way. An Open Source, parallel, in-memory processing, cluster computing framework. Solution for loading, processing and end to end analyzing large scale data. Iterative and Interactive : Scala, Java, Python, R and with Command line interface.
This presentation will be useful to those who would like to get acquainted with Apache Spark architecture, top features and see some of them in action, e.g. RDD transformations and actions, Spark SQL, etc. Also it covers real life use cases related to one of ours commercial projects and recall roadmap how we’ve integrated Apache Spark into it.
Was presented on Morning@Lohika tech talks in Lviv.
Design by Yarko Filevych: http://www.filevych.com/
This slide introduces Hadoop Spark.
Just to help you construct an idea of Spark regarding its architecture, data flow, job scheduling, and programming.
Not all technical details are included.
This presentation will be useful to those who would like to get acquainted with Apache Spark architecture, top features and see some of them in action, e.g. RDD transformations and actions, Spark SQL, etc. Also it covers real life use cases related to one of ours commercial projects and recall roadmap how we’ve integrated Apache Spark into it.
Was presented on Morning@Lohika tech talks in Lviv.
Design by Yarko Filevych: http://www.filevych.com/
This slide introduces Hadoop Spark.
Just to help you construct an idea of Spark regarding its architecture, data flow, job scheduling, and programming.
Not all technical details are included.
In this slidecast, Alex Gorbachev from Pythian presents a Practical Introduction to Hadoop. This is a great primer for viewers who want to get the big picture on how Hadoop works with Big Data and how this approach differs from relational databases.
Watch the presentation: http://inside-bigdata.com/slidecast-a-practical-introduction-to-hadoop/
Download the audio:
Introduction to Apache Spark. With an emphasis on the RDD API, Spark SQL (DataFrame and Dataset API) and Spark Streaming.
Presented at the Desert Code Camp:
http://oct2016.desertcodecamp.com/sessions/all
Real time Analytics with Apache Kafka and Apache SparkRahul Jain
A presentation cum workshop on Real time Analytics with Apache Kafka and Apache Spark. Apache Kafka is a distributed publish-subscribe messaging while other side Spark Streaming brings Spark's language-integrated API to stream processing, allows to write streaming applications very quickly and easily. It supports both Java and Scala. In this workshop we are going to explore Apache Kafka, Zookeeper and Spark with a Web click streaming example using Spark Streaming. A clickstream is the recording of the parts of the screen a computer user clicks on while web browsing.
How Apache Spark fits into the Big Data landscapePaco Nathan
Boulder/Denver Spark Meetup, 2014-10-02 @ Datalogix
http://www.meetup.com/Boulder-Denver-Spark-Meetup/events/207581832/
Apache Spark is intended as a general purpose engine that supports combinations of Batch, Streaming, SQL, ML, Graph, etc., for apps written in Scala, Java, Python, Clojure, R, etc.
This talk provides an introduction to Spark — how it provides so much better performance, and why — and then explores how Spark fits into the Big Data landscape — e.g., other systems with which Spark pairs nicely — and why Spark is needed for the work ahead.
Your data is getting bigger while your boss is getting anxious to have insights! This tutorial covers Apache Spark that makes data analytics fast to write and fast to run. Tackle big datasets quickly through a simple API in Python, and learn one programming paradigm in order to deploy interactive, batch, and streaming applications while connecting to data sources incl. HDFS, Hive, JSON, and S3.
SF Big Analytics_20190612: Scaling Apache Spark on Kubernetes at LyftChester Chen
Talk 1. Scaling Apache Spark on Kubernetes at Lyft
As part of this mission Lyft invests heavily in open source infrastructure and tooling. At Lyft Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark at Lyft has evolved to solve both Machine Learning and large scale ETL workloads. By combining the flexibility of Kubernetes with the data processing power of Apache Spark, Lyft is able to drive ETL data processing to a different level. In this talk, We will talk about challenges the Lyft team faced and solutions they developed to support Apache Spark on Kubernetes in production and at scale. Topics Include: - Key traits of Apache Spark on Kubernetes. - Deep dive into Lyft's multi-cluster setup and operationality to handle petabytes of production data. - How Lyft extends and enhances Apache Spark to support capabilities such as Spark pod life cycle metrics and state management, resource prioritization, and queuing and throttling. - Dynamic job scale estimation and runtime dynamic job configuration. - How Lyft powers internal Data Scientists, Business Analysts, and Data Engineers via a multi-cluster setup.
Speaker: Li Gao
Li Gao is the tech lead in the cloud native spark compute initiative at Lyft. Prior to Lyft, Li worked at Salesforce, Fitbit, Marin Software, and a few startups etc. on various technical leadership positions on cloud native and hybrid cloud data platforms at scale. Besides Spark, Li has scaled and productionized other open source projects, such as Presto, Apache HBase, Apache Phoenix, Apache Kafka, Apache Airflow, Apache Hive, and Apache Cassandra.
Apache Spark: The Next Gen toolset for Big Data Processingprajods
The Spark project from Apache(spark.apache.org), is the next generation of Big Data processing systems. It uses a new architecture and in-memory processing for orders of magnitude improvement in performance. Some would call it the successor to the Hadoop set of tools. Hadoop is a batch mode Big Data processor and depends on disk based files. Spark improves on this and supports real time and interactive processing, in addition to batch processing.
Table of contents:
1. The Big Data triangle
2. Hadoop stack and its limitations
3. Spark: An Overview
3.a. Spark Streaming
3.b. GraphX: Graph processing
3.c. MLib: Machine Learning
4. Performance characteristics of Spark
What Is Apache Spark? | Introduction To Apache Spark | Apache Spark Tutorial ...Simplilearn
This presentation about Apache Spark covers all the basics that a beginner needs to know to get started with Spark. It covers the history of Apache Spark, what is Spark, the difference between Hadoop and Spark. You will learn the different components in Spark, and how Spark works with the help of architecture. You will understand the different cluster managers on which Spark can run. Finally, you will see the various applications of Spark and a use case on Conviva. Now, let's get started with what is Apache Spark.
Below topics are explained in this Spark presentation:
1. History of Spark
2. What is Spark
3. Hadoop vs Spark
4. Components of Apache Spark
5. Spark architecture
6. Applications of Spark
7. Spark usecase
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
Simplilearn’s Apache Spark and Scala certification training are designed to:
1. Advance your expertise in the Big Data Hadoop Ecosystem
2. Help you master essential Apache and Spark skills, such as Spark Streaming, Spark SQL, machine learning programming, GraphX programming and Shell Scripting Spark
3. Help you land a Hadoop developer job requiring Apache Spark expertise by giving you a real-life industry project coupled with 30 demos
What skills will you learn?
By completing this Apache Spark and Scala course you will be able to:
1. Understand the limitations of MapReduce and the role of Spark in overcoming these limitations
2. Understand the fundamentals of the Scala programming language and its features
3. Explain and master the process of installing Spark as a standalone cluster
4. Develop expertise in using Resilient Distributed Datasets (RDD) for creating applications in Spark
5. Master Structured Query Language (SQL) using SparkSQL
6. Gain a thorough understanding of Spark streaming features
7. Master and describe the features of Spark ML programming and GraphX programming
Who should take this Scala course?
1. Professionals aspiring for a career in the field of real-time big data analytics
2. Analytics professionals
3. Research professionals
4. IT developers and testers
5. Data scientists
6. BI and reporting professionals
7. Students who wish to gain a thorough understanding of Apache Spark
Learn more at https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training
we will see an overview of Spark in Big Data. We will start with an introduction to Apache Spark Programming. Then we will move to know the Spark History. Moreover, we will learn why Spark is needed. Afterward, will cover all fundamental of Spark components. Furthermore, we will learn about Spark’s core abstraction and Spark RDD. For more detailed insights, we will also cover spark features, Spark limitations, and Spark Use cases.
Quick overview of what Spark is and how we use it at Viadeo with Mesos.
Presenting also two concrete applications of Spark at Viadeo:
Predicting click on job offers in emails and building our Member Segmentation & Targeting platform.
This session covers how to work with PySpark interface to develop Spark applications. From loading, ingesting, and applying transformation on the data. The session covers how to work with different data sources of data, apply transformation, python best practices in developing Spark Apps. The demo covers integrating Apache Spark apps, In memory processing capabilities, working with notebooks, and integrating analytics tools into Spark Applications.
In this slidecast, Alex Gorbachev from Pythian presents a Practical Introduction to Hadoop. This is a great primer for viewers who want to get the big picture on how Hadoop works with Big Data and how this approach differs from relational databases.
Watch the presentation: http://inside-bigdata.com/slidecast-a-practical-introduction-to-hadoop/
Download the audio:
Introduction to Apache Spark. With an emphasis on the RDD API, Spark SQL (DataFrame and Dataset API) and Spark Streaming.
Presented at the Desert Code Camp:
http://oct2016.desertcodecamp.com/sessions/all
Real time Analytics with Apache Kafka and Apache SparkRahul Jain
A presentation cum workshop on Real time Analytics with Apache Kafka and Apache Spark. Apache Kafka is a distributed publish-subscribe messaging while other side Spark Streaming brings Spark's language-integrated API to stream processing, allows to write streaming applications very quickly and easily. It supports both Java and Scala. In this workshop we are going to explore Apache Kafka, Zookeeper and Spark with a Web click streaming example using Spark Streaming. A clickstream is the recording of the parts of the screen a computer user clicks on while web browsing.
How Apache Spark fits into the Big Data landscapePaco Nathan
Boulder/Denver Spark Meetup, 2014-10-02 @ Datalogix
http://www.meetup.com/Boulder-Denver-Spark-Meetup/events/207581832/
Apache Spark is intended as a general purpose engine that supports combinations of Batch, Streaming, SQL, ML, Graph, etc., for apps written in Scala, Java, Python, Clojure, R, etc.
This talk provides an introduction to Spark — how it provides so much better performance, and why — and then explores how Spark fits into the Big Data landscape — e.g., other systems with which Spark pairs nicely — and why Spark is needed for the work ahead.
Your data is getting bigger while your boss is getting anxious to have insights! This tutorial covers Apache Spark that makes data analytics fast to write and fast to run. Tackle big datasets quickly through a simple API in Python, and learn one programming paradigm in order to deploy interactive, batch, and streaming applications while connecting to data sources incl. HDFS, Hive, JSON, and S3.
SF Big Analytics_20190612: Scaling Apache Spark on Kubernetes at LyftChester Chen
Talk 1. Scaling Apache Spark on Kubernetes at Lyft
As part of this mission Lyft invests heavily in open source infrastructure and tooling. At Lyft Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark at Lyft has evolved to solve both Machine Learning and large scale ETL workloads. By combining the flexibility of Kubernetes with the data processing power of Apache Spark, Lyft is able to drive ETL data processing to a different level. In this talk, We will talk about challenges the Lyft team faced and solutions they developed to support Apache Spark on Kubernetes in production and at scale. Topics Include: - Key traits of Apache Spark on Kubernetes. - Deep dive into Lyft's multi-cluster setup and operationality to handle petabytes of production data. - How Lyft extends and enhances Apache Spark to support capabilities such as Spark pod life cycle metrics and state management, resource prioritization, and queuing and throttling. - Dynamic job scale estimation and runtime dynamic job configuration. - How Lyft powers internal Data Scientists, Business Analysts, and Data Engineers via a multi-cluster setup.
Speaker: Li Gao
Li Gao is the tech lead in the cloud native spark compute initiative at Lyft. Prior to Lyft, Li worked at Salesforce, Fitbit, Marin Software, and a few startups etc. on various technical leadership positions on cloud native and hybrid cloud data platforms at scale. Besides Spark, Li has scaled and productionized other open source projects, such as Presto, Apache HBase, Apache Phoenix, Apache Kafka, Apache Airflow, Apache Hive, and Apache Cassandra.
Apache Spark: The Next Gen toolset for Big Data Processingprajods
The Spark project from Apache(spark.apache.org), is the next generation of Big Data processing systems. It uses a new architecture and in-memory processing for orders of magnitude improvement in performance. Some would call it the successor to the Hadoop set of tools. Hadoop is a batch mode Big Data processor and depends on disk based files. Spark improves on this and supports real time and interactive processing, in addition to batch processing.
Table of contents:
1. The Big Data triangle
2. Hadoop stack and its limitations
3. Spark: An Overview
3.a. Spark Streaming
3.b. GraphX: Graph processing
3.c. MLib: Machine Learning
4. Performance characteristics of Spark
What Is Apache Spark? | Introduction To Apache Spark | Apache Spark Tutorial ...Simplilearn
This presentation about Apache Spark covers all the basics that a beginner needs to know to get started with Spark. It covers the history of Apache Spark, what is Spark, the difference between Hadoop and Spark. You will learn the different components in Spark, and how Spark works with the help of architecture. You will understand the different cluster managers on which Spark can run. Finally, you will see the various applications of Spark and a use case on Conviva. Now, let's get started with what is Apache Spark.
Below topics are explained in this Spark presentation:
1. History of Spark
2. What is Spark
3. Hadoop vs Spark
4. Components of Apache Spark
5. Spark architecture
6. Applications of Spark
7. Spark usecase
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
Simplilearn’s Apache Spark and Scala certification training are designed to:
1. Advance your expertise in the Big Data Hadoop Ecosystem
2. Help you master essential Apache and Spark skills, such as Spark Streaming, Spark SQL, machine learning programming, GraphX programming and Shell Scripting Spark
3. Help you land a Hadoop developer job requiring Apache Spark expertise by giving you a real-life industry project coupled with 30 demos
What skills will you learn?
By completing this Apache Spark and Scala course you will be able to:
1. Understand the limitations of MapReduce and the role of Spark in overcoming these limitations
2. Understand the fundamentals of the Scala programming language and its features
3. Explain and master the process of installing Spark as a standalone cluster
4. Develop expertise in using Resilient Distributed Datasets (RDD) for creating applications in Spark
5. Master Structured Query Language (SQL) using SparkSQL
6. Gain a thorough understanding of Spark streaming features
7. Master and describe the features of Spark ML programming and GraphX programming
Who should take this Scala course?
1. Professionals aspiring for a career in the field of real-time big data analytics
2. Analytics professionals
3. Research professionals
4. IT developers and testers
5. Data scientists
6. BI and reporting professionals
7. Students who wish to gain a thorough understanding of Apache Spark
Learn more at https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training
we will see an overview of Spark in Big Data. We will start with an introduction to Apache Spark Programming. Then we will move to know the Spark History. Moreover, we will learn why Spark is needed. Afterward, will cover all fundamental of Spark components. Furthermore, we will learn about Spark’s core abstraction and Spark RDD. For more detailed insights, we will also cover spark features, Spark limitations, and Spark Use cases.
Quick overview of what Spark is and how we use it at Viadeo with Mesos.
Presenting also two concrete applications of Spark at Viadeo:
Predicting click on job offers in emails and building our Member Segmentation & Targeting platform.
This session covers how to work with PySpark interface to develop Spark applications. From loading, ingesting, and applying transformation on the data. The session covers how to work with different data sources of data, apply transformation, python best practices in developing Spark Apps. The demo covers integrating Apache Spark apps, In memory processing capabilities, working with notebooks, and integrating analytics tools into Spark Applications.
Apache Spark presentation at HasGeek FifthElelephant
https://fifthelephant.talkfunnel.com/2015/15-processing-large-data-with-apache-spark
Covering Big Data Overview, Spark Overview, Spark Internals and its supported libraries
Big Data Processing with Apache Spark 2014mahchiev
Apache Spark™ is a fast and general engine for large-scale data processing. It has gained enormous popularity recently with its speed and ease of use and is currently replacing traditional Hadoop MapReduce. We'll talk about:
1. What is Big Data ?
2. The Map-Reduce paradigm
3. What does Apache Spark do?
4. Finally, we'll make a quick demo
A gentle introduction to Apache Spark from the theorem of Resilient Distributed Datasets to deploying software to the core platform, Spark Streaming, and Spark SQL
Introduction to Apache Spark. With an emphasis on the RDD API, Spark SQL (DataFrame and Dataset API) and Spark Streaming.
Presented at the Desert Code Camp:
http://oct2016.desertcodecamp.com/sessions/all
In this one day workshop, we will introduce Spark at a high level context. Spark is fundamentally different than writing MapReduce jobs so no prior Hadoop experience is needed. You will learn how to interact with Spark on the command line and conduct rapid in-memory data analyses. We will then work on writing Spark applications to perform large cluster-based analyses including SQL-like aggregations, machine learning applications, and graph algorithms. The course will be conducted in Python using PySpark.
This introductory workshop is aimed at data analysts & data engineers new to Apache Spark and exposes them how to analyze big data with Spark SQL and DataFrames.
In this partly instructor-led and self-paced labs, we will cover Spark concepts and you’ll do labs for Spark SQL and DataFrames
in Databricks Community Edition.
Toward the end, you’ll get a glimpse into newly minted Databricks Developer Certification for Apache Spark: what to expect & how to prepare for it.
* Apache Spark Basics & Architecture
* Spark SQL
* DataFrames
* Brief Overview of Databricks Certified Developer for Apache Spark
Container technology is shaping the future of software development and is causing a structural change in the cloud-computing world. Developers are embracing container technology and enterprises are adopting it at an explosive rate. Containers are portion of "IT" in technology as they're a very powerful tool which streamline your development and ops processes, save company's money & make life for developers much easier.
Many organizations are embracing the latest practices for DevOps agility and cloud innovations to manage their heterogeneous environments (Hybrid DC). Yet they are also concerned about their ability to make appropriate and responsible decisions about how to monitor those workloads. How to monitor application and infrastructure in a centralized location?
Enterprises need to manage these resources being provisioned as well as define a consistent methodology for adopting the cloud. Management refers to the tasks and processes required to maintain business applications and the resources that support them.
Azure Governance, one of the aspects of Azure Management, has various concepts and services that are designed to enable management of various Azure resources. Understanding the different available tools for a variety of management scenarios becomes necessary.
1. Overview of DevOps
2. Infrastructure as Code (IaC) and Configuration as code
3. Identity and Security protection in CI CD environment
4. Monitor Health of the Infrastructure/Application
5. Open Source Software (OSS) and third-party tools, such as Chef, Puppet, Ansible, and Terraform to achieve DevOps.
6. Future of DevOps Application
This year our Synergetics has decided to have a Social Media Contest “The Social Synergetician”, the winner of which will win a FASTRACK REFLEX 2.0 HEALTH BAND.
Undoubtedly, the winner would be the one achieving the highest number of Followers, likes and shares on Social Media.
Build your MICROSOFT AZURE expertise to jumpstart your career, Adding a Microsoft certification to your resume helps you stand out and get hired—faster.
In this Digital Transformation note get to know,
Whats Digital Transformation
Gains of Digital Transformation
Know how Digital Transformation is impacting Industry
We are country’s premium Solutions Organization: Awarded by Microsoft – Masters Service Agreement for Driving Solution Centric Engagements on Azure/ SPS / O365 / SQL Server. We are a Specialist Training & Consulting services company with over 20+ year of success story. The Depth of Deliverables is such that our services are banked by Top 15 SI and ISV for developing and driving SMAC competency within their Organization. By Far we are the “BEST “Microsoft – Azure, Sales and Pre-sales Competency Development Organization having delivered more than 50+ Sessions Globally.
Described as “Solutions Partner” by Microsoft in its Partner ecosystem
Since 1993 a Hi –End Technology Solutions Company
Leading Microsoft’s Azure circle Partner Program across Western Zone :: A4
Awarded Master Service Agreement by Microsoft for Delivering Solution Centric & Consulting Engagements – Structured Roadmap Rollouts for India’s TOP SIs and ISVs
Aspiring Architects Program “ASAP”
Azure Architecting and Technical Readiness
Azure IT Pro Technical Readiness
SQL Server 2012 Architecting and Technical Readiness
SharePoint 2013 Architecting and Technical Readiness
Microsoft’s Online Technology Suite Architecting and Technical Readiness
Microsoft Azure & O365 Sales & Presales – Business Value Prop and Technical Value Prop Readiness
Azure Circle Partner ; launched Azure Global Boot Camp in India Mumbai
Since 1993 a Corporate “Developer” Training Solutions Company
Described as “Solutions Partner” by Microsoft in its Partner Eco System
Awarded Master Service Agreement by Microsoft for Delivering Solution Centric Engagements – Roadmap Rollouts
Aspiring Architects Program “ASAP”
Azure Technical Readiness and Architecting
SQL Server 2012 Technical Readiness and Architecting
SharePoint 2013 Technical Readiness and Architecting
Microsoft’s Online Technology Suite Technical Readiness and Architecting
Azure Circle Partner ; launched Azure Global Boot Camp in India Mumbai
Services Portfolio mapped for Onboarding New Talent as well as Skilling Aspiring Architects
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.
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.
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.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
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.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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.
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.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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.
3. Apache Spark Overview
• An Engine to process big data in faster(than MR), easy and extremely scalable way
• An Open Source, parallel, in-memory processing, cluster computing framework
• Solution for loading, processing and end to end analyzing large scale data
• Iterative and Interactive : Scala, Java, Python, R and with Command line interface
• Stream Processing (Real time streams and DStreams)
• Unifies Big Data with Batch processing, Streaming and Machine Learning
• Appreciated and Widely used by: Amazon, eBay, Yahoo
• Can very well go with Apache Kafka, ZeroMQ, Cassandra etc.
• Powerful platform to implement Lambda and Kappa Architecture
5. Spark - Benefits
5
Performance
Using in-memory computing, Spark is
considerably faster than Hadoop (100x in
some tests).
Can be used for batch and real-time data
processing.
Developer Productivity
Easy-to-use APIs for processing large
datasets.
Includes 100+ operators for transforming.
Ecosystem
Spark has built-in support for many data
sources such as HDFS, RDBMS, S3, Apache
Hive, Cassandra and MongoDB.
Runs on top the Apache YARN resource
manager.
Unified Engine
Integrated framework includes higher-level
libraries for interactive SQL queries,
processing streaming data, machine learning
and graph processing.
A single application can combine all types of
processing
6. Spark is fast
6
Spark is the current (2014) Sort Benchmark winner.
3x faster than 2013 winner (Hadoop).
tinyurl.com/spark-sort
7. … especially for iterative applications
7Logistic regression on a 100-node cluster with 100 GB of data
Logistic Regression
140
120
100
80
40
20
0
60
Hadoop
Spark 0.9
8. Tuples of MR Vs. RDDs of Spark
Tuples
in Map
Reduce
RDDs in
Spark
9. Spark in-memory
• Spark does all intermediate steps in-memory…
• Faster in execution with fewer Secondary Storage r/w.
• Memory extensive
• The memory objects are called as Resilient Distributed Datasets.
• RDD’s are partitioned memory objects existing in multiple worker machines along with their
replicas.
10. A unified Framework
10
An unified, open source, parallel, data processing framework for Big Data Analytics
Spark Core Engine
Spark SQL
Interactive
Queries
Spark
Streaming
Stream processing
Spark MLlib
Machine
Learning
GraphX
Graph
Computation
Yarn Mesos
Standalone
Scheduler
14. Spark Modes
• Batch mode: A scheduled program executed through scheduler in periodic
manner to process data.
• Interactive mode: Execute spark commands through Spark Interactive command
interface.
• The Shell provides default Spark Context and works as a Driver Program. The Spark context
runs tasks on Cluster.
• Stream mode: Process stream data in real time fashion.
15. Spark Scalability
Single Cluster, Stand-alone,
Single Box
• All components (Driver,
Executors) run within same
JVM.
• Partitions data for multiple
core.
• Runs as Single Threaded mode.
Managed Clustered
• Can scale from 2 to 1000
nodes.
• Can use different cluster
managers like- YARN, MESOS
etc.
• Partitions data for all nodes.
16. Area of applicability
• Data Integration and ETL
• Interactive Analytics
• High performance batch and micro-batch computations
• Advanced and complex analytics
• Machine learning
• Real time stream processing including IoT
• Example:
• Market trends and patterns
• Predicting sales
• Credit card frauds detection
• Network intrusion detection
• Advertisement targeting
• Customer’s 360 analysis
17. Spark – Use cases
17
Use case Description Users
Data Integration and
ETL
Cleansing and combining data from
diverse sources
Palantir: Data analytics platform
Interactive analytics
Gain insight from massive data sets tin
ad hoc investigations or regularly
planned dashboards.
Goldman Sachs: Analytics platform
Huawei: Query platform in the
telecom sector.
High performance
batch computation
Run complex algorithms against large
scale data
Novartis: Genomic Research
MyFitnessPal: Process food data
Machine Learning
Predict outcomes to make decisions
based on input data
Alibaba: Marketplace Analysis
Spotify: Music Recommendation
Real-time stream
processing
Capturing and processing data
continuously with low latency and high
reliability
Netflix: Recommendation Engine
British Gas: Connected Homes
19. Spark Architecture
• Spark Master: Manages number of applications. In
HDInsight, it also manages resources at cluster level.
• Spark Driver: Per application to manage workflow of an
application.
• Spark Context: Created by driver and keeps track and
metadata of RDDs. Gives API to exercise various
features of Spark.
• Worker Node: Read and write data from and to
HDFS/Storage.
20. Spark Execution components
• Driver Program
• It’s a main initiating program from where Spark operations are defined. It is executed in
Master Node.
• It controls and co-ordinates all operations.
• It defines RDDs.
• Each driver program execution is a ‘Job’.
21. Spark Execution components
• Spark Context
• Provides access to Spark functionalities.
• Represents connection to the computing cluster.
• Builds, partitions and distributes RDDs to clusters.
• Works with Cluster Manager
• Splits job as parallel task and executes them on worker nodes
• Collects and accumulates results and present them to the driver program
22. Resilient Distributed Datasets
• Operations with spark are mostly with RDDs. We create, transform, analyze and store RDDs in
Spark operations.
• RDDs are fast in access as stored in memory.
• Partitioned and Distributed as divided in parts and each part exist in a cluster.
• The data sets are formed of Strings, rows, objects, collection.
• They are immutable.
• To change, apply transformation and create new RDD.
• They can be cached and persisted.
• Actions produce summarized results.
23. Demo 1
TestSpark010_FirstProgram.java
Aim: This program basically demonstrates...
1. Configuring Spark context.
2. Using Resilient Distributed Datasets.
3. Introduction to Mappings, Filtering, Actions etc.
4. Creating String RDD from CSV text file.
5. Applying simple Map to convert strings to Upper case
6. Browser monitoring of Spark Context.
26. Loading data.
• RDD Loading sources…
• Text files
• JSON files
• Sequence files of HDFS
• Parallelize on collection
• Java Collection
• Python Lists
• R data frames
• RDBMS/NoSQL
• Use direct API
• Bring it first as Collection (DAO Classes) and then create RDD.
• Very large data sets
• Create HDFS out side spark and then create RDDs using Apache Sqoop.
• Spark aligns its own partitioning techniques to the partitioning of Hadoop.
27. Storing data
• Storing of RDD in variety of data sinks.
• Text files
• JSON
• Sequence Files
• Collection
• RDBMS/NoSQL
• For persistence
• Spark Utilities
• Language specific support
Spark power lies in processing data in distribute manner.
Though Spark provides API for Loading and sinking of data,
here where its real power does not lay. Out side capabilities
are recommended for simplicity and performance.
28. Lazy Evaluation
• Spark will not load or transform data unless action is encountered.
• Step 1: Load a file content into RDD.
• Step 2: Apply filtration
• Step 3: Count for number of records (Now Step 1 to 3 are executed).
• The above statement is true even for interactive mode.
• The lazy evaluation helps spark to optimize operations and manage resources in
better way.
• Makes trouble shooting difficult: Any problem in loading is detected while
executing Action.
29. Transformations
• Recall: RDDs are immutable.
• Transformation: Operation on RDD to create a new RDD.
• Examples: Maps, Flat map, Filter etc.
• Operate on one element at a time.
• Evaluates lazily.
• Distributed across multiple nodes and executed by Executor within a cluster
on local RDD independently.
• Creates its own subset of resultant RDD.
30. Transformations: Maps
JavaRDD<String> mutateRDD1 = autoAllData.map(function);
• Simulate Map Reduce of Hadoop.
• Element level computation or transformation.
• The result RDD may have same number of elements as source RDD.
• Result type may be different.
• In java it allows Lambda expressions or anonymous class.
• Scala/Python: Inline function, function reference allowed.
• Use cases:
• Data standardization of may be names
• Data type conversion i.e. from String to Custom object.
• Data Computation like tax calculations.
• Adding new attributes like calculating Grade.
• Data checking, cleansing etc.
31. Transformations: Filters
JavaRDD<String> mutateRDD1 = autoAllData.filter(function);
• From RDD, it selects element which passes given criterion.
• Result in RDD of smaller size than original RDD as some records getting eliminated.
• The filter() takes a function which returns Boolean value.
• In java it allows Lambda expressions (Predicate) or anonymous class.
• Scala/Python: Inline function, function reference allowed.
32. Actions
• Acts on entire RDD to reduce to a precise and consolidated result.
• Max/Min, Summerization
• Spark lazily evaluates all processing on encountering an Action.
• Simple Actions
• The collect() operation: Converts RDD into collection.
• The count() operation: Count number of elements of RDD.
• The first() operation: Returns first record as a string.
• The take(n) operation: Returns first ‘n’ elements as a List of Strings.
34. Apache Spark on HDInsight
Azure Storage
Azure Data Lake Store
Hive and HBase
Azure Data Factory
Event Hub
S
P
A
R
K
Apache Kafka
Apache Flum
Orchestration
35. Spark Support on HDInsight
35
Feature Description
SLA 99.9% uptime
Ease of creating a
cluster
Possible using Azure Portal, Azure Powershell, Azure Insight .Net SDK.
Ease of use For interactive data processing and visualization, Jupyter and Zeppeline notebooks are provided.
REST APIs
Spark Cluster in HDInsight include Livy, a REST API based Spark Job Server to remotely submit and
monitor jobs.
Azure DataLake
The Azure DataLake Store can be used as primary storage (HDInsight 3.5 onwards) or an additional
storage.
Integration with
Azure services
Provides connectors for Azure Event Hub, Kafka.
R Server Can setup R Server and run R computations.
Concurrent
Queries
Supports concurrent queries. This enables multiple queries from one user or multiple queries from
various users and applications to share the same cluster resources.
SSD cache Cache of data either in memory or in SSD for better performance.
BI tools
integration
Connectors available for Power BI and Tableau for data analytics.
Machine Learning
Libraries
Preloaded 200 Anaconda libraries for Machine Learning, data analysis and visualization.
Scalability The Cloude’s prominent feature
37. Azure Data Lake Store
37
• Enterprise-wide hyper-scale repository for big data analytic workloads.
• Can capture data of any size, type, and ingestion speed in one single place
for operational and exploratory analytics.
• Hadoop accesses Data Lake Store through Web-HDFS REST API.
• Is tuned for performance for data analytics scenarios.
• Supports all enterprise-grade capabilities—security, manageability,
scalability, reliability, and availability.
• Stores variety of data in native format without transformation. Can handle
structured, semi-structured, and unstructured data.
39. Azure Storage Vs. Azure Data Lake
Azure Data Lake Store Azure Blob Storage
General purpose scalable object store Hyper-scale repository for big data analytics workload.
Use cases: Batch, interactive, streaming analytics and
machine learning data such as log files, IoT data, click
streams, large datasets
Any type of text or binary data, such as application back
end, backup data, media storage for streaming and
general purpose data.
Folders containing data in files Containers containing data in blobs.
Hierarchical file system Object store with flat namespace
Authentication: Azure AD identity Account Access Keys, Shared signature Access key
Optimized performance for parallel analytical workload Not optimized for analytics workload
Size Limit: No limit on file size or number of size. Specific limits as mentioned in document.
Geo-Redundancy: Locally redundant. Locally redundant, Globally redundant, Read Access
Globally Redundant.
40. Azure Data Factory
40
• A cloud data integration service, to compose data storage,
movement, and processing services into automated data
pipelines.
• Can handle ETL and complex hybrid ETL.
• Allows you to create data-driven workflows in the cloud for
orchestrating and automating data movement and data
transformation.
42. Azure Event Hub
42
• A highly scalable ingestion system
• Can ingest millions of events per second, enabling an application to process
and analyze the massive amounts of data produced by your connected
devices and applications.
• Works as Event Ingestor which works as intermediate between even
publisher and event consumer.
• Decouples production of Even streams from consumption mechanism.
• Enables behavior tracking in mobile apps, traffic information from web
farms, in-game event capture in console games, or telemetry collected from
industrial machines, connected vehicles, or other devices.
47. HDInsight Spark Resource Manager
47
The Resource Manager
enables you to control the
number of cores and amount
of memory allocated to Spark
cluster components and
notebooks.
Increasing the resources
allocated to the Thrift Server
can potentially improve the
performance with BI Tools
51. HDInsight Spark: Zeppelin Notebooks
51
Zeppelin notebook must be connected to a
Spark cluster to run
A notebook ‘paragraph’ can be executed
by clicking this icon
Like Jupytr, Zeppelin enables interactive
charts and graphs to be easily included in
a notebook.
You can control the visualization using the
“Settings” drop-down menu
A number of charts and graphs are already
built into the Zeppelin notebook
54. Overview
Spark SQL
• It’s a library built on Spark to support SQL Like operations.
• Facilitate eliminating RDDs from API for simplicity.
• Traditional RDBMS developers can easily transit to Big Data.
• Works with Structured Data that has a Schema.
• Seamlessly mix SQL Queries with Spark Programs.
• Supports JDBC.
• Mix with RDBMS and NoSQL.
55. Data Frames
Spark Session
• Like SparkContext for RDDs.
• Gives Data Frames and Temp Tables.
Data Frames
• RDDs are for Spark Core while Data Frames are for Spark SQL.
• Built upon RDDs
• It’s a distributed collection of data organized as Rows and Columns.
• Has a schema with column names and column types.
• Interoperability with…
• Collections, CSV, Data Bases, Hive/NoSQL tables, JSON, RDD etc.
56. Operations on Data Frames
• The filter: Its like ‘where’ clause.
• The join: Its like joins in SQL.
• The groupby: For grouping to get consolidation.
• The agg: Compute aggregation like sum, average.
• Allows mapping and reducing.
• Operations nesting allowed.
57. Spark SQL
• Spark SQL :
• Not intended for interactive/exploratory analysis.
• Spark SQL reuses the Hive frontend and meta-store.
• Gives full compatibility with existing Hive data, queries, and UDFs.
• Spark SQL includes a cost-based optimizer, columnar storage and code generation to make
queries fast.
• It scales to thousands of nodes and multi hour queries using the Spark engine. Performance
is its biggest advantage.
• Provides full mid-query fault tolerance.
61. Why Spark Streaming?
• One of the real powers of Spark
• Typically analytics is performed on data at rest:
• Databases, Flat files. The historical data, survey data etc.
• The real time analytics is performed on data the moment generated
• Complex Event Processing, Fraud detections, click stream processing etc.
• What Spark Stream can do?
• Look at data the moment it is arrived from source.
• Transform, summarize, analyze
• Perform machine learning
• Prediction in real time
63. Spark Streaming
Credit card fraud detection with high scalability and parallelism.
Spam filtering
Network intrusion detection
Real time social media analytics
Click Stream analytics
Stock market analysis
Advertise analytics
64. Spark Streaming architecture
Master Node
Driver Program
Spark Context
Stream
Context
Cluster
Manager
Worker Node
Executor
Long
Task Receiver
Input Source
Worker Node
Worker Node
Executor
Tas
k
Cache
65. Spark Streaming architecture
A master node is with driver program with Spark Context.
Create a streaming context from Spark Context.
One of the worker node is assigned a long task of listening a source.
The receiver keeps receiving data from input source.
The receiver propagate data to worker nodes.
The normal tasks in worker node act upon data.
66. The DStream
• Discretized stream
• Created from Stream context
• The micro batch window is set up for Dstream (Normally in secords)
• The micro batch window is a small time slice (around 3 sec) in which
generated real time data is accumulated as batch and wrapped in RDD called
Dstream.
• The Dstream allows all RDD operations.
• A common data can be shared across Global Variables
67. The Dstream windowing functions
• They are for computing across multiple Dstreams.
• All RDD functions are applicable on data accumulated from last X
batches.
• Ex: Accumulate last 3 batches together, Average of something of last 5
batches.
73. Types of Analytics
Descriptive Analytics :
• Defining problem
statement. What exactly
happened.
Exploratory Data
Analytics:
• Why something is
happening.
Inferential Analytics:
• Understand population
from the sample. Take
sample and extrapolate to
whole population.
Predictive Analytics:
• Forecast what will
happen.
Causal Analytics:
• Variables are related.
Understanding effect of
change in one variable to
another variable.
Deep Analytics:
• Analytics uses multi-
source data sources,
combining some or all
above analytics.
74. Data Analytics is needed everywhere –
Recommendation
engines
Smart meter
monitoringEquipment monitoringAdvertising analysisLife sciences research
Fraud
detection
Healthcare outcomes
Weather forecasting
for business planningOil & Gas exploration
Social network analysis
Churn
analysis
Traffic flow
optimization
IT infrastructure &
Web App optimization
Legal
discovery and
document archivingIntelligence Gathering
Location-based
tracking & services
Pricing Analysis
Personalized Insurance
75. Machine Learning in Sparks
• Makes ML easy
• Standard and common interface for different ML algorithms.
• Contains algorithms and utilities.
• It has two machine learning libraries.
• The spark.mllib: Original API built on RDDs. May be deprecated soon.
• The spark.ml: New higher level API built on Data Frames.
• The Machine Learning Algorithms of Spark uses these data types…
• Local Vector
• Labeled Point
• Every data to be submitted to ML must be converted to these data types.
76. Other algorithms supported
• Decision Tree
• Dimensionality Reduction
• Random Forest
• Linear Regression
• Naïve Bayes Classification
• K-Means Clustering
• Recommendation Engines
• …. And many more
77. Q & A
Contact: chandrashekhardeshpande@synergetics-india.com,
maheshshinde@synergetics-india.com
3 Slides - 5-10 Minute Discussion – Good to understand customers cloud strategy and see if S+S and Leveraging Existing Investments is important to them. Find out what cloud vendors and solutions they are evaluating.
For more details on the cloud definitions see wikipedia
http://en.wikipedia.org/wiki/Cloud_computing
points = spark.textFile(...).map(parsePoint).cache()w = numpy.random.ranf(size = D) # current separating planefor i in range(ITERATIONS): gradient = points.map( lambda p: (1 / (1 + exp(-p.y*(w.dot(p.x)))) - 1) * p.y * p.x ).reduce(lambda a, b: a + b) w -= gradientprint "Final separating plane: %s" % w
The property graph is a directed multigraph which can have multiple edges in parallel. Every edge and vertex has user defined properties associated with it. The parallel edges allow multiple relationships between the same vertices. Calculating shortest path between two airports. Calculating cheapest travel between two stations etc.
3 Slides - 5-10 Minute Discussion – Good to understand customers cloud strategy and see if S+S and Leveraging Existing Investments is important to them. Find out what cloud vendors and solutions they are evaluating.
For more details on the cloud definitions see wikipedia
http://en.wikipedia.org/wiki/Cloud_computing
Master node:
Driver Program: A program you write to initiate process.
Spark Context: A gate way to all spark functionalities.
Worker node: They work as per instruction from Master node. It executes Executor Programs. It is controlled by Cluster manager. The Cluster Manager may be YARN, MESOS or Spark Scheduler.
How RDDs are created? The Spark Context reads the records from Data Source and hands they over to Cluster Manager. Cluster Manager partitioned and distribute them to different worker nodes.
How transformation is applied to RDDs?
The spark context delegate transformation (Job) through cluster manager to the Executors. They are now called as Tasks and are executed in Executors. Executors may create new RDDs as outcome of execution of Transformation. All these RDDs are collected in Master Spark Context.
3 Slides - 5-10 Minute Discussion – Good to understand customers cloud strategy and see if S+S and Leveraging Existing Investments is important to them. Find out what cloud vendors and solutions they are evaluating.
For more details on the cloud definitions see wikipedia
http://en.wikipedia.org/wiki/Cloud_computing
3 Slides - 5-10 Minute Discussion – Good to understand customers cloud strategy and see if S+S and Leveraging Existing Investments is important to them. Find out what cloud vendors and solutions they are evaluating.
For more details on the cloud definitions see wikipedia
http://en.wikipedia.org/wiki/Cloud_computing
3 Slides - 5-10 Minute Discussion – Good to understand customers cloud strategy and see if S+S and Leveraging Existing Investments is important to them. Find out what cloud vendors and solutions they are evaluating.
For more details on the cloud definitions see wikipedia
http://en.wikipedia.org/wiki/Cloud_computing
3 Slides - 5-10 Minute Discussion – Good to understand customers cloud strategy and see if S+S and Leveraging Existing Investments is important to them. Find out what cloud vendors and solutions they are evaluating.
For more details on the cloud definitions see wikipedia
http://en.wikipedia.org/wiki/Cloud_computing
Credit card fraud detection: Every time credit card is swiped, a system must within fraction of seconds to analyze for abnormality situation to prevent credit card access from frauds by blocking.
Spam filtering: Many mails may hit the email box in unit time. It is essential to apply some parameters to know is it a spam mail.
Social media analytics: The data arising through social media need to be analyzed in real time to quickly arrive to some urgent conclusion.
Network intrusion detection: To prevent hacking into system by quickly analyzing web logs and system logs.
Click stream analysis: When internet user clicks through or browse through web pages, through analytics system need to give some recommendations
Stock Market analysis: Very frequent fluctuations in stock market leads to analysis to anticipate and draw some conclusion.
Advertise analysis: When search string is given, system needs to quickly show different advertises related to the search string.
3 Slides - 5-10 Minute Discussion – Good to understand customers cloud strategy and see if S+S and Leveraging Existing Investments is important to them. Find out what cloud vendors and solutions they are evaluating.
For more details on the cloud definitions see wikipedia
http://en.wikipedia.org/wiki/Cloud_computing