Conquering the Lambda architecture in LinkedIn metrics platform with Apache C...Khai Tran
Metrics play an important role in data-driven companies like LinkedIn, where we leverage them extensively for reporting, experimentation, and in-product applications. We built an offline platform to help people define and produce metrics driven through their transformation code, mostly in Pig or Hive, and metadata-rich configurations. Many of our users would like to look at these metrics in a real-time fashion. To support this, we recently built an extension to the platform that auto-generates Samza real-time flow from existing offline transformation code with just a single command. Combining with the existing offline platform, we delivered Lambda architecture without maintaining multiple code bases.
In this talk, we will describe how we use Apache Calcite to translate our offline logic, served as the single source of truth, into both Samza code and configuration for real-time execution.
Synchronization in distributed computingSVijaylakshmi
Synchronization in distributed systems is achieved via clocks. The physical clocks are used to adjust the time of nodes. Each node in the system can share its local time with other nodes in the system. The time is set based on UTC (Universal Time Coordination).
Introduction to Database Management SystemsAdri Jovin
This presentation contains content relevant to the introduction to the database management systems. The content is adapted from the original work of Abe Silberschatz et. al.
Conquering the Lambda architecture in LinkedIn metrics platform with Apache C...Khai Tran
Metrics play an important role in data-driven companies like LinkedIn, where we leverage them extensively for reporting, experimentation, and in-product applications. We built an offline platform to help people define and produce metrics driven through their transformation code, mostly in Pig or Hive, and metadata-rich configurations. Many of our users would like to look at these metrics in a real-time fashion. To support this, we recently built an extension to the platform that auto-generates Samza real-time flow from existing offline transformation code with just a single command. Combining with the existing offline platform, we delivered Lambda architecture without maintaining multiple code bases.
In this talk, we will describe how we use Apache Calcite to translate our offline logic, served as the single source of truth, into both Samza code and configuration for real-time execution.
Synchronization in distributed computingSVijaylakshmi
Synchronization in distributed systems is achieved via clocks. The physical clocks are used to adjust the time of nodes. Each node in the system can share its local time with other nodes in the system. The time is set based on UTC (Universal Time Coordination).
Introduction to Database Management SystemsAdri Jovin
This presentation contains content relevant to the introduction to the database management systems. The content is adapted from the original work of Abe Silberschatz et. al.
Unified Batch & Stream Processing with Apache SamzaDataWorks Summit
The traditional lambda architecture has been a popular solution for joining offline batch operations with real time operations. This setup incurs a lot of developer and operational overhead since it involves maintaining code that produces the same result in two, potentially different distributed systems. In order to alleviate these problems, we need a unified framework for processing and building data pipelines across batch and stream data sources.
Based on our experiences running and developing Apache Samza at LinkedIn, we have enhanced the framework to support: a) Pluggable data sources and sinks; b) A deployment model supporting different execution environments such as Yarn or VMs; c) A unified processing API for developers to work seamlessly with batch and stream data. In this talk, we will cover how these design choices in Apache Samza help tackle the overhead of lambda architecture. We will use some real production use-cases to elaborate how LinkedIn leverages Apache Samza to build unified data processing pipelines.
Speaker
Navina Ramesh, Sr. Software Engineer, LinkedIn
Distributed Systems Introduction and Importance SHIKHA GAUTAM
Distributed Systems Introduction and Importance. It covers the following Topics: Characterization of Distributed Systems: Introduction, Examples of distributed Systems, Resource sharing and the Web Challenges. Architectural models, Fundamental Models.
Theoretical Foundation for Distributed System: Limitation of Distributed system, absence of global clock, shared memory, Logical clocks ,Lamport’s & vectors logical clocks.
Concepts in Message Passing System.
Radical Speed for SQL Queries on Databricks: Photon Under the HoodDatabricks
Join this session to hear from the Photon product and engineering team talk about the latest developments with the project.
As organizations embrace data-driven decision-making, it has become imperative for them to invest in a platform that can quickly ingest and analyze massive amounts and types of data. With their data lakes, organizations can store all their data assets in cheap cloud object storage. But data lakes alone lack robust data management and governance capabilities. Fortunately, Delta Lake brings ACID transactions to your data lakes – making them more reliable while retaining the open access and low storage cost you are used to.
Using Delta Lake as its foundation, the Databricks Lakehouse platform delivers a simplified and performant experience with first-class support for all your workloads, including SQL, data engineering, data science & machine learning. With a broad set of enhancements in data access and filtering, query optimization and scheduling, as well as query execution, the Lakehouse achieves state-of-the-art performance to meet the increasing demands of data applications. In this session, we will dive into Photon, a key component responsible for efficient query execution.
Photon was first introduced at Spark and AI Summit 2020 and is written from the ground up in C++ to take advantage of modern hardware. It uses the latest techniques in vectorized query processing to capitalize on data- and instruction-level parallelism in CPUs, enhancing performance on real-world data and applications — all natively on your data lake. Photon is fully compatible with the Apache Spark™ DataFrame and SQL APIs to ensure workloads run seamlessly without code changes. Come join us to learn more about how Photon can radically speed up your queries on Databricks.
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
Tecnológico Nacional de México
Ingeniería en Sistemas Computacionales
Este material didáctico fue desarrollado para la asignatura de Tópicos Avanzados de Programación, del plan SCD-1027 2016
Using COBIT PO9 to perform Project Risk Analysiswebmentorman
How to Approach an Issue Using COBIT: Start by looking over the 34 Processes to see if one seems like a logical fit for the issue
Review Description and Control Objectives to validate this is the right Process for the issue
Consult the inputs/outputs to see what other processes are related to this issue
Review the RACI chart to begin organizing team members around resolution activities
Consult the Goals & Objectives and Maturity Model to identify current capability and steps needed to reach desired level
Unified Batch & Stream Processing with Apache SamzaDataWorks Summit
The traditional lambda architecture has been a popular solution for joining offline batch operations with real time operations. This setup incurs a lot of developer and operational overhead since it involves maintaining code that produces the same result in two, potentially different distributed systems. In order to alleviate these problems, we need a unified framework for processing and building data pipelines across batch and stream data sources.
Based on our experiences running and developing Apache Samza at LinkedIn, we have enhanced the framework to support: a) Pluggable data sources and sinks; b) A deployment model supporting different execution environments such as Yarn or VMs; c) A unified processing API for developers to work seamlessly with batch and stream data. In this talk, we will cover how these design choices in Apache Samza help tackle the overhead of lambda architecture. We will use some real production use-cases to elaborate how LinkedIn leverages Apache Samza to build unified data processing pipelines.
Speaker
Navina Ramesh, Sr. Software Engineer, LinkedIn
Distributed Systems Introduction and Importance SHIKHA GAUTAM
Distributed Systems Introduction and Importance. It covers the following Topics: Characterization of Distributed Systems: Introduction, Examples of distributed Systems, Resource sharing and the Web Challenges. Architectural models, Fundamental Models.
Theoretical Foundation for Distributed System: Limitation of Distributed system, absence of global clock, shared memory, Logical clocks ,Lamport’s & vectors logical clocks.
Concepts in Message Passing System.
Radical Speed for SQL Queries on Databricks: Photon Under the HoodDatabricks
Join this session to hear from the Photon product and engineering team talk about the latest developments with the project.
As organizations embrace data-driven decision-making, it has become imperative for them to invest in a platform that can quickly ingest and analyze massive amounts and types of data. With their data lakes, organizations can store all their data assets in cheap cloud object storage. But data lakes alone lack robust data management and governance capabilities. Fortunately, Delta Lake brings ACID transactions to your data lakes – making them more reliable while retaining the open access and low storage cost you are used to.
Using Delta Lake as its foundation, the Databricks Lakehouse platform delivers a simplified and performant experience with first-class support for all your workloads, including SQL, data engineering, data science & machine learning. With a broad set of enhancements in data access and filtering, query optimization and scheduling, as well as query execution, the Lakehouse achieves state-of-the-art performance to meet the increasing demands of data applications. In this session, we will dive into Photon, a key component responsible for efficient query execution.
Photon was first introduced at Spark and AI Summit 2020 and is written from the ground up in C++ to take advantage of modern hardware. It uses the latest techniques in vectorized query processing to capitalize on data- and instruction-level parallelism in CPUs, enhancing performance on real-world data and applications — all natively on your data lake. Photon is fully compatible with the Apache Spark™ DataFrame and SQL APIs to ensure workloads run seamlessly without code changes. Come join us to learn more about how Photon can radically speed up your queries on Databricks.
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
Tecnológico Nacional de México
Ingeniería en Sistemas Computacionales
Este material didáctico fue desarrollado para la asignatura de Tópicos Avanzados de Programación, del plan SCD-1027 2016
Using COBIT PO9 to perform Project Risk Analysiswebmentorman
How to Approach an Issue Using COBIT: Start by looking over the 34 Processes to see if one seems like a logical fit for the issue
Review Description and Control Objectives to validate this is the right Process for the issue
Consult the inputs/outputs to see what other processes are related to this issue
Review the RACI chart to begin organizing team members around resolution activities
Consult the Goals & Objectives and Maturity Model to identify current capability and steps needed to reach desired level
Thinking of COBIT implementation – Where to start?Vyom Labs
Executives today are increasingly under pressure to manage IT risk and to transform the way business value can be generated from IT.In this Webinar you will learn how to apply COBIT to specific business problems, pain points, trigger events and risk scenarios within the organization.Also how to effectively use it for client initiatives.
Identificación de Talento ITSM Excepcional antes que los demásEXIN
Las prácticas de Gestión de Talento juegan un papel crítico para el éxito de la Gestión de Servicios de TI, aún mucho más importante que los procesos y la tecnologíaEl uso de un juego de simulación de negocio como medio para identificar candidatos de alto desempeño ha dado resultados altamente satisfactorios a empresas que lo utilizan; quienes ya lo han incorporado como parte de su práctica habitual en el reclutamiento y selección de personal.
Los resultados que se derivan de este tipo de ejercicio son considerados por estas empresas un predictor altamente confiable del desempeño futuro de los nuevos empleadosSe evalúa el desempeño individual de los miembros de un equipo de trabajo que gestionan un negocio simulado. Los participantes serán evaluados de acuerdo a los criterios de la empresa, puede ser en cuanto a si sus conocimientos, competencias (habilidades, aptitudes; naturaleza y estilo de trabajo, preferencias y valores) y personalidad se ajustan y “calzan” con los valores corporativos específicos de una organización.
Conocer las características que debe comprender un inventario de RRHH y evaluación del desempeño con técnica 360°, generando competencias y habilidades laborales, dentro de la gestión de recursos humanos.
Anna Lucia Alfaro Dardón, Harvard MPA/ID.
Opportunities, constraints and challenges for the development of the small and medium enterprise (SME) sector in Central America, with an analytical study of the SME sector in Nicaragua. - focused on the current supply and demand gap for credit and financial services.
Anna Lucía Alfaro Dardón
Dr. Ivan Alfaro
Anna Lucia Alfaro Dardón, Harvard MPA/ID. The international successful Case Study of Banco de Desarrollo Rural S.A. in Guatemala - a mixed capital bank with a multicultural and multisectoral governance structure, and one of the largest and most profitable banks in the Central American region.
INCAE Business Review, 2010.
Anna Lucía Alfaro Dardón
Dr. Ivan Alfaro
Dr. Luis Noel Alfaro Gramajo
2. Control sobre el proceso de TI de: Administración de recursos humanos Que satisface los requerimientos de negocio de: Adquirir y mantener una fuerza de trabajo motivada ycompetentey maximizar las contribuciones del personal a los procesos de TI
3. Se hace posible a través de: Prácticas de administración de personal, sensatas, justas y transparentes para reclutar, alinear, pensionar, compensar, entrenar, promover y despedir Y toma en consideración: • Reclutamiento y promoción • Entrenamiento y requerimientos de calificaciones • Desarrollo de conciencia • Entrenamiento cruzado y rotación de puestos • Procedimientos para contratación, veto y despidos • Evaluación objetiva y medible del desempeño • Responsabilidades sobre los cambios técnicos y de mercado • Balance apropiado de recursos internos y externos • Plan de sucesión para posiciones clave
4.
5.
6.
7. ROLES DEL GRAFICO RACI Los roles en la gráfica RACI están clasificados para todos los procesos como sigue: Director ejecutivo (CEO) Director financiero (CFO) Ejecutivos del negocio Director de información (CIO) Propietario del proceso de negocio Jefe de operaciones Arquitecto en jefe Jefe de desarrollo Jefe de administración de TI (para empresas grandes, el jefe de funciones como recursos humanos, presupuestos y control interno) Cumplimiento, auditoría, riesgo y seguridad (grupos con responsabilidades de control que no tienen responsabilidades operacionales de TI)
8. SIGNIFICADO DE LAS LETRAS DEL MODELO RACI R = Responsible (encargado). La persona que tiene a cargo el proyecto/problema. A = Accountable. Es ante quien "R" debe reportarse, quien debe firmar o aprobar el trabajo antes de que sea ACEPTADO. S = Supportive (puede ser de apoyo). puede proporcionar recursos o puede desempeñar un papel al apoyar la puesta en práctica. C = Consulted (es quien debe ser consultado). Tiene la información y/o la capacidad necesarios para terminar el trabajo. I = debe ser informado. Debe ser notificado de los resultados, pero no necesita ser consultado.
9. GRAFICA RACI Funciones CEO Arquitecto en Jefe Jefe de Desarrollo Ejecutivo del Negocio Cumplimiento, Auditoria, Riesgo y Seguridad Departamento de Capacitación Jefe de Administración de TI Jefe de Operaciones PMO Prop. de proceso del negocio CIO CFO Actividades