Enterprise Content Management Migration Best Practices Feat Migrations From...Alfresco Software
www.alfresco.com/about/events/ondemand (for full webinar)
Technology Services Group (TSG) discusses a recent project showcasing a migration effort from SharePoint 2003 to Alfresco.
TSG has extensive content migration experience and is able to understand and meet complex migration requirements by leveraging their OpenSource migration framework, OpenMigrate.
This webinar will include various Alfresco migration success stories (like Documentum to Alfresco)...
as well as an architectural overview of their open source migration tool - OpenMigrate.
Enterprise Content Management Migration Best Practices Feat Migrations From...Alfresco Software
www.alfresco.com/about/events/ondemand (for full webinar)
Technology Services Group (TSG) discusses a recent project showcasing a migration effort from SharePoint 2003 to Alfresco.
TSG has extensive content migration experience and is able to understand and meet complex migration requirements by leveraging their OpenSource migration framework, OpenMigrate.
This webinar will include various Alfresco migration success stories (like Documentum to Alfresco)...
as well as an architectural overview of their open source migration tool - OpenMigrate.
Apache® Spark™ MLlib 2.x: migrating ML workloads to DataFramesDatabricks
In the Apache Spark 2.x releases, Machine Learning (ML) is focusing on DataFrame-based APIs. This webinar is aimed at helping users take full advantage of the new APIs. Topics will include migrating workloads from RDDs to DataFrames, ML persistence for saving and loading models, and the roadmap ahead.
Migrating ML workloads to use Spark DataFrames and Datasets allows users to benefit from simpler APIs, plus speed and scalability improvements. As the DataFrame/Dataset API becomes the primary API for data in Spark, this migration will become increasingly important to MLlib users, especially for integrating ML with the rest of Spark data processing workloads. We will give a tutorial covering best practices and some of the immediate and future benefits to expect.
ML persistence is one of the biggest improvements in the DataFrame-based API. With Spark 2.0, almost all ML algorithms can be saved and loaded, even across languages. ML persistence dramatically simplifies collaborating across teams and moving ML models to production. We will demonstrate how to use persistence, and we will discuss a few existing issues and workarounds.
At the end of the webinar, we will discuss major roadmap items. These include API coverage, major speed and scalability improvements to certain algorithms, and integration with structured streaming.
A Deep Dive into Structured Streaming: Apache Spark Meetup at Bloomberg 2016 Databricks
Tathagata 'TD' Das presented at Bay Area Apache Spark Meetup. This talk covers the merits and motivations of Structured Streaming, and how you can start writing end-to-end continuous applications using Structured Streaming APIs.
Intro to Talend Open Studio for Data IntegrationPhilip Yurchuk
An overview of Talend Open Studio for Data Integration, along with some tips learned from building production jobs and a list of resources. Feel free to contact me for more information.
As organizations pursue Big Data initiatives to capture new opportunities for data-driven insights, data governance has become table stakes both from the perspective of external regulatory compliance as well as business value extraction internally within an enterprise. This session will introduce Apache Atlas, a project that was incubated by Hortonworks along with a group of industry leaders across several verticals including financial services, healthcare, pharma, oil and gas, retail and insurance to help address data governance and metadata needs with an open extensible platform governed under the aegis of Apache Software Foundation. Apache Atlas empowers organizations to harvest metadata across the data ecosystem, govern and curate data lakes by applying consistent data classification with a centralized metadata catalog.
In this talk, we will present the underpinnings of the architecture of Apache Atlas and conclude with a tour of governance capabilities within Apache Atlas as we showcase various features for open metadata modeling, data classification, visualizing cross-component lineage and impact. We will also demo how Apache Atlas delivers a complete view of data movement across several analytic engines such as Apache Hive, Apache Storm, Apache Kafka and capabilities to effectively classify, discover datasets.
The aim of the presentation is to give readers the framework to think about building the rest APIs right. It starts with describing the need for APIs, what REST APIs are and how are they different from general web APIs. Furthermore, it explains how HTTP and REST play together to build the overall solution and HTTP best practices to be followed when designing REST APIs. It then outlines, some of the basic features / functionality to expect from properly designed REST API system.
Frequently asked MuleSoft Interview Questions and Answers from TechlightningArul ChristhuRaj Alphonse
MuleSoft Interview Questions and answers from youtube channel Techlightning
https://www.youtube.com/watch?v=JqBgT2t6cCQ&list=PLfEAetjBY9s4YdBie3VSpufxNcnC3wJvK
This document contains top 50 MuleSoft Interview questions for the MuleSoft software development job aspirants. Also, this helps, who are taking MCD API Design Associate exam.
Sharing metadata across the data lake and streamsDataWorks Summit
Traditionally systems have stored and managed their own metadata, just as they traditionally stored and managed their own data. A revolutionary feature of big data tools such as Apache Hadoop and Apache Kafka is the ability to store all data together, where users can bring the tools of their choice to process it. Apache Hive's metastore can be used to share the metadata in the same way. It is already used by many SQL and SQL-like systems beyond Hive (e.g. Apache Spark, Presto, Apache Impala, and via HCatalog, Apache Pig). As data processing changes from only data in the cluster to include data in streams, the metastore needs to expand and grow to meet these use cases as well. There is work going on in the Hive community to separate out the metastore, so it can continue to serve Hive but also be used by a more diverse set of tools. This talk will discuss that work, with particular focus on adding support for storing schemas for Kafka messages.
Speaker
Alan Gates, Co-Founder, Hortonworks
Talend Data Integration Tutorial | Talend Tutorial For Beginners | Talend Onl...Edureka!
( Talend Training - https://www.edureka.co/talend-for-big... )
This Edureka PPT on Talend Data Integration Tutorial will help you in understanding the basic concepts of Talend and getting familiar with the Talend Open Studio which is an open source software provided by Talend to develop the ETL Jobs.
This video helps you to learn following topics:
1. What Is Talend?
2. Talend Open Studio
3. TOS Installation
4. TOS GUI
5. Talend Components & Connectors
6. Metadata & Context Variables
We will show a case study of moving from SQL Server DWH to Hadoop and Vertica. In this case study you will see implementation of Lambda Architecture with Hadoop, Vertica and MongoDB for real time statistics. We will start from showing the Legacy system and describe the problems we encountered. From there we will cover all the decision making on technology choosing for the current solution. We will finish by presenting the next steps of our Data Platform solution.
Spark SQL Tutorial | Spark SQL Using Scala | Apache Spark Tutorial For Beginn...Simplilearn
This presentation about Spark SQL will help you understand what is Spark SQL, Spark SQL features, architecture, data frame API, data source API, catalyst optimizer, running SQL queries and a demo on Spark SQL. Spark SQL is an Apache Spark's module for working with structured and semi-structured data. It is originated to overcome the limitations of Apache Hive. Now, let us get started and understand Spark SQL in detail.
Below topics are explained in this Spark SQL presentation:
1. What is Spark SQL?
2. Spark SQL features
3. Spark SQL architecture
4. Spark SQL - Dataframe API
5. Spark SQL - Data source API
6. Spark SQL - Catalyst optimizer
7. Running SQL queries
8. Spark SQL demo
This Apache Spark and Scala certification training is designed to advance your expertise working with the Big Data Hadoop Ecosystem. You will master essential skills of the Apache Spark open source framework and the Scala programming language, including Spark Streaming, Spark SQL, machine learning programming, GraphX programming, and Shell Scripting Spark. This Scala Certification course will give you vital skillsets and a competitive advantage for an exciting career as a Hadoop Developer.
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
Learn more at https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training
Apache® Spark™ MLlib 2.x: migrating ML workloads to DataFramesDatabricks
In the Apache Spark 2.x releases, Machine Learning (ML) is focusing on DataFrame-based APIs. This webinar is aimed at helping users take full advantage of the new APIs. Topics will include migrating workloads from RDDs to DataFrames, ML persistence for saving and loading models, and the roadmap ahead.
Migrating ML workloads to use Spark DataFrames and Datasets allows users to benefit from simpler APIs, plus speed and scalability improvements. As the DataFrame/Dataset API becomes the primary API for data in Spark, this migration will become increasingly important to MLlib users, especially for integrating ML with the rest of Spark data processing workloads. We will give a tutorial covering best practices and some of the immediate and future benefits to expect.
ML persistence is one of the biggest improvements in the DataFrame-based API. With Spark 2.0, almost all ML algorithms can be saved and loaded, even across languages. ML persistence dramatically simplifies collaborating across teams and moving ML models to production. We will demonstrate how to use persistence, and we will discuss a few existing issues and workarounds.
At the end of the webinar, we will discuss major roadmap items. These include API coverage, major speed and scalability improvements to certain algorithms, and integration with structured streaming.
A Deep Dive into Structured Streaming: Apache Spark Meetup at Bloomberg 2016 Databricks
Tathagata 'TD' Das presented at Bay Area Apache Spark Meetup. This talk covers the merits and motivations of Structured Streaming, and how you can start writing end-to-end continuous applications using Structured Streaming APIs.
Intro to Talend Open Studio for Data IntegrationPhilip Yurchuk
An overview of Talend Open Studio for Data Integration, along with some tips learned from building production jobs and a list of resources. Feel free to contact me for more information.
As organizations pursue Big Data initiatives to capture new opportunities for data-driven insights, data governance has become table stakes both from the perspective of external regulatory compliance as well as business value extraction internally within an enterprise. This session will introduce Apache Atlas, a project that was incubated by Hortonworks along with a group of industry leaders across several verticals including financial services, healthcare, pharma, oil and gas, retail and insurance to help address data governance and metadata needs with an open extensible platform governed under the aegis of Apache Software Foundation. Apache Atlas empowers organizations to harvest metadata across the data ecosystem, govern and curate data lakes by applying consistent data classification with a centralized metadata catalog.
In this talk, we will present the underpinnings of the architecture of Apache Atlas and conclude with a tour of governance capabilities within Apache Atlas as we showcase various features for open metadata modeling, data classification, visualizing cross-component lineage and impact. We will also demo how Apache Atlas delivers a complete view of data movement across several analytic engines such as Apache Hive, Apache Storm, Apache Kafka and capabilities to effectively classify, discover datasets.
The aim of the presentation is to give readers the framework to think about building the rest APIs right. It starts with describing the need for APIs, what REST APIs are and how are they different from general web APIs. Furthermore, it explains how HTTP and REST play together to build the overall solution and HTTP best practices to be followed when designing REST APIs. It then outlines, some of the basic features / functionality to expect from properly designed REST API system.
Frequently asked MuleSoft Interview Questions and Answers from TechlightningArul ChristhuRaj Alphonse
MuleSoft Interview Questions and answers from youtube channel Techlightning
https://www.youtube.com/watch?v=JqBgT2t6cCQ&list=PLfEAetjBY9s4YdBie3VSpufxNcnC3wJvK
This document contains top 50 MuleSoft Interview questions for the MuleSoft software development job aspirants. Also, this helps, who are taking MCD API Design Associate exam.
Sharing metadata across the data lake and streamsDataWorks Summit
Traditionally systems have stored and managed their own metadata, just as they traditionally stored and managed their own data. A revolutionary feature of big data tools such as Apache Hadoop and Apache Kafka is the ability to store all data together, where users can bring the tools of their choice to process it. Apache Hive's metastore can be used to share the metadata in the same way. It is already used by many SQL and SQL-like systems beyond Hive (e.g. Apache Spark, Presto, Apache Impala, and via HCatalog, Apache Pig). As data processing changes from only data in the cluster to include data in streams, the metastore needs to expand and grow to meet these use cases as well. There is work going on in the Hive community to separate out the metastore, so it can continue to serve Hive but also be used by a more diverse set of tools. This talk will discuss that work, with particular focus on adding support for storing schemas for Kafka messages.
Speaker
Alan Gates, Co-Founder, Hortonworks
Talend Data Integration Tutorial | Talend Tutorial For Beginners | Talend Onl...Edureka!
( Talend Training - https://www.edureka.co/talend-for-big... )
This Edureka PPT on Talend Data Integration Tutorial will help you in understanding the basic concepts of Talend and getting familiar with the Talend Open Studio which is an open source software provided by Talend to develop the ETL Jobs.
This video helps you to learn following topics:
1. What Is Talend?
2. Talend Open Studio
3. TOS Installation
4. TOS GUI
5. Talend Components & Connectors
6. Metadata & Context Variables
We will show a case study of moving from SQL Server DWH to Hadoop and Vertica. In this case study you will see implementation of Lambda Architecture with Hadoop, Vertica and MongoDB for real time statistics. We will start from showing the Legacy system and describe the problems we encountered. From there we will cover all the decision making on technology choosing for the current solution. We will finish by presenting the next steps of our Data Platform solution.
Spark SQL Tutorial | Spark SQL Using Scala | Apache Spark Tutorial For Beginn...Simplilearn
This presentation about Spark SQL will help you understand what is Spark SQL, Spark SQL features, architecture, data frame API, data source API, catalyst optimizer, running SQL queries and a demo on Spark SQL. Spark SQL is an Apache Spark's module for working with structured and semi-structured data. It is originated to overcome the limitations of Apache Hive. Now, let us get started and understand Spark SQL in detail.
Below topics are explained in this Spark SQL presentation:
1. What is Spark SQL?
2. Spark SQL features
3. Spark SQL architecture
4. Spark SQL - Dataframe API
5. Spark SQL - Data source API
6. Spark SQL - Catalyst optimizer
7. Running SQL queries
8. Spark SQL demo
This Apache Spark and Scala certification training is designed to advance your expertise working with the Big Data Hadoop Ecosystem. You will master essential skills of the Apache Spark open source framework and the Scala programming language, including Spark Streaming, Spark SQL, machine learning programming, GraphX programming, and Shell Scripting Spark. This Scala Certification course will give you vital skillsets and a competitive advantage for an exciting career as a Hadoop Developer.
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
Learn more at https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training
Type "Google.com" into the Browser and Hit Enter: What Happens Next?Graeme Mathieson
I haven’t done any interviewing for a while but I went through a period of growth in one of the companies I worked for where we were feverishly expanding the development team, so we had to be a little more systematic in our approach to interviewing. Instead of just having an open conversation with candidates to see where it led (which is what I’d previously done in such situations), I wound up preparing a ‘standard’ set of questions. It took a few goes, but eventually I settled on a favourite question for the technical portion of the interview:
"When I pull up my favourite Internet browser, type “google.com” into the address bar, and press return, what happens?"
I reckon it’s a doozy of a technical question. There’s so much breadth and depth in that answer. Let's dig into it a bit more...
Architecting for Scalable and Usable Web Applications
As Enterprises and Software Vendors start to develop more and more applications on the Internet there is an increasing importance to architect these applications for both growth and for the optimal user experience. Software + Services allows you to develop fantastic applications, but there are pitfalls with architecting the applications in the wrong way.
Our Central Region Architect Evangelists will lead us through two great discussions on scaling web applications and creating the best possible user experience.
Session 1: Architecting for Scalable Web Applications In this session we will explore the patterns that typical applications follow as their scalability needs grow due to increased demand. We will also discuss best practices from companies that have gone up the scalability curve like Amazon.com, MySpace and Flickr. We will discuss the common bottlenecks that prevent scalability as well as how to tackle tough issues like state management in a application that is scaled across servers and even data centers. We will also discuss the “scale later” philosophy and how it should be accompanied by a solid plan to scale your applications.
Session 2: Architecting for Usable Web Applications In this session we will discuss how to architect your application with the user in mind. We have more choices than ever before for developing applications (Traditional Web Apps, AJAX, RIA technologies like Flex and Microsoft Silverlight and even smart clients) and picking the technology is only part of the solution. The architecture of the application must be designed correctly to provide a pleasing user experience and (potentially) to add new and interesting clients in the future.
HTTP Session Replication with Oracle Coherence, GlassFish, WebLogicOracle
In this talk we will cover the integration of Coherence and Application Servers like Oracle WebLogic and Oracle GlassFish Server, and touch on the native capabilities of each server for HTTP session state management as well. The integration makes it simpler to access Coherence named caches through resource injection. It also provides an optimized integration of Coherence*Web for HTTP session state management. From a management perspective, it offers Coherence cluster configuration support through the WLS administration domain as well as Runtime monitoring support through the WebLogic console.
An introduction to REST and RESTful web services.
You can take the course below to learn about REST & RESTful web services.
https://www.udemy.com/building-php-restful-web-services/
REST: So What's It All About? (SAP TechEd 2011, MOB107)Sascha Wenninger
Google and Twitter have been using it for years and now SAP has joined in with Project Gateway. So what is REST all about, how is it different from SOA-style integration and what could you use it for? This presentation will give you an overview of the concepts which define the REST architectural style and what has made it so popular with Internet companies and long-haired developers. You will also get some pointers on how to implement RESTful services in your SAP systems and expose your SAP systems to Web and mobile applications - both with and without Project Gateway! And to see all this in action, SAP Mentor John Moy will demo how a mobile Web application using jQuery Mobile can consume a RESTful service built in ABAP!
SAP FIORI COEP Pune - pavan golesar (ppt)Pavan Golesar
Hi,
This material is not for commercial purpose, Disclaimer: Copyright content included.
For learning purpose only.
sapparamount@gmail.com
Pavan Golesar
23. RDF Net API – Query RDF Net API Processing Layer Client Op-Prototype: query(ModelReference, Query, QueryLang, ResultsFormat) => StatementSet ModelReference: Reference to the target model for this operation Query: The query to be executed QueryLanguage: Indication of the query language ResultsFormat: Indication of the format of the results to be returned as a set of statements StatementSet: Set of statements returned
24. RDF Net API – GetStatements RDF Net API Processing Layer Client Op-Prototype: getStatements(ModelReference, Subject, Predicate, Object) => StatementSet ModelReference: Reference to the target model for this operation Subject: URI or * (wildcard) Predicate: URI or * Object: URI,literal or * StatementSet: Set of statements returned
25. RDF Net API – InsertStatements RDF Net API Processing Layer Client Op-Prototype: insertStatements(ModelReference, StatementSet) ModelReference: Reference to the target model for this operation StatementSet: Set of RDF statements for the operation
26. RDF Net API – RemoveStatements RDF Net API Processing Layer Client Op-Prototype: removeStatements(ModelReference, StatementSet) ModelReference: Reference to the target model for this operation StatementSet: Set of RDF statements for the operation
27. RDF Net API – PutStatements RDF Net API Processing Layer Client Op-prototype: putStatements(ModelReference, StatementSet) ModelReference: Reference to the target model for this operation StatementSet: Set of RDF statements for the operation
28. RDF Net API - UpdateStatements RDF Net API Processing Layer Client Op-prototype: updateStatements(ModelReference, RemoveSet , InsertSet) ModelReference: Reference to the target model for this operation RemoveSet: Set of RDF statements to be removed InsertSet: Set of RDF statements to be inserted
29. RDF Net API - Options RDF Net API Processing Layer Client Op-prototype: options(ModelReference) => StatementSet ModelReference: Reference to the target model for this operation StatementSet: Results of the operation
49. Topic Map Server – Fragment Contexts <topic id=“t-1”> <baseName> <baseNameString>Semantic Web Server</baseNameString> </baseName> </topic> If we are adding this, do we want to add a new topic with a new name, or add a name to an existing Topic?