Mule batch
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Mule ESB allows processing of messages in batches. A batch job splits large messages into individual records, processes each record through batch steps, and reports results. It is useful for bulk database operations or integrating large data sets. The key parts of a batch job are the input phase to prepare data, loading phase to split it into records, process phase to handle each record asynchronously through steps, and completion phase to report outcomes.
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Batch job processing
The document discusses batch job processing in Mule. It describes a use case where a batch job queries a database to retrieve approved users and generates a CSV file with the user account attributes. Batch processing in Mule allows splitting messages into records, performing actions on each record, and reporting results. It is useful for integrating or synchronizing data sets, ETL processes, and handling large amounts of incoming data.
Batch processing
Mule allows processing of messages in batches through batch jobs. A batch job splits incoming messages into individual records, processes each record using message processors, and reports results. Batch processing is useful for streaming data integration, synchronizing data between applications like Salesforce and Netsuite, ETL processes, and handling large API data. A batch job exists outside flows and contains steps to sequentially process records through variables and MEL expressions.
Batch processing
Batch processing in Mule allows splitting messages into individual records that are processed asynchronously and in parallel. A batch job contains steps that act on each record, and reports results. Batch processing is useful for integrating or synchronizing large datasets between systems, ETL processes, and handling large volumes of incoming API data. Key elements include batch jobs, steps, records, and reporting on results through the batch job instance and result objects.
Scopes in mule
Scopes in mule allow you to group message processors together. Certain scopes like Poll, Message Enricher, and Until Successful allow only one embedded message processor. Scopes can execute synchronously, halting the parent flow until the child scope completes, or asynchronously where the parent flow continues independently. The Cache Scope stores and reuses frequently called data, and the Composite Source handles messages from multiple sources. The Foreach splits messages into parts for processing while the Poll periodically retrieves messages. The Transactional scope executes a series of steps atomically, while the Until Successful retries an embedded processor until it succeeds or hits retry limits.
Clustering concepts
Mule clusters allow Mule runtimes to communicate and share information to act as a single virtual server. Clusters provide high availability through automatic failover if a node fails. They also improve performance and scalability by distributing workloads across multiple nodes. Additional benefits include automatic coordination of shared resources, load balancing, cluster management, and performance monitoring. Mule uses an active-active clustering model where all nodes actively process applications rather than one primary node.
Database example
This document provides an overview of batch processing in Mule and demonstrates how to read records from an Oracle database and write them to a file using a Mule batch job. It defines a batch job as processing records asynchronously outside of any Mule flows. The example shows a message flow that uses a batch job to split a message into records, process them through multiple steps, and write the results to a new file. Testing and logging are used to verify the new file was generated correctly from the database records. References are provided for additional documentation on Mule batch processing and the database connector.
Mule batch processing
The document describes the three phases of a batch component in Mule: input, process records, and on complete. The input phase prepares a collection object with the input message. The process records phase processes each record in the collection individually and in parallel. The on complete phase provides status information about the number of successful, failed, and total records. An example is also provided that uses a batch component to transform a CSV file to XML using data mapping and writes the output to a file.
Controlling message flow
This document summarizes key concepts around controlling message flow in Mule such as filter types, flow processing strategies, and common routers. It describes synchronous and asynchronous processing strategies and how Mule determines the appropriate strategy. It provides examples of common routers like scatter-gather, choice, and aggregators. It also summarizes filter types that can determine if a message proceeds in a flow including the idempotent filter.
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Batch job processing
The document discusses batch job processing in Mule. It describes a use case where a batch job queries a database to retrieve approved users and generates a CSV file with the user account attributes. Batch processing in Mule allows splitting messages into records, performing actions on each record, and reporting results. It is useful for integrating or synchronizing data sets, ETL processes, and handling large amounts of incoming data.
Batch processing
Mule allows processing of messages in batches through batch jobs. A batch job splits incoming messages into individual records, processes each record using message processors, and reports results. Batch processing is useful for streaming data integration, synchronizing data between applications like Salesforce and Netsuite, ETL processes, and handling large API data. A batch job exists outside flows and contains steps to sequentially process records through variables and MEL expressions.
Batch processing
Batch processing in Mule allows splitting messages into individual records that are processed asynchronously and in parallel. A batch job contains steps that act on each record, and reports results. Batch processing is useful for integrating or synchronizing large datasets between systems, ETL processes, and handling large volumes of incoming API data. Key elements include batch jobs, steps, records, and reporting on results through the batch job instance and result objects.
Scopes in mule
Scopes in mule allow you to group message processors together. Certain scopes like Poll, Message Enricher, and Until Successful allow only one embedded message processor. Scopes can execute synchronously, halting the parent flow until the child scope completes, or asynchronously where the parent flow continues independently. The Cache Scope stores and reuses frequently called data, and the Composite Source handles messages from multiple sources. The Foreach splits messages into parts for processing while the Poll periodically retrieves messages. The Transactional scope executes a series of steps atomically, while the Until Successful retries an embedded processor until it succeeds or hits retry limits.
Clustering concepts
Mule clusters allow Mule runtimes to communicate and share information to act as a single virtual server. Clusters provide high availability through automatic failover if a node fails. They also improve performance and scalability by distributing workloads across multiple nodes. Additional benefits include automatic coordination of shared resources, load balancing, cluster management, and performance monitoring. Mule uses an active-active clustering model where all nodes actively process applications rather than one primary node.
Database example
This document provides an overview of batch processing in Mule and demonstrates how to read records from an Oracle database and write them to a file using a Mule batch job. It defines a batch job as processing records asynchronously outside of any Mule flows. The example shows a message flow that uses a batch job to split a message into records, process them through multiple steps, and write the results to a new file. Testing and logging are used to verify the new file was generated correctly from the database records. References are provided for additional documentation on Mule batch processing and the database connector.
Mule batch processing
The document describes the three phases of a batch component in Mule: input, process records, and on complete. The input phase prepares a collection object with the input message. The process records phase processes each record in the collection individually and in parallel. The on complete phase provides status information about the number of successful, failed, and total records. An example is also provided that uses a batch component to transform a CSV file to XML using data mapping and writes the output to a file.
Controlling message flow
This document summarizes key concepts around controlling message flow in Mule such as filter types, flow processing strategies, and common routers. It describes synchronous and asynchronous processing strategies and how Mule determines the appropriate strategy. It provides examples of common routers like scatter-gather, choice, and aggregators. It also summarizes filter types that can determine if a message proceeds in a flow including the idempotent filter.
Batch processing
Batch processing in Mule allows splitting messages into individual records that are processed asynchronously and in parallel. A batch job contains steps that act on each record, and reports results. Batch processing is useful for integrating or synchronizing large datasets between systems, ETL, and handling large API data. Records are processed across phases including input, load/dispatch, processing by steps, and optional reporting on completion.
Batch processing
Mule, Batch Processing, Mule Batch Concept, Mule Batch, Mule Batch process, Mule batch processing concepts
Mule batch processing
Batch processing in Mule allows splitting messages into individual records, performing actions on each record in parallel, and reporting results. It is useful for integrating or synchronizing large datasets between systems, extracting/transforming/loading data into a target system, and handling large volumes of incoming API data. Mule's batch jobs simplify processing massive amounts of data with record-based reporting, error handling, and parallel processing.
Mule esb batch
Batch processing in Mule allows splitting messages into individual records, performing actions on each record in parallel, and reporting results. It is useful for integrating or synchronizing large datasets between systems, extracting/transforming/loading data into a target system, and handling large volumes of incoming API data. Mule's batch jobs simplify processing massive amounts of data through record-based reporting, error handling, and parallel processing to improve reliability.
Batch processing
Batch processing in Mule allows splitting messages into individual records, performing actions on each record in parallel, and reporting results. It is useful for integrating or synchronizing large datasets between systems, extracting/transforming/loading data into a target system, and handling large volumes of incoming API data. Mule's batch jobs simplify processing massive amounts of data with record-based reporting, error handling, and parallel processing.
Mule batch processing
Batch processing in Mule allows splitting messages into individual records, performing actions on each record in parallel, and reporting results. It is useful for integrating or synchronizing large datasets between systems, extracting/transforming/loading data into a target system, and handling large volumes of incoming API data. Mule's batch jobs simplify processing massive amounts of data through record-based reporting, error handling, and parallel processing to improve reliability.
Mule batch processing
Batch processing in Mule allows splitting messages into individual records, performing actions on each record in parallel, and reporting results. It is useful for integrating or synchronizing large datasets between systems, extracting/transforming/loading data into a target system, and handling large volumes of incoming API data. Mule's batch jobs simplify processing massive amounts of data with record-based reporting, error handling, and parallel processing.
Mule batch processing
Batch processing in Mule allows splitting messages into individual records, performing actions on each record in parallel, and reporting results. It is useful for integrating or synchronizing large datasets between systems, extracting/transforming/loading data into a target system, and handling large volumes of incoming API data. Mule's batch jobs feature introduced in 2013 simplifies processing massive amounts of data through parallelization, record-based reporting and error handling.
Mule batch processing
Batch processing in Mule allows splitting messages into individual records, performing actions on each record in parallel, and reporting results. It is useful for integrating or synchronizing large datasets between systems, extracting/transforming/loading data into a target system, and handling large volumes of incoming API data. Mule's batch jobs simplify processing massive amounts of data through record-based reporting, error handling, and parallel processing to improve reliability.
Mule batch processing
Batch processing in Mule allows splitting messages into individual records, performing actions on each record in parallel, and reporting results. It is useful for integrating or synchronizing large datasets between systems, extracting/transforming/loading data into a target system, and handling large volumes of incoming API data. Mule's batch jobs simplify processing massive amounts of data through record-based reporting, error handling, and parallel processing to improve reliability.
Mule batch processing
Batch processing in Mule allows splitting messages into individual records, performing actions on each record in parallel, and reporting results. It is useful for integrating or synchronizing large datasets between systems, extracting/transforming/loading data into a target system, and handling large volumes of incoming API data. Mule's batch jobs simplify processing massive amounts of data through record-based reporting, error handling, and parallel processing to improve reliability.
Mule batch processing
Batch processing in Mule allows splitting messages into individual records, performing actions on each record in parallel, and reporting results. It is useful for integrating or synchronizing large datasets between systems, extracting/transforming/loading data into a target system, and handling large volumes of incoming API data. Mule's batch jobs simplify processing massive amounts of data with record-based reporting, error handling, and parallel processing.
Mule Esb Batch process
Batch processing in Mule allows splitting messages into individual records, performing actions on each record, and reporting results. It is useful for integrating or synchronizing data between systems, extracting/transforming/loading data into a target system, and handling large volumes of incoming API data. Mule's batch jobs introduced in 2013 simplify processing large amounts of data with parallel processing, record-based reporting and error handling. Improvements were added in 2015 for error handling in batches of millions of records and near real-time synchronization using polling, watermarking, and batch modules.
Mulesoft anypoint batch processing
Mule allows processing of messages in batches through batch jobs. A batch job splits incoming messages into individual records, processes each record using message processors, and reports results. Batch processing is useful for streaming data integration, synchronizing data between applications like Salesforce and Netsuite, ETL processes, and handling large API data. A batch job exists outside flows and contains steps to sequentially process records through variables and MEL expressions.
Mule batch processing
Batch processing in Mule allows processing of messages in batches by initiating batch jobs. A batch job splits messages into individual records, performs actions on each record in parallel, and reports results while optionally outputting processed data to other systems. Batch processing is useful for integrating or synchronizing data between applications, extracting-transforming-loading data from flat files to databases, and handling large volumes of API data. Mule's batch feature introduced in 2013 simplifies processing large amounts of data through record-based reporting, error handling, and parallel processing.
Mule concepts
- Mule is an event-driven architecture that processes messages through flows and batch jobs. Flows contain a series of message processors that accept and process messages, while batch jobs process large messages as records.
- The basic building blocks of flows are message sources that receive messages, transformers that convert message formats, and components that contain business logic. Messages move through processors, filters, and routers that route messages down different paths.
- Batch jobs split large messages into records and process them asynchronously across batch steps like flows process messages. Batch jobs execute when triggered and produce reports upon completion.
Mule batch processing
Mule provides batch processing capabilities that allow applications to split incoming messages into individual records, perform actions on each record in parallel, and report results. Batch processing is useful for integrating and synchronizing data between systems, extracting and loading large amounts of data into target systems, and handling large volumes of API data into legacy systems. It involves splitting messages into records, executing actions on each record in batches, and reporting outcomes.
Mule fundamentals
Mule is a lightweight integration platform that enables connecting systems, services, APIs and devices together. It handles message routing, data mapping, orchestration, reliability, security and scalability between nodes. With Mule, users can integrate on-premise or cloud applications, build and expose APIs, create interfaces for mobile consumption, and connect business-to-business activities. Mule uses event-driven architecture and processes messages through flows containing message processors that accept and process messages. It can also process large messages through batch jobs that split messages into records.
Anypoint Batch Processing and Polling Scope With Mulesoft
Batch processing in MuleSoft can handle large quantities of incoming data, perform ETL tasks, and enable near real-time integration between systems. It works by splitting large messages into individual records that are processed asynchronously within batch jobs. Poll scopes can retrieve new data from resources on a fixed or cron-based schedule. A batch job contains input, process, and on complete phases where records are loaded, processed asynchronously in batches, and a summary report is generated. The example creates a batch job to synchronize data from a MySQL database to Salesforce using a poll scope, watermarking, transforms, and a batch commit.
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Exceções do sistema
O documento descreve diferentes estratégias de tratamento de erros no Mule, incluindo estratégias de exceção de sistema, estratégias de exceção de mensagens, filtros de exceção e estratégias de reconexão. Cinco tipos de estratégias de exceção de mensagens são descritos: Default, Catch, Rollback, Reference e Choice, cada um com comportamentos diferentes para lidar com exceções lançadas durante o processamento de mensagens. Estratégias de reconexão controlam como os conectores lidam com falhas de cone
Conectores mule
1) O documento descreve vários conectores Mule, incluindo conectores para Salesforce, arquivos, bancos de dados JDBC e relacionais.
2) O conector Salesforce requer uma conta de desenvolvedor Salesforce e tokens de segurança para acessar APIs.
3) Os conectores de arquivo e JDBC podem ser usados como pontos de entrada ou saída e configurados para ler ou gravar arquivos/bancos de dados.
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Batch processing in Mule allows splitting messages into individual records that are processed asynchronously and in parallel. A batch job contains steps that act on each record, and reports results. Batch processing is useful for integrating or synchronizing large datasets between systems, ETL, and handling large API data. Records are processed across phases including input, load/dispatch, processing by steps, and optional reporting on completion.
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Batch processing in Mule allows splitting messages into individual records, performing actions on each record in parallel, and reporting results. It is useful for integrating or synchronizing large datasets between systems, extracting/transforming/loading data into a target system, and handling large volumes of incoming API data. Mule's batch jobs simplify processing massive amounts of data with record-based reporting, error handling, and parallel processing.
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Batch processing in Mule allows splitting messages into individual records, performing actions on each record in parallel, and reporting results. It is useful for integrating or synchronizing large datasets between systems, extracting/transforming/loading data into a target system, and handling large volumes of incoming API data. Mule's batch jobs simplify processing massive amounts of data with record-based reporting, error handling, and parallel processing.
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Batch processing in Mule allows splitting messages into individual records, performing actions on each record in parallel, and reporting results. It is useful for integrating or synchronizing large datasets between systems, extracting/transforming/loading data into a target system, and handling large volumes of incoming API data. Mule's batch jobs simplify processing massive amounts of data through record-based reporting, error handling, and parallel processing to improve reliability.
Mule batch processing
Batch processing in Mule allows splitting messages into individual records, performing actions on each record in parallel, and reporting results. It is useful for integrating or synchronizing large datasets between systems, extracting/transforming/loading data into a target system, and handling large volumes of incoming API data. Mule's batch jobs simplify processing massive amounts of data with record-based reporting, error handling, and parallel processing.
Mule batch processing
Batch processing in Mule allows splitting messages into individual records, performing actions on each record in parallel, and reporting results. It is useful for integrating or synchronizing large datasets between systems, extracting/transforming/loading data into a target system, and handling large volumes of incoming API data. Mule's batch jobs feature introduced in 2013 simplifies processing massive amounts of data through parallelization, record-based reporting and error handling.
Mule batch processing
Batch processing in Mule allows splitting messages into individual records, performing actions on each record in parallel, and reporting results. It is useful for integrating or synchronizing large datasets between systems, extracting/transforming/loading data into a target system, and handling large volumes of incoming API data. Mule's batch jobs simplify processing massive amounts of data through record-based reporting, error handling, and parallel processing to improve reliability.
Mule batch processing
Batch processing in Mule allows splitting messages into individual records, performing actions on each record in parallel, and reporting results. It is useful for integrating or synchronizing large datasets between systems, extracting/transforming/loading data into a target system, and handling large volumes of incoming API data. Mule's batch jobs simplify processing massive amounts of data through record-based reporting, error handling, and parallel processing to improve reliability.
Mule batch processing
Batch processing in Mule allows splitting messages into individual records, performing actions on each record in parallel, and reporting results. It is useful for integrating or synchronizing large datasets between systems, extracting/transforming/loading data into a target system, and handling large volumes of incoming API data. Mule's batch jobs simplify processing massive amounts of data through record-based reporting, error handling, and parallel processing to improve reliability.
Mule batch processing
Batch processing in Mule allows splitting messages into individual records, performing actions on each record in parallel, and reporting results. It is useful for integrating or synchronizing large datasets between systems, extracting/transforming/loading data into a target system, and handling large volumes of incoming API data. Mule's batch jobs simplify processing massive amounts of data with record-based reporting, error handling, and parallel processing.
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Batch processing in Mule allows splitting messages into individual records, performing actions on each record, and reporting results. It is useful for integrating or synchronizing data between systems, extracting/transforming/loading data into a target system, and handling large volumes of incoming API data. Mule's batch jobs introduced in 2013 simplify processing large amounts of data with parallel processing, record-based reporting and error handling. Improvements were added in 2015 for error handling in batches of millions of records and near real-time synchronization using polling, watermarking, and batch modules.
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Mule allows processing of messages in batches through batch jobs. A batch job splits incoming messages into individual records, processes each record using message processors, and reports results. Batch processing is useful for streaming data integration, synchronizing data between applications like Salesforce and Netsuite, ETL processes, and handling large API data. A batch job exists outside flows and contains steps to sequentially process records through variables and MEL expressions.
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Batch processing in Mule allows processing of messages in batches by initiating batch jobs. A batch job splits messages into individual records, performs actions on each record in parallel, and reports results while optionally outputting processed data to other systems. Batch processing is useful for integrating or synchronizing data between applications, extracting-transforming-loading data from flat files to databases, and handling large volumes of API data. Mule's batch feature introduced in 2013 simplifies processing large amounts of data through record-based reporting, error handling, and parallel processing.
Mule concepts
- Mule is an event-driven architecture that processes messages through flows and batch jobs. Flows contain a series of message processors that accept and process messages, while batch jobs process large messages as records.
- The basic building blocks of flows are message sources that receive messages, transformers that convert message formats, and components that contain business logic. Messages move through processors, filters, and routers that route messages down different paths.
- Batch jobs split large messages into records and process them asynchronously across batch steps like flows process messages. Batch jobs execute when triggered and produce reports upon completion.
Mule batch processing
Mule provides batch processing capabilities that allow applications to split incoming messages into individual records, perform actions on each record in parallel, and report results. Batch processing is useful for integrating and synchronizing data between systems, extracting and loading large amounts of data into target systems, and handling large volumes of API data into legacy systems. It involves splitting messages into records, executing actions on each record in batches, and reporting outcomes.
Mule fundamentals
Mule is a lightweight integration platform that enables connecting systems, services, APIs and devices together. It handles message routing, data mapping, orchestration, reliability, security and scalability between nodes. With Mule, users can integrate on-premise or cloud applications, build and expose APIs, create interfaces for mobile consumption, and connect business-to-business activities. Mule uses event-driven architecture and processes messages through flows containing message processors that accept and process messages. It can also process large messages through batch jobs that split messages into records.
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Batch processing in MuleSoft can handle large quantities of incoming data, perform ETL tasks, and enable near real-time integration between systems. It works by splitting large messages into individual records that are processed asynchronously within batch jobs. Poll scopes can retrieve new data from resources on a fixed or cron-based schedule. A batch job contains input, process, and on complete phases where records are loaded, processed asynchronously in batches, and a summary report is generated. The example creates a batch job to synchronize data from a MySQL database to Salesforce using a poll scope, watermarking, transforms, and a batch commit.
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2) O conector Salesforce requer uma conta de desenvolvedor Salesforce e tokens de segurança para acessar APIs.
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In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Programming Foundation Models with DSPy - Meetup Slides
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Mind map of terminologies used in context of Generative AI
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Driving Business Innovation: Latest Generative AI Advancements & Success Story
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Building Production Ready Search Pipelines with Spark and Milvus
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
Presentation of the OECD Artificial Intelligence Review of Germany
Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm
HCL Notes and Domino License Cost Reduction in the World of DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
20240607 QFM018 Elixir Reading List May 2024
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TrustArc Webinar - 2024 Global Privacy Survey
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
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- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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Mule batch
- 2. Mule ESB has the ability to process messages in batches. Within an application, we can initiate a batch job which is basically a block of code that splits a large messages into individual records, then performs actions upon each record, then reports on the results and potentially pushes the processed output to other systems or queues. This functionality is particularly useful when working with a large set of data for example Database where a large set of Data is required to retrieve or insert in a bulk. Source:- MuleSoft
- 3. A batch job is a top-level element in Mule which exists outside all Mule flows. Generally a batch jobs split a large messages into small parts which is called records and which Mule processes asynchronously in a batch job; similarly just as flows process messages, batch jobs process records. A batch job contains one or more batch steps which, in turn, can contain any number of message processors or Mule component that act upon records as they move through the batch job. A batch job executes when triggered by either a batch executor in a Mule flow or a message source in a batch-accepting input which is when triggered, will create a new batch job instance. After all record are processed and passed though all the batch steps, the batch job instances is ended and result of all the batch jobs are accumulated and summarised in a report that reflect which batch job as succeed and which job has failed at the time of processing. Source:- MuleSoft
- 4. Structure of batch job in Mule Config :- <batch:job name="myBatchJobSteps"> <batch:process-records> <batch:step name="Step1"/> <batch:step name="Step2"/> <batch:step name="Step3"/> <batch:step name="Step4"/> ……………………. </batch:process-records> </batch:job>
- 5. The batch processing is useful in particular scenario :- • When a large message fails, for example a database insertion in bulk, we can continue with the rest. • When integrating a data sets, that may be small or large, streaming or not, to parallel process records. • When it require to handle a large quantities of incoming data from an API into a legacy system. • Synchronising data sets between various business applications. Example Netsuite and Salesforce Source:- MuleSoft
- 6. There are basically 4 parts in Mule batch Job:- Input phase: In Input phase, we can place a one-way message source and/or message processors to prepare the data that is actually going to be fed into the job. Here we are processing synchronously at a message level and this is an optional phase. Loading phase: This phase is automatic and implicit and we don’t have to do anything here. In this phase payload from Input phase are split into records and stored in persistent queues. Process phase: In this phase, each records is processed separately and independently and after that it is moved across the steps in an asynchronous and paralleled fashion. On Complete phase: In this phase you get a result object that tells you how many records were processed, how many succeeded, which ones failed (and in which step), etc. This is useful for generating reports and sending out notifications Source:- MuleSoft
- 7. Let’s consider we have a simple Mule flow as following:- Here , we can see there are 3 phases :- In Input phase it is retrieving the data from databases at a fixed interval of time In Process phase it is executing 3 steps .. In step 1, it is inserting data into database.. In step 2 it is logging messages if any and in step 3, it is logging failed messages if any. In complete phase it log the Number of failed Records , Number of successful Records and Elapsed Time
- 8. So, our Mule config will be as following:-
- 9. So, if we run our flow, we will see the following in log :- Here you can see the Number of failed Records , Number of successful Records and Elapsed Time in the log
- 10. So, here you can see how to use a Mule batch module. It’s pretty simple and effective in handling larges set of data
- 11. In my next slide I will bring some other techniques in Mule implementation . Hope you have enjoyed this simpler version. Keep sharing your knowledge and let our Mule community grow