Mule transformers
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Mule Transformers prepare messages for further processing by altering message properties, variables, or payload. Transformers include the Anypoint DataMapper Transformer which can both transform data formats and map fields, such as mapping a "last_name" field to a "family_name" field. Out of the box, Mule provides standard transformers to handle common data transformation scenarios with minimal configuration.
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Mule transformers
Mule Transformers prepare messages for further processing by altering message properties, variables, or payload. Transformers include the Anypoint DataMapper Transformer which can both transform data formats and map input fields to different output fields. Out of the box, Mule provides standard transformers to handle common data transformation scenarios with minimal configuration.
Elements in a muleflow
This document describes various elements that compose Mule flows, including connectors, components, transformers, and exception handling strategies. Connectors receive and send messages from external sources and can act as sources, processors, or destinations in a flow. Components enable custom business logic without code. Transformers prepare messages for further processing by altering properties, variables, or payloads. Exception strategies precisely handle errors that occur in flows.
Excavating the Hidden Parallelism Inside DRAM Architectures With Buffered Com...
Excavating the Hidden Parallelism Inside DRAM Architectures With Buffered Com...JAYAPRAKASH JPINFOTECH
The document proposes an approach called buffered compares that places a small buffer and simple ALU per DRAM bank to exploit parallelism inside DRAM architectures. It defines new DRAM commands to fill buffers, perform operations, and return results. This reduces processor-memory communication and accelerates applications by utilizing parallel compares across banks. Experimental results show significant performance and efficiency improvements on tested workloads. The approach can be implemented with minimal changes to existing DDR interfaces.Xml to-object-transformer
Mule provides a transformer that converts XML data into objects by using annotation decorated model classes, requiring the name of the component, fully qualified name of the root class, colon separated package name of model classes, and MIME type. The transformer allows a simple flow to transform XML data into objects using annotated model classes.
Mule scopes foreach_scope
The Foreach scope allows processing of each element in a collection iteratively without losing any message payload data. It splits collections into individual elements, processes them with message processors inside the scope, and returns the original message rather than aggregating into a new collection. This avoids issues like losing XML metadata or needing to transform collection types when using split-aggregate processing. The Foreach scope can iterate over various collection types and properties. It does not make deep copies during processing and changes to element values will persist in the returned message.
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Anypoint Studio Transformers helps us to transform the message to required format which helps in easy integration with other systems. You can use in built transformers given by Mule or you can develop a new custom on your own.
Mule Expression Transformer
The Expression Transformer evaluates expressions on a message and returns the results. It can return a single result or an array of results from multiple expressions. It has configuration options to set the display name, return class, encoding, MIME type, and expressions to transform the message data. The Advanced tab allows adding multiple evaluators to transform message properties. The Notes and Metadata tabs add notes and metadata to the transformer.
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Mule transformers
Mule Transformers prepare messages for further processing by altering message properties, variables, or payload. Transformers include the Anypoint DataMapper Transformer which can both transform data formats and map input fields to different output fields. Out of the box, Mule provides standard transformers to handle common data transformation scenarios with minimal configuration.
Elements in a muleflow
This document describes various elements that compose Mule flows, including connectors, components, transformers, and exception handling strategies. Connectors receive and send messages from external sources and can act as sources, processors, or destinations in a flow. Components enable custom business logic without code. Transformers prepare messages for further processing by altering properties, variables, or payloads. Exception strategies precisely handle errors that occur in flows.
Excavating the Hidden Parallelism Inside DRAM Architectures With Buffered Com...
Excavating the Hidden Parallelism Inside DRAM Architectures With Buffered Com...JAYAPRAKASH JPINFOTECH
The document proposes an approach called buffered compares that places a small buffer and simple ALU per DRAM bank to exploit parallelism inside DRAM architectures. It defines new DRAM commands to fill buffers, perform operations, and return results. This reduces processor-memory communication and accelerates applications by utilizing parallel compares across banks. Experimental results show significant performance and efficiency improvements on tested workloads. The approach can be implemented with minimal changes to existing DDR interfaces.Xml to-object-transformer
Mule provides a transformer that converts XML data into objects by using annotation decorated model classes, requiring the name of the component, fully qualified name of the root class, colon separated package name of model classes, and MIME type. The transformer allows a simple flow to transform XML data into objects using annotated model classes.
Mule scopes foreach_scope
The Foreach scope allows processing of each element in a collection iteratively without losing any message payload data. It splits collections into individual elements, processes them with message processors inside the scope, and returns the original message rather than aggregating into a new collection. This avoids issues like losing XML metadata or needing to transform collection types when using split-aggregate processing. The Foreach scope can iterate over various collection types and properties. It does not make deep copies during processing and changes to element values will persist in the returned message.
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The document summarizes the architecture of a Teradata database system. It consists of nodes that each contain a parsing engine, BYNET messaging layer, and access module processors (AMPs). The parsing engine receives queries, prepares execution plans, and returns results. BYNET passes data between the parsing engine and AMPs. AMPs store and retrieve data from disks and perform query operations like filtering and aggregation. Records are distributed evenly among AMPs.
Mule esb transformers
Anypoint Studio Transformers helps us to transform the message to required format which helps in easy integration with other systems. You can use in built transformers given by Mule or you can develop a new custom on your own.
Mule Expression Transformer
The Expression Transformer evaluates expressions on a message and returns the results. It can return a single result or an array of results from multiple expressions. It has configuration options to set the display name, return class, encoding, MIME type, and expressions to transform the message data. The Advanced tab allows adding multiple evaluators to transform message properties. The Notes and Metadata tabs add notes and metadata to the transformer.
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Distributed Database Designs are nothing but multiple, logically related Database systems, physically distributed over several sites, using a Computer Network, which is usually under a centralized site control.
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Given a database and its workload, how should the database be split and allocated to sites so as to optimize certain objective function
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(ii) Data allocation which determines how the fragments should be allocated.
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This document discusses distributed database management systems (DBMS). It covers topics like distributed database design, distributed query processing, and transaction management in a distributed environment. It also discusses parallel DBMS architectures like shared memory, shared disk, and shared nothing and techniques for data partitioning, parallel query processing and optimization in a distributed parallel DBMS.
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DATASTAGE ONLINE TRAINING
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Mule transformers
Mule Transformers can alter message properties, variables, or payloads to prepare them for further processing. Standard transformers are provided to handle common data conversion scenarios, such as XML-to-Object. If no single transformer achieves the needed output, multiple transformers can be used sequentially. Transformer categories include those for Java Objects, Content, SAP, Scripting, Properties/Variables/Attachments. The DataWeave transformer provides powerful data querying and transformation capabilities.
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The presentation focus on details regarding how distributed database work. Its issues and methodologies. Basic techniques on how to design distributed systems.
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- Messages entering flows can be validated, enriched, transformed, processed by custom logic, logged, and used to determine a response.
- The main building blocks are optional message sources, message processors, message processing blocks, and endpoints.
- Message sources receive incoming messages to trigger flow instances. Message processors perform actions like validation and transformation. Processing blocks combine multiple processors. Endpoints implement transport channels to insert and extract data.
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Transformers convert message payloads between formats. There are several types of built-in transformers in Mule Studio for tasks like XML to JSON conversion. Custom transformers can be created by annotating methods or by extending the AbstractTransformer class and implementing the transform logic.
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Database , 1 Introduction
The document outlines the key topics covered in a textbook on distributed database management systems (DDBMS). It discusses what constitutes a distributed DBMS, highlights the promises of distributed DBMS like transparency and improved performance/reliability. It also examines some of the main challenges in distributed DBMS like distributed query processing, concurrency control, and reliability. Finally, it discusses the relationship between the different issues in distributed DBMS design.
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Chapter02
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1 introduction ddbms
The document outlines the key topics covered in a book on distributed database management systems (DDBMS). It discusses distributed DBMS architecture and how data is distributed across multiple sites connected by a network. It also covers important DDBMS concepts like distributed database design, distributed query processing, concurrency control, and reliability. The relationships between these distributed database issues are interdependent. The goal of a DDBMS is to provide transparent access to data located across a computer network.
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This presentation explains Mule En-richer Component with an example and also Complex Enrichment with its graphical flows.
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The document discusses several Mule scopes including:
- Composite Source which accepts messages from multiple input channels and triggers processing when a message enters from any supported channel.
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- Message Enricher which appends information to a message payload using an expression.
- Poll which periodically polls an embedded receiver for new messages.
- Sub Flow which defines a reusable flow that can be called from another flow.
- Transactional which groups steps that must succeed or fail together in a transaction.
- Until Successful which retries message processing at an interval until it succeeds or exceeds retries.
Mule scopes async_scope
The document discusses Mule's async scope which allows for asynchronous processing within a message flow. The async scope executes simultaneously with the parent flow by sending a copy of the message to the first processor in the async scope and another copy to the next processor in the main flow. It does not support request-response but instead uses processing strategies like queued-asynchronous. The document provides instructions on adding an async scope to a flow and configuring it including optional processing strategies.
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Mule transformers strictly enforce the types of incoming and outgoing data by default, but this can be configured to be more relaxed. Transformers can change the payload type, format, or message properties. Mule provides many built-in transformers across its core and modules to handle common transformation needs like payload type changes or property modifications. Individual transformer configurations can ignore invalid inputs or enforce a specific return type.
Cassandra Data Migration
EMT is a data migration solution that provides concise summaries of documents. It uses parallel processing to maximize performance during data extraction, validation, and loading. The solution includes adapters to integrate with various target systems, such as Oracle Database, Siebel, and Apache Cassandra. It reduces the costs and complexity of large-scale migration projects through features like automatic process execution monitoring, error handling, and performance reporting.
DISTRIBUTED DATABASE WITH RECOVERY TECHNIQUES
Distributed Database Designs are nothing but multiple, logically related Database systems, physically distributed over several sites, using a Computer Network, which is usually under a centralized site control.
Distributed database design refers to the following problem:
Given a database and its workload, how should the database be split and allocated to sites so as to optimize certain objective function
There are two issues:
(i) Data fragmentation which determines how the data should be fragmented.
(ii) Data allocation which determines how the fragments should be allocated.
Database ,14 Parallel DBMS
This document discusses distributed database management systems (DBMS). It covers topics like distributed database design, distributed query processing, and transaction management in a distributed environment. It also discusses parallel DBMS architectures like shared memory, shared disk, and shared nothing and techniques for data partitioning, parallel query processing and optimization in a distributed parallel DBMS.
Lect 07 data replication
This document provides an overview of data replication in SQL Server 2012. It discusses key concepts like publications, articles, subscribers and distributors. It explains the different types of replication including snapshot, transactional and merge replication. Snapshot replication copies the entire dataset each time. Transactional replication uses logs to replicate changes in real-time. Merge replication allows bidirectional synchronization so changes can occur at both the publisher and subscriber. The document is intended as a lecture on data replication fundamentals.
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The document provides an overview of Java Messaging Services (JMS) in MuleSoft Mule ESB 3.6. It describes key JMS concepts like the JMS messaging model with queues and topics, backchannels, message filtering using selectors, transformers, and provides an example ActiveMQ configuration with inbound and outbound endpoints.
DATASTAGE ONLINE TRAINING
DATASTAGE Online Training by real time Experts with live projects in Hyderabad India. Trainingicon provides complete in-depth Online training on DATASTAGE scenarios
Mule transformers
Mule Transformers can alter message properties, variables, or payloads to prepare them for further processing. Standard transformers are provided to handle common data conversion scenarios, such as XML-to-Object. If no single transformer achieves the needed output, multiple transformers can be used sequentially. Transformer categories include those for Java Objects, Content, SAP, Scripting, Properties/Variables/Attachments. The DataWeave transformer provides powerful data querying and transformation capabilities.
Distributed Database Management System(DDMS)
The presentation focus on details regarding how distributed database work. Its issues and methodologies. Basic techniques on how to design distributed systems.
Working with components
This document discusses the key building blocks of Mule flows:
- Messages entering flows can be validated, enriched, transformed, processed by custom logic, logged, and used to determine a response.
- The main building blocks are optional message sources, message processors, message processing blocks, and endpoints.
- Message sources receive incoming messages to trigger flow instances. Message processors perform actions like validation and transformation. Processing blocks combine multiple processors. Endpoints implement transport channels to insert and extract data.
Ashok mule esb
Transformers convert message payloads between formats. There are several types of built-in transformers in Mule Studio for tasks like XML to JSON conversion. Custom transformers can be created by annotating methods or by extending the AbstractTransformer class and implementing the transform logic.
IMSDB - DBRC
DBRC (Database Recovery Control) is an IMS tool that controls logging and recovery of IMS databases. It stores database recovery information in RECON (Recovery Control) data sets. DBRC manages log control, recovery control, and share control through these records. It controls online log data sets, secondary log data sets, and other logs to enable full or time stamp recovery of databases. DBRC also controls database sharing levels to determine which subsystems can access and update databases.
Database , 1 Introduction
The document outlines the key topics covered in a textbook on distributed database management systems (DDBMS). It discusses what constitutes a distributed DBMS, highlights the promises of distributed DBMS like transparency and improved performance/reliability. It also examines some of the main challenges in distributed DBMS like distributed query processing, concurrency control, and reliability. Finally, it discusses the relationship between the different issues in distributed DBMS design.
Xml to xml transformation
The document discusses using XSLT in Mule to transform an XML payload from one format to another. An example is provided where an input XML payload containing name and id elements is transformed into an output XML payload that adds a designation element. This is done by using an XSLT transformer in a Mule flow along with an XSL stylesheet that maps the input to output XML formats and adds the new element. The flow demonstrates how XSLT can easily modify XML payloads between different formats in Mule applications.
Adbms 27 parallel database distribution architecture
Parallel database architectures allow multiple processors to control multiple disk units containing partitions of a database. There are several types of architectures including shared memory, shared disk, and shared nothing. In shared memory, all processors access the same memory and disks. In shared disk, each processor has exclusive memory access but shared disks. In shared nothing, each processor has exclusive memory and disk control but can communicate. Careful data partitioning across disks is important to allow parallel query processing.
Mule Schema Validation Filter
The Schema Validation filter validates incoming XML messages against an XSD schema. It can be used before message processors to determine if the message should continue in the flow based on validation results. The filter configuration allows specifying the schema location, language, and error handling. A resource resolver can also be configured to locate schemas, with options to define its class, scope, initialization, and factory settings.
Chapter02
The document discusses database system concepts and architecture. It covers topics such as data models, schemas, instances, the three-schema architecture, data independence, DBMS languages, interfaces, utilities, centralized and client-server architectures, and classifications of DBMSs. The three-schema architecture defines schemas at the internal, conceptual, and external levels to support data independence. DBMS languages include data definition languages to define schemas and data manipulation languages to retrieve and update data. Client-server architectures distribute the database system across network tiers, with specialized database, application, and interface servers.
Advanatages csc
The document discusses the advantages and disadvantages of client-server computing. Some key advantages include optimization of resources through dividing functions between clients and servers, scalability by replacing servers as needs increase, and reduced network traffic through high-level queries. Additional advantages are flexibility in accessing the same data through different interfaces, leveraging IT staff through end-user tools, and centralized administration and management of resources. Potential disadvantages include network congestion from many client requests, a single point of failure if the server fails, and higher costs of installation and maintenance compared to peer-to-peer systems.
1 introduction ddbms
The document outlines the key topics covered in a book on distributed database management systems (DDBMS). It discusses distributed DBMS architecture and how data is distributed across multiple sites connected by a network. It also covers important DDBMS concepts like distributed database design, distributed query processing, concurrency control, and reliability. The relationships between these distributed database issues are interdependent. The goal of a DDBMS is to provide transparent access to data located across a computer network.
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- Foreach which splits a message collection into individual messages, processes them, and aggregates the results.
- Message Enricher which appends information to a message payload using an expression.
- Poll which periodically polls an embedded receiver for new messages.
- Sub Flow which defines a reusable flow that can be called from another flow.
- Transactional which groups steps that must succeed or fail together in a transaction.
- Until Successful which retries message processing at an interval until it succeeds or exceeds retries.
Mule scopes async_scope
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The Mule Message Enricher allows processing of the message without modifying the original payload. It provides a scope to add message processors that perform tasks like calling external services and extracting values from the response, without changing the original message. For example, a flow can use the enricher to call a web service, extract a value from the response into a variable, and log both the variable and original payload at the end, keeping the payload unchanged throughout.
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Scatter gather flow control
The document discusses scatter-gather flow control in Mulesoft. Scatter-gather sends a request message concurrently to multiple targets and aggregates the responses. It replaces the deprecated all message processor by executing routes in parallel instead of sequentially, improving efficiency. Scatter-gather collects results into a message collection. If a route fails, it sets the exception payload and throws a composite exception mapping failures to routes.
Datasense
This document discusses testing connections to external resources in Mulesoft DataSense. It explains that when configuring connectors in Studio, you can test the connection to ensure it is working properly. It provides details on testing new connections, such as filling out the configuration values and testing before saving, and on testing existing connections to identify potential issues. Limitations around call limits are also covered.
Choice flow control reference
The document describes the choice flow control in Mulesoft, which dynamically routes messages based on message properties or payload. It uses expressions to evaluate the message and route it to the first matching routing option, or the default route if none match. The document provides details on adding, configuring, and changing the default route of the choice flow control, and includes an example code snippet.
Soa(service oriented architecture)
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This document outlines the key stages of a Service Oriented Architecture (SOA) project lifecycle:
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Soa planning
This document discusses key factors for planning a successful SOA initiative:
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- Cooperation across multiple project teams is required, unlike traditional siloed applications, as services involve many teams.
- Teams must use a common framework of vocabulary, concepts, and understanding of the target state to build reliability and trust.
- Consistency in applying knowledge and practices is critical for success, as teams must be disciplined in their roles and methodology.
Soa methodology
This document discusses an SOA methodology and service layers. It introduces SOA methodology as a way to reduce risk by providing processes and practices for SOA projects. There are three common types of services: entity services which represent business entities, task services which are associated with business tasks and processes, and utility services which provide non-business logic. Each service type has a different focus and level of reuse potential.
Soa governance
This document discusses SOA governance and its key components. It defines governance as a system for administering and making decisions within an organization. A governance system establishes who can make decisions, constraints on decisions, and consequences for noncompliance. The primary building blocks of a governance system are precepts (authoritative rules), processes (activities), people (roles), and metrics (measuring compliance). Precepts determine authority and constraints. Processes control and guide decisions. People make decisions within precept constraints. Metrics measure compliance with precepts.
Principles of soa
This document discusses the key principles of service-oriented architecture (SOA), including standardized service contracts, loose coupling between services, reusability of services, service autonomy, and statelessness. Services should comply with common contract standards, have low dependencies on other services or their environment, contain reusable logic, control their runtime environment, and minimize state when possible. Following these principles allows solutions to gain the benefits of SOA.
Mule exception strategies
Mule provides exception strategies to handle errors that occur within Mule flows. There are two categories of exceptions: system exceptions which occur during startup or connection failures, and are handled by system strategies; and messaging exceptions which occur during message processing and are handled by messaging strategies. When a message throws an exception, normal flow stops and the message is passed to the messaging exception strategy containing message processors to handle the error.
Mule components
Mule components allow you to add custom business logic to flows without writing Mule-specific code. Components include scripting components to add custom scripts, web service components to expose and consume web services, and an HTTP component to make HTTP calls. Examples demonstrate using a Groovy script component and various other common components like logger, echo, and flow reference.
Mule agent architecture
The document provides an overview of the main components and architecture of the Mule Agent, which exposes the Mule ESB Java API as a service allowing external systems to manipulate and monitor Mule instances. The key components are the Mule Service for connecting to the Mule API, the Transport for handling communication, External Message Handlers for exposing the API and routing requests, and Internal Message Handlers for handling Mule notifications. Communication can be synchronous, asynchronous, or push-based. The architecture diagram shows how the components interact through the transport layer.
Global elements
Global elements in Mule allow configurations to be defined once and then referenced across multiple flows. This provides flexibility, reusability, and efficiency compared to repeating the same configuration in multiple places. Common examples of global elements include HTTP, JDBC, and JMS connectors. To create a global element, you define the configuration with attributes like username and password, then reference it using config-ref when needed in flows.
Flows and subflows
Flows and subflows allow for breaking up complex Mule applications into discrete processing units. A flow processes messages, and can route them to other flows or subflows. Subflows process messages synchronously and inherit processing strategies, pausing the triggering flow. Synchronous flows also process synchronously but do not inherit strategies. Asynchronous flows process messages independently and in parallel to the triggering flow, allowing for improved performance.
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Mule transformers
- 2. • In a Mule flow, a Transformer prepares a message for further processing by enhancing or altering the contents of the message properties, variables, or payload. Data transformation is one of the most powerful functionalities of Mule: rather than spending a lot of time building a customized connection between resources or processors, you can just use a pre-built transformer to perform a standard data conversion
- 3. • Transformer Library • Examples of transformers in Mule fall into these categories: • Anypoint DataMapper Transformer • DataMapper Transformer Example
- 4. Transformer Library • Out of the box, Mule provides a set of standard transformers to handle the most common data transformation scenarios. Typically, these elements require minimal configuration so as to facilitate quick construction of applications that must juggle different data formats between resources and processors
- 5. Anypoint DataMapper Transformer • Beyond transformation, there is one type of message processor in Mule that both converts and maps data: the Anypoint™ DataMapper Transformer. In addition to transforming data from one format to another, DataMapper can map an input field, such as last_name, to a different output field, such as family_name (see image below)
- 6. • Even better, it can map multiple fields, such as title, first_name, and last_name, to a composite output field such as full_name. It can retrieve session state information in a message to facilitate conditional message routing, it can use Mule expression evaluation to facilitate conditional value recalculation, it can even look up information in tables or other flows.
- 7. DataMapper Transformer Example • <data-mapper:config name="datamapper_grf" transformationGraphPath="datamapper.grf" doc:name="DataMapper"/> • • <flow name="Contacts_to_SFDC" doc:name="Contacts_to_SFDC" doc:description="Upload a CSV file of contact information into Salesforce as new contacts."> • <file:inbound-endpoint path="src/test/resources/input" moveToDirectory="src/test/resources/output" pollingFrequency="10000" responseTimeout="10000" doc:name="File Input"/> • <data-mapper:transform config-ref="datamapper_grf" doc:name="DataMapper"/> • <sfdc:create config-ref="Salesforce" type="Contact" doc:name="Salesforce"> • <sfdc:objects ref="#[payload]"/>