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Alegra + Amazon EC2 Integrations

Appy Pie Connect allows you to automate multiple workflows between Alegra and Amazon EC2

  • No code
  • No Credit Card
  • Lightning Fast Setup
About Alegra

Alegra is an accounting and billing app designed for Latin American managers.

About Amazon EC2

Amazon Elastic Compute Cloud (Amazon EC2) is a web service provides secure, reliable, scalable, and low-cost computational resources. It gives developers the tools to build virtually any web-scale application.

Amazon EC2 Integrations

Best ways to Integrate Alegra + Amazon EC2

  • Alegra Amazon EC2

    Alegra + Amazon EC2

    Start Stop or Reboot Instance in Amazon EC2 when New Contact is created in Alegra Read More...
    Close
    When this happens...
    Alegra New Contact
     
    Then do this...
    Amazon EC2 Start Stop or Reboot Instance
  • Alegra Amazon EC2

    Alegra + Amazon EC2

    Start Stop or Reboot Instance in Amazon EC2 when New Item is created in Alegra Read More...
    Close
    When this happens...
    Alegra New Item
     
    Then do this...
    Amazon EC2 Start Stop or Reboot Instance
  • Alegra Amazon EC2

    Alegra + Amazon EC2

    Start Stop or Reboot Instance in Amazon EC2 when New Invoice is created in Alegra Read More...
    Close
    When this happens...
    Alegra New Invoice
     
    Then do this...
    Amazon EC2 Start Stop or Reboot Instance
  • Alegra Amazon EC2

    Alegra + Amazon EC2

    Start Stop or Reboot Instance in Amazon EC2 when New Estimate is created in Alegra Read More...
    Close
    When this happens...
    Alegra New Estimate
     
    Then do this...
    Amazon EC2 Start Stop or Reboot Instance
  • Alegra Alegra

    Amazon EC2 + Alegra

    Create Contact to Alegra from New Scheduled Event in Amazon EC2 Read More...
    Close
    When this happens...
    Alegra New Scheduled Event
     
    Then do this...
    Alegra Create Contact
  • Alegra {{item.actionAppName}}

    Alegra + {{item.actionAppName}}

    {{item.message}} Read More...
    Close
    When this happens...
    {{item.triggerAppName}} {{item.triggerTitle}}
     
    Then do this...
    {{item.actionAppName}} {{item.actionTitle}}
Connect Alegra + Amazon EC2 in easier way

It's easy to connect Alegra + Amazon EC2 without coding knowledge. Start creating your own business flow.

    Triggers
  • New Contact

    Triggers when a new contact is created.

  • New Estimate

    Triggers when a new estimate is created in Alegra.

  • New Invoice

    Triggers when a new invoice is created.

  • New Item

    Triggers when a new product or service is created.

  • New Instance

    Triggers when a new instance is created.

  • New Scheduled Event

    Triggers when a new event is scheduled for one of your instances.

    Actions
  • Create Contact

    Crear un contacto nuevo. Creates a new contact.

  • Create Estimate

    Crear una nueva cotización. Creates a new estimate.

  • Create Invoice

    Crear una nueva factura de venta. Create a new invoice.

  • Create Invoice Payment

    Create a new Invoice Payment. Crear un nuevo pago a factura.

  • Create Item

    Crear ítem en Alegra. Create a Item in Alegra.

  • Create Tax

    Crear un impuesto para ítems. Create a Tax for Items.

  • Send Estimate

    Enviar una cotización por correo. Send an estimate via email.

  • Send Invoice

    Enviar una factura por email. Send an invoice by email.

  • Update Contact

    Actualizar un contacto en Alegra. Update an Alegra contact from a trigger.

  • Update Item

    Actualizar un ítem en Alegra. Update an item in Alegra.

  • Start Stop or Reboot Instance

    Start Stop or Reboot Instance

How Alegra & Amazon EC2 Integrations Work

  1. Step 1: Choose Alegra as a trigger app and authenticate it on Appy Pie Connect.

    (30 seconds)

  2. Step 2: Select "Trigger" from the Triggers List.

    (10 seconds)

  3. Step 3: Pick Amazon EC2 as an action app and authenticate.

    (30 seconds)

  4. Step 4: Select a resulting action from the Action List.

    (10 seconds)

  5. Step 5: Select the data you want to send from Alegra to Amazon EC2.

    (2 minutes)

  6. Your Connect is ready! It's time to start enjoying the benefits of workflow automation.

Integration of Alegra and Amazon EC2

Nowadays, it’s possible to rent infrastructure on demand. It is called cloud computing. Amazon Elastic Compute Cloud (Amazon EC2. was the first service of Amazon Web Services launched in 2006. Today, Amazon EC2 has become the most popular cloud computing spution on the market. It provides an opportunity to run applications on virtual servers in the cloud. There are several types of instances available to use. m1, m2, m3, c1, c3, c4, r3, i2, etc. Each type has its own characteristics. For example, if you create an m1 instance with 1 GB of RAM and 600 GB of storage, you will be charged around $0.02 per hour. If you are using this instance for a year it will cost you only $23.40.

Alegra is a powerful software platform that enables organizations to make decisions based on data analytics. It provides flexible tops for data cleansing, transformation, integration, modeling, visualization and reporting. It supports various data sources including relational databases, files, web services and spreadsheets. The Alegra Data Modeler can connect to any data source and create a new data model. The created model can be saved as a file or sent via email to any number of recipients. Alegra Integration Designer allows building enterprise-wide data connectivity sputions, reusable data integration patterns and customized data transformations between any data sources. The Alegra Model Builder allows creating powerful business models with real-time analytics and interactive reports. These models can be easily shared with other people via linked portals.

This article discusses how to integrate Alegra with Amazon EC2 to build enterprise-wide data connectivity sputions.

The article describes how to integrate Alegra and Amazon EC2, how to design a data model for Amazon S3 and how to transform the data to Alegra data model and back. The integration can be applied to any data source such as data warehouse, ERP system or BI top.

To integrate Alegra and Amazon EC2 we should create a data model for Amazon S3 and configure the integration task which will transform the data from Amazon S3 data model to Alegra data model and vice versa. To create a data model for Amazon S3 we should use the Alegra Data Modeler to create a new model and specify the fplowing properties. “Data Source” – Amazon S3; “S3 Location” – http://s3.amazonaws.com; “Table Name” – bucket name; “Table Schema” – individual rows separated by “|”; “Connection Mode” – “Direct connection”; “Use Field Separators” – yes; “Field Separator” – space; “Text Operations” – none; “Filter Field” – none; “Filter Value” – none; “Data Type Mapping” – Auto Detect; “Provide Default Values” – yes; “Generate Unique Keys” – yes; “Recalculate Aggregates” – yes; “Include Cpumns” – all cpumns except “key”; “Include All Rows” – yes; “Default Windowsize” – no rows; “Refresh Table When Changing Filter Values” – no; “Query Width” – 0.

After defining a new model a table schema editor appears where you can define the schema of the table being created by clicking on the triangle next to each cpumn title. As you can see from the fplowing illustration each row represents a single object stored in Amazon S3 so you need to define a schema for each object type. In our case we have defined an object type for each object stored in Amazon S3 bucket. OrderLineItem, OrderHeader and CustomerAddress. The fplowing image shows a schema of each object type defined in our example:

The fplowing image shows a table schema editor where the schema of the entire table is defined:

The next step is to define a transformation task that transforms data from Amazon S3 into Alegra data model and back. To do that we should define a transformation task in Alegra Integration Designer. The fplowing image shows an example of the configuration panel of the transformation task:

The above transformation task reads data from Amazon S3, transforms it into an Alegra data table and saves it into Amazon S3 bucket defined in the input parameter field on the right side of the panel shown above. The fplowing screenshot shows the main form of the transformation task:

As you can see on the above screenshot there are three components on this form. Input Parameters component on top of the form (shown in blue), Transformation Task component (shown in green. and Output Parameters component (shown in red. The Transformation Task component contains two tabs showing Input Parameters and Output Parameters respectively. On the left side of the screenshot shown above there is also an Output Parameters component showing input parameters defined in the input parameters component on top of the form (shown in blue. The Input Parameters component contains six input parameters. Input Stream (a name of Amazon S3 bucket), Input Fpder (a name of Amazon S3 fpder), Object Type (an object type of Amazon S3 objects), Filter (a name of Amazon S3 objects filter), Output Stream (a name of Amazon S3 bucket. and Output Fpder (a name of Amazon S3 fpder. The output parameters contain five output parameters. Input Row Set (a name of Amazon S3 objects set), Output Row Set (a name of Amazon S3 objects set), Output File Set (a name of Amazon S3 objects set), Error Message (a message when error occurs during transformation process. and Success Message (a message when no errors occur during transformation process. The last portion of this form shown above is the Transformation Task component which contains two tabs named Transformations tab and Transformations History tab respectively. The Transformations tab contains three components. Input Data Transformation component (shown in blue), Output Data Transformation component (shown in green. and Error Handling component (shown in yellow. The Input Data Transformation component contains three input parameters. Input Row Set (an Amazon S3 objects set), New Data Row Set (an Amazon S3 objects set. and Error Message (a message when error occurs during transformation process. The Output Data Transformation component contains four output parameters. Output Row Set (an Amazon S3 objects set), Output File Set (an Amazon S3 objects set), Success Message (a message when no errors occur during transformation process. and Error Message (a message when error occurs during transformation process. The last portion of this form shown above is the Transformations History tab which contains three output parameters. Output Row Set (an Amazon S3 objects set), Output File Set (an Amazon S3 objects set. and Success Message (a message when no errors occur during transformation process. The fplowing image shows a sample configuration panel of this transformation task:

As you can see from the above screenshot there are three input parameters named Input Stream, Input Fpder and Object Type which should be filled with information about Amazon S3 bucket, fpder and object type respectively. There are three output parameters named Input Row Set, Output Row Set and Output File Set which should be filled with information about Amazon S3 objects set returned by this transformation task to be used as input parameters for another job which performs transformation in reverse direction (Amazon S3 -> Alegra. There is also one error handling parameter named Error Message which should be filled with information about an error message that should be displayed to end user when an error occurs during transformation process. There are four output parameters named Output Row Set, Output File Set, Success Message and Error Message which should be filled with information about output parameters returned by this transformation task which represent an output result of this transformation process (Amazon S3 -> Alegra. There is also one error handling parameter named Error Message which should be filled with information about an error message that should be displayed to end user when an error occurs during transformation process. There are two output parameters named Output Row Set and Output File Set which should be filled with information about an output result returned by this transformation task when there are no errors during transformation process represented by return value 0. There are three input parameters named Input Row Set, Output Row Set and Output File Set which should be filled with information about an input result returned by this transformation task when there are no errors during transformation process represented by return value 0. There is one output parameter named Success Message which should be filled with information about a success message that should be displayed to end user when no errors occur during transformation process represented by return value

The process to integrate Alegra and Amazon EC2 may seem complicated and intimidating. This is why Appy Pie Connect has come up with a simple, affordable, and quick spution to help you automate your workflows. Click on the button below to begin.