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Amazon DynamoDB + Expensify Integrations

Appy Pie Connect allows you to automate multiple workflows between Amazon DynamoDB and Expensify

  • No code
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  • Lightning Fast Setup
About Amazon DynamoDB

DynamoDB is a fully managed NoSQL database service from Amazon that delivers rapid performance at any scale. It breaks down your data storage and management problems into tractable pieces so that you can focus on building great apps instead of managing complex infrastructure.

About Expensify

Expensify is a simple expense tracking app that automatically captures your receipts and organizes them into usable data – so you can stop wasting time doing paperwork and focus on what really matters.

Expensify Integrations

Best ways to Integrate Amazon DynamoDB + Expensify

  • Amazon DynamoDB Expensify

    Amazon DynamoDB + Expensify

    Export Report to PDF in Expensify when New Table is created in Amazon DynamoDB Read More...
    Close
    When this happens...
    Amazon DynamoDB New Table
     
    Then do this...
    Expensify Export Report to PDF
  • Amazon DynamoDB Expensify

    Amazon DynamoDB + Expensify

    Create Expense Report to Expensify from New Table in Amazon DynamoDB Read More...
    Close
    When this happens...
    Amazon DynamoDB New Table
     
    Then do this...
    Expensify Create Expense Report
  • Amazon DynamoDB Expensify

    Amazon DynamoDB + Expensify

    Create Single Expense to Expensify from New Table in Amazon DynamoDB Read More...
    Close
    When this happens...
    Amazon DynamoDB New Table
     
    Then do this...
    Expensify Create Single Expense
  • Amazon DynamoDB Expensify

    Amazon DynamoDB + Expensify

    Export Report to PDF in Expensify when New Item is created in Amazon DynamoDB Read More...
    Close
    When this happens...
    Amazon DynamoDB New Item
     
    Then do this...
    Expensify Export Report to PDF
  • Amazon DynamoDB Expensify

    Amazon DynamoDB + Expensify

    Create Expense Report to Expensify from New Item in Amazon DynamoDB Read More...
    Close
    When this happens...
    Amazon DynamoDB New Item
     
    Then do this...
    Expensify Create Expense Report
  • Amazon DynamoDB {{item.actionAppName}}

    Amazon DynamoDB + {{item.actionAppName}}

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

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

    Triggers
  • New Item

    Trigger when new item created in table.

  • New Table

    Trigger when new table created.

    Actions
  • Create Item

    Creates new item in table.

  • Create Expense Report

    Only for Expensify Premium users! Creates a new expense report.

  • Create Single Expense

    Creates a single expense item

  • Export Report to PDF

    Given a Report ID (from a trigger), export that report to a PDF document

How Amazon DynamoDB & Expensify Integrations Work

  1. Step 1: Choose Amazon DynamoDB 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 Expensify 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 Amazon DynamoDB to Expensify.

    (2 minutes)

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

Integration of Amazon DynamoDB and Expensify

In this paper, I am going to discuss the advantages of the integration between Amazon DynamoDB and Expensify.

Amazon DynamoDB is a NoSQL database that supports key-value data model. It is used for storing and managing data for applications. It can handle massive volumes of data with low latency and high throughput. It works on tablets, phones, browsers, and servers.Digitek Inc. is a software company based in San Jose, California that makes Expensify, an expense tracking app available on Android, iOS, Windows Phone, Apple Watch, Web, and Chrome App. Expensify allows users to track their expenses and send them along with receipts to accounts payable departments. Expensify has an API which allows users to access to Expensify from external sources applications. Thus integrating Amazon DynamoDB with Expensify enables users to use the power of both platforms (Amazon DynamoDB and Expensify. on a single web application.Expensify provides a developer API that helps developers track expenses programmatically. The developer API integrates with a number of other services including Amazon DynamoDB. Developers can use this API to build custom integrations with Amazon DynamoDB.

Amazon MapReduce enables users to process and analyse large amounts of data using Hadoop and Amazon S3. It uses map and reduce operations to perform both batch processing of large amounts of data as well as stream processing of huge amounts of data. It supports MapReduce programming model and parallel execution of MapReduce tasks on multiple computers. Users can write MapReduce programs in Java, C++ or Amazon’s provided Hadoop libraries such as Streaming, MRUnit, Pig, Hive, etc. Amazon's S3 web service stores data at a massive scale; it is designed to deliver 99.999999999% durability and 99.99% availability of objects over a given year. It is designed to deliver object updates in "near real-time".It also offers users an option to store data using their own custom schema allowing them to add new fields as they go along. User can retrieve data from buckets using either HTTP GET/PUT requests or by executing custom queries against S3 using AWS SDKs or command line tools.This paper is a case study about integrating Amazon DynamoDB with Expensify using Amazon MapReduce to create an application that automatically uploads expenses from Expensify into Amazon DynamoDB table. First, I will explain how to integrate Amazon DynamoDB with Expensify using the Expensify API. Then I will explain how to create an application that runs MapReduce jobs on Amazon EMR and creates tables in Amazon DynamoDB automatically when an expense report is created in Expensify.Amazon DynamoDB is a web service which provides scalable, highly-available NoSQL cloud database platform for applications running on AWS. It supports both document and key-value data models and allows users to access their database using HTTP and Query APIs. DynamoDB provides users with choice of provisioned throughput capacity during the initial creation of their tables or they can specify read and write capacity units for each table after table is created (You can read more about provisioned throughput capacity and read/write units here . You can choose between 3 types of storage – Provisioned Capacity, Throughput Capacity or No Capacity at all.With DynamoDB you pay only for what you use. You don't have to provision storage in advance or guess how much storage will be required later on. You only pay for what you store and the amount of throughput requested per second (unit = Read + Write Capacity Units. If your application does not require much capacity then you can set enough capacity for smaller reads/writes but if your application requires huge amount of capacity then you can pay for the same without worrying about provisioning capacity in advance or paying extra for unused capacity.When you are creating your tables you can specify two things. provisioned throughput capacity (consumed in units. and read/write units (consumed in units. Provisioned throughput capacity controls the number of read/write requests per second that can be completed by a table; read/write units control the number of individual requests that can be made against the table per second. That means even if you have enough provisioned throughput capacity but your application is generating too many read/write requests per second then your application will fail because you may exceed the maximum allowed number of requests per second. On the other hand if you specify enough read/write units but do not have enough provisioned throughput capacity then your application will be throttled by Amazon DynamoDB and your application will not be able to perform read/write operations at desired speed because your application will be waiting for data from Amazon DynamoDB in order to complete request. So make sure that you specify enough provisioned throughput capacity with enough read/write units (assuming your application has predictable read/write request patterns.The best way to understand how provisioned throughput capacity works with read/write units is through example. Let's assume that you want to create a table with provisioned throughput capacity of 10 read/write requests per second (RPS. and 1 RPS per second per partition for up to 5 partitions of the table (each partition is sharded by a specified key range. Let's assume that you also want to allow up to 1000 RPS total across all partitions for up to 1 million items in total in the table (1 million items might sound too much but we are just trying to show how scaled out our application becomes when there are many partitions. Then the following table shows how many read/write units would be consumed by different scenarios:Scenario RPS per sec Total RPS Total CUs Total RWU Reads Writes Reads Writes A 0 0 0 0 0 0 B 0 0 0 0 1 1 C 1 1 1 2 2 4 D 2 2 1 3 4 8 E 3 3 1 4 7 14 F 4 4 1 5 9 18 G 5 5 1 6 12 24 H 10 10 1 11 22 44 Total Avg RWU per sec = 4 Total Avg RWU per sec = 21 Total Avg RWU per sec = 91 Total Avg RWU per sec = 42 avg RWU per item = 0.2 avg RWU per item = 0.8 avg RWU per item = 2Let's say that your application requires 200 rps (reads + writes. against DynamoDB; if you choose not to specify any provisioned throughput capacity then your application will go through throttling while doing around 60 rps because it has no provisioned throughput capacity against DynamoDB (200 rps vs 10 rps); if you choose to specify provisioned throughput capacity of 200 rps then your application will not get throttled at 60 rps because it has 200 rps against DynamoDB; if you choose to specify provisioned throughput capacity of 400 rps then your application will get throttled at 120 rps because it has 200 rps against DynamoDB (400 rps vs 200 rps); if you choose to specify provisioned throughput capacity of 600 rps then your application will get throttled at 180 rps because it has 400 rps against DynamoDB (600 rps vs 200 rps); if you choose to specify provisioned throughput capacity of 800 rps then your application will not get throttled at all because it has 800 rps against DynamoDB (800 rps vs 200 rps. Once again let's assume that your application requires 200 rps (reads + writes. against DynamoDB; if you choose not to specify any read/write units then your application will go through throttling while doing around 60 rps because it has no read/write units against DynamoDB (200 rps vs 1 rps); if you choose to specify read/write units of 200 then your application will not get throttled at 60 rps (200 rps vs 1 rps); if you choose to specify read/write units of 400 then your application will get throttled at 120 rps (400 rps vs 1 rps); if you choose to specify read/write units of 600 then your application will get throttled at 180 rps (600 rps vs 1 rps); if you choose to specify read/write units of 800 then your application will not get throttled at all (800 rps vs 1 rps.Here are few things that confuse people when they start working with AWS DynamoDB:·          Too many things are exposed via the console making it difficult to find things sometimes·          Regions are exposed through the console although regions are just another property that we can set on our DB instances·          You can't delete items from a table; instead you can delete an entire table and re-create it once you create a new table with desired settings

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