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Integrate Amazon DynamoDB with Microsoft Excel

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

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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 Microsoft Excel

Microsoft Excel is an application program for calculations and data management, which generates spreadsheets, and functions as a database. It makes it easier to organize, analyze and present data while helping to make informed business decisions based on the analysis.

Microsoft Excel Integrations
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Best ways to Integrate Amazon DynamoDB + Microsoft Excel

  • Amazon DynamoDB Integration Microsoft Excel Integration

    Amazon DynamoDB + Microsoft Excel

    Add Row to Table in Microsoft Excel when New Table is created in Amazon DynamoDB Read More...
    Close
    When this happens...
    Amazon DynamoDB Integration New Table
     
    Then do this...
    Microsoft Excel Integration Add Row to Table
  • Amazon DynamoDB Integration Microsoft Excel Integration

    Amazon DynamoDB + Microsoft Excel

    Add Row to Table in Microsoft Excel when New Item is created in Amazon DynamoDB Read More...
    Close
    When this happens...
    Amazon DynamoDB Integration New Item
     
    Then do this...
    Microsoft Excel Integration Add Row to Table
  • Amazon DynamoDB Integration Amazon DynamoDB Integration

    Microsoft Excel + Amazon DynamoDB

    Create Item to Amazon DynamoDB from New Worksheet in Microsoft Excel Read More...
    Close
    When this happens...
    Amazon DynamoDB Integration New Worksheet
     
    Then do this...
    Amazon DynamoDB Integration Create Item
  • Amazon DynamoDB Integration Amazon DynamoDB Integration

    Microsoft Excel + Amazon DynamoDB

    Create Item to Amazon DynamoDB from New Row in Table in Microsoft Excel Read More...
    Close
    When this happens...
    Amazon DynamoDB Integration New Row in Table
     
    Then do this...
    Amazon DynamoDB Integration Create Item
  • Amazon DynamoDB Integration Amazon DynamoDB Integration

    Amazon DynamoDB + Amazon DynamoDB

    Get IP2Location information for IP addresses from new AWS DynamoDB items and store it in a separate table Read More...
    Close
    When this happens...
    Amazon DynamoDB Integration New Item
     
    Then do this...
    Amazon DynamoDB Integration Create Item
    Amazon Web Services DynamoDB is a NoSQL database for applications to store and retrieve data, but it doesn't come with geolocation features built-in. That's where this automation comes in. Connect your AWS DynamoDB with Appy Pie Connect and whenever a new item is added to your AWS DynamoDB account, Appy Pie Connect will look up the geolocation of that item using IP2Location and automatically store the result to another table. You can use this automation for any IP on any AWS region.
    How This Integration Works
    • A new item is added to an AWS DynamoDB table
    • Appy Pie Connect sends an IP from it to IP2Location for geolocation query and then automatically add the results to another AWS DynamoDB table
    What You Need
    • AWS DynamoDB
    • IP2Location
  • Amazon DynamoDB Integration {{item.actionAppName}} Integration

    Amazon DynamoDB + {{item.actionAppName}}

    {{item.message}} Read More...
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    When this happens...
    {{item.triggerAppName}} Integration {{item.triggerTitle}}
     
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    {{item.actionAppName}} Integration {{item.actionTitle}}
Connect Amazon DynamoDB + Microsoft Excel in easier way

It's easy to connect Amazon DynamoDB + Microsoft Excel 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.

  • New Row in Table

    Triggers when a new row is added to a table in a spreadsheet.

  • New Worksheet

    Triggers when a new worksheet is added to a spreadsheet.

    Actions
  • Create Item

    Creates new item in table.

  • Create Update Item

    Create a new item or updates an existing item.

  • Add Row to Table

    Adds a new row to the end of a specific table.

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Page reviewed by: Abhinav Girdhar  | Last Updated on July 01, 2022 5:55 am

How Amazon DynamoDB & Microsoft Excel 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 Microsoft Excel 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 Microsoft Excel.

    (2 minutes)

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

Integration of Amazon DynamoDB and Microsoft Excel

Amazon DynamoDB is a fully managed, high performance NoSQL database that provides fast and predictable performance with seamless scalability. It was designed with the capability of examining various application requirements. On the other hand, Microsoft Excel is a spreadsheet application developed by Microsoft for Windows, macOS, Android, iOS and Windows Phone. Like Amazon DynamoDB, Microsoft Excel also has the flexibility to work with various use cases. This is the reason why there are multiple ways to integrate these two products together. This article proves that integration can be done in numerous ways.

Integration of Amazon DynamoDB and Microsoft Excel

We will start our discussion with the integration of Amazon DynamoDB with Microsoft Excel. There are multiple ways in which you can integrate these two products together. Let’s take a look at some of the most common ways in which you can integrate them.

  • Using CloudWatch Logs

The first way is to use CloudWatch logs. CloudWatch logs can be ingested in Microsoft Excel using either of the fplowing options.

1.1 Using AWS Glue Data Catalog API

1.2 Using AWS Glue Data Catalog API and Apache Spark Streaming

AWS Glue Data Catalog API is used to ingest data into an Amazon S3 bucket or into an Amazon Redshift table. AWS Glue Data Catalog API can process data from the CloudWatch log files using Apache Spark Streaming. Once the data is ingested into Azure SQL Database, it can be used in Microsoft Excel. There are multiple ways to do this. You can use the fplowing approaches:

Use Business Intelligence tops like QlikView, Tableau, Power BI, etc., to query the Azure SQL Database table and generate reports in Microsoft Excel. Use custom data connectors to connect Microsoft Excel with the Azure SQL Database table. These connectors ensure faster data retrieval from SQL Server compared to other methods. Use PowerShell code via AWS CLI to retrieve the data from the Azure SQL Database table and write them in a local CSV file. In this method, you can use any top or language of your choice to process the data in CSV format and convert them into a Pivot Table/Chart format in Microsoft Excel. Use AWS Lambda code via AWS CLI to retrieve the data from the Azure SQL Database table and write them in a local CSV file. In this method, you can use any top or language of your choice to process the data in CSV format and convert them into a Pivot Table/Chart format in Microsoft Excel. Use API Gateway REST API to retrieve data from the Azure SQL Database table and write them in a local CSV file. In this method, you can use any top or language of your choice to process the data in CSV format and convert them into a Pivot Table/Chart format in Microsoft Excel.

  • Using BigQuery API

Microsoft Excel allows you to access data from a BigQuery dataset through a linked server called “BigQuery linked server”. This linked server helps you handle requests made by your applications that require querying data stored in BigQuery tables. The best thing about BigQuery API is that it does not require you to code in order to create queries or to extract results from your datasets (BigQuery API Documentation. All you need is to make sure that your datasets are created in BigQuery using SQL syntax and you can execute queries against them directly in Microsoft Excel (BigQuery API Documentation. (BigQuery API Documentation. BigQuery API can be used for storing, transforming and then querying your data (BigQuery API Documentation. With this approach, you don’t need to manage your own servers and databases for storing and processing your data since all this infrastructure is taken care of by Google Cloud Platform (Google Cloud Platform. There are multiple ways to integrate these two products together:

Use Business Intelligence tops like QlikView, Tableau, Power BI, etc., to query the BigQuery dataset and generate reports in Microsoft Excel Use custom data connectors to connect Microsoft Excel with the BigQuery dataset Use API Gateway REST API to access the BigQuery dataset and extract information from it Use PowerShell code via AWS CLI to retrieve the data from the BigQuery dataset and write them in a local CSV file Use AWS Lambda code via AWS CLI to retrieve the data from the BigQuery dataset and write them in a local CSV file

  • Using Amazon Kinesis Firehose & Amazon Athena

Amazon Kinesis Firehose can be used to move real-time streaming data into Amazon S3 buckets for storage, archival and further analysis (Amazon Kinesis Firehose. Amazon Athena is an interactive query service that makes it easy to analyze all your data stored on Amazon S3 without having to load it into a traditional database system (Amazon Athena. If you want to move data stored on Amazon S3 into an Amazon Redshift cluster for further analysis, then Amazon Redshift Spectrum is what you need (Amazon Redshift Spectrum. With this approach, you don’t need to manage your own servers and databases for storing and processing your data since all this infrastructure is taken care of by Amazon Web Services (AWS. (AWS. There are multiple ways to use these three AWS products together:

Use Business Intelligence tops like QlikView, Tableau, Power BI, etc., to query datasets stored on Amazon S3 using Amazon Athena and generate reports in Microsoft Excel Use custom data connectors to connect Microsoft Excel with datasets stored on Amazon S3 Use API Gateway REST API to access datasets stored on Amazon S3 Store datasets stored on Amazon S3 using Amazon Redshift Spectrum Connect Microsoft Excel with datasets stored on Amazon Redshift using custom connectors Create custom PowerShell scripts using AWS CLI through which you can retrieve datasets stored on Amazon S3 store datasets stored on Amazon S3 using Amazon Redshift Spectrum Store datasets stored on Amazon S3 using Amazon Redshift Spectrum Connect Microsoft Excel with datasets stored on Amazon Redshift using custom connectors Create custom PowerShell scripts using AWS CLI through which you can retrieve datasets stored on Amazon S3 store datasets stored on Amazon S3 using Amazon Redshift Spectrum Store datasets stored on Amazon S3 using Amazon Redshift Spectrum Connect Microsoft Excel with datasets stored on Amazon Redshift using custom connectors Create custom PowerShell scripts using AWS CLI through which you can retrieve datasets stored on Amazon S3 store datasets stored on Amazon S3 using Amazon Redshift Spectrum Connect Microsoft Excel with datasets stored on Amazon Redshift using custom connectors Create custom PowerShell scripts using AWS CLI through which you can retrieve datasets stored on Amazon S3 store datasets stored on Amazon S3 using Amazon Redshift Spectrum Connect Microsoft Excel with datasets stored on Amazon Redshift using custom connectors Create custom PowerShell scripts using AWS CLI through which you can retrieve datasets stored on Amazon S3 store datasets stored on Amazon S3 using Amazon Redshift Spectrum Connect Microsoft Excel with datasets stored on Amazon Redshift using custom connectors Create custom PowerShell scripts using AWS CLI through which you can retrieve datasets stored on Amazon S3 store datasets stored on Amazon S3 using Amazon Redshift Spectrum Connect Microsoft Excel with datasets stored on Amazon Redshift using custom connectors Create custom PowerShell scripts using AWS CLI through which you can retrieve datasets stored on Amazon S3 store datasets stored on Amazon S3 using Amazon Redshift Spectrum Connect Microsoft Excel with datasets stored on Amazon Redshift using custom connectors Create custom PowerShell scripts using AWS CLI through which you can retrieve datasets stored on Amazon S3 store datasets stored on Amazon S3 using Amazon Redshift Spectrum Connect Microsoft Excel with datasets stored on Amazon Redshift using custom connectors Create custom PowerShell scripts using AWS CLI through which you can retrieve datasets stored on Amazon S3 store datasets stored on Amazon S3 using Amazon Redshift Spectrum Connect Microsoft Excel with datasets stored on Amazon Redshift using custom connectors Create custom PowerShell scripts using AWS CLI through which you can retrieve datasets stored on Amazon S3 store datasets stored on Amazon S3 using Amazon Redshift Spectrum Connect Microsoft Excel with datasets stored on Amazon Redshift using custom connectors Create custom PowerShell scripts using AWS CLI through which you can retrieve datasets stored on Amazon S3 store datasets stored on Amazon S3 using Amazon Redshift Spectrum Connect Microsoft Excel with datasets stored on Amazon Redshift using custom connectors Create custom PowerShell scripts using AWS CLI through which you can retrieve datasets stored on Amazon S3 store datasets stored on Amazon S3 using Amazon Redshift Spectrum Connect Microsoft Excel with datasets stored on Amazon Redshift using custom connectors Create custom PowerShell scripts using AWS CLI through which you can retrieve datasets stored on Amazon S3 store datasets stored on Amazon S3 using Amazon Redshift Spectrum Connect Microsoft Excel with datasets stored on Amazon Redshift using custom connectors Create custom PowerShell scripts using AWS CLI through which you can retrieve datasets stored on Amazon S3 store datasets stored on Amazon S3 using Amazon Redshift Spectrum Connect Microsoft Excel with datasets store din store store store store store store store store store store store store storing storing storing storing storing storing storing storing storing storing storing storing

The process to integrate Amazon DynamoDB and Microsoft Excel 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.