In our previous article, we saw how to get Office 365 data in Power BI. Now, let’s look at how to get Office 365 Mail Attachments using SSIS without any coding. Microsoft Graph API is a unified way to access many Microsoft services, including the Office 365 API. In this post, we will use the SSIS JSON/REST API Source Connector to retrieve the Outlook Messages list and its attachments.

Use Case of Microsoft Graph API

Here are some use cases for why you want to use the Microsoft Graph API

• Read/Write events from Outlook Calendar
• Send email  / Read emails
• Get the list of files from OneDrive
• Upload/ Download files to OneDrive
• Read/Write Excel Sheet (Use range or a specific range of a specific sheet)
• Read the SharePoint Document library
• Search content from the SharePoint document library
• Get AD users for your Organization (AD Accounts)
• Update OneNote
• Track changes to Users, Events, Calendar Items using the delta API

Step-By-Step to call the Microsoft Graph Rest API Using SSIS

Now, let’s look at examples to access your Outlook.com emails using SSIS. You can use the same techniques to call pretty much any Office 365 API.

Configure SSIS OAuth Connection for Graph REST API

The first step to access any Graph API is to configure an OAuth connection

1. Open Visual Studio
2. Open an existing SSIS Project or create a new one using File > New > Project > Choose “Integration Services Project” Type under Business Intelligence template category.
3. Create Package
4. Right-click inside the Connection Managers area and click New Connection…
5. From the connection type list, select the ZS-OAUTH connection type.
   

   Create a new SSIS OAuth API Connection Manager

6. On the OAuth Connection Manager, configure the following options
   1. Select “Custom” from the Provider dropdown
   2. Select OAuth2 from the OAuth version
   3. Enter your Application Id and Secret Key (i.e., App Password) obtained in the previous section from here.
   4. Enter the following URL in the Authorization URL
      

      https://login.microsoftonline.com/common/oauth2/v2.0/authorize

   5. Use the following URL in the token URL field
      

      https://login.microsoftonline.com/common/oauth2/v2.0/token

   6. Enter the following Graph API Scopes (each scope must be entered on a new line).
      

      user.read
      mail.read
      offline_access

      NOTE: Always include the offline_access scope, which returns a refresh_token, which allows you to renew the token without going through the login process again. For more information about which scopes are needed, always refer to the API help page (e.g., if you want to read mail using this API, then you will see it says you can use Mail.Read or Mail.ReadWrite scope).
   7. Go to the Advanced tab and enter the following URL in the Callback/Return URL (assuming the same URL used when you registered the App in the previous section)
      

      https://zappysys.com/oauth

   8. Now go back to the first tab and click Generate Token. When prompted, log in using your Personal Microsoft Account or Work Account (Office 365 or AD login)
   9. After logging in, you will see the Accept option. Just click it. If things go right, then you will see the Access Token and Refresh Token fields populated.
7. Once everything is configured, you can click Test Connection to verify the connection works.
   

   SSIS OAuth Connection Manager for Office 365 REST API Microsoft Graph API

Download the Messages Attachment Files from Outlook

Let’s start with an example. We use the SSIS JSON Source and Web API Destination components to make a call to the Microsoft Graph REST API.

1. First of all, drag and drop the Data Flow Task from the SSIS Toolbox and double-click it to edit.
   

   Drag and drop Data flow

2. From the SSIS toolbox, drag and drop the JSON Source onto the Data Flow Designer surface.
   

   SSIS JSON Source – Drag and Drop

3. Double-click the JSON Source, then enter the following URL to invoke the Messages API call with search options to retrieve emails with attachments.
   

   https://graph.microsoft.com/v1.0/me/messages?$select=sentDateTime,from,subject,hasAttachments&$search="hasAttachments:True"

   Set the filter to $.value[*] and click the preview.
   

   SSIS JSON Source – Get data from Microsoft Graph API (Office 365 API – Read Mail Example)

   To customize the URL with additional parameters, check this help link. It’s a standard OData Protocol, so you can leverage many common OData features (e.g., use $top parameters to set pageSize. Default is 10 for List Mail, but you can include more records per response by changing it, such as …/messages?$top=50).

4.  Now, let’s use Template Transform to set the URL using the message ID to make the Get attachment API call.

   https://graph.microsoft.com/v1.0/me/messages/<%id%>/attachments

   

   SSIS Template Transform sending attachment id
5. Now we need to use the Web API Destination to make the Get Attachment API call by message ID. Select the Input column for URL as that TemplateOutput.
   

   SSIS Web API Destination – Get data from Microsoft Graph API (Office 365 API – Get Attachment Example)

6. Now, the next step is to parse the JSON Response String of the get attachment api call. For that, we need to use a JSON Parser. Select the input as Web PAI Destination ResponseText and use the below sample JSON string to configure the metadata(Columns).
   

   {"@odata.context":"https://graph.microsoft.com/v1.0/$metadata#users('support%40zappysys.com')/messages('AQMkADAwATMwMAItMDkyMC0zZTg4AC0wMAItMDAKAEYAAAP_JUy')/attachments"
   ,"value":[{"@odata.type":"#microsoft.graph.fileAttachment","@odata.mediaContentType":"text/plain"
   ,"id":"AQMkADAwATMwMAItMDkyMC0zZTg4AC0wMAItMDAKAEYAAAP_JUy_27M"
   ,"lastModifiedDateTime":"2020-02-21T07:36:16Z"
   ,"name":"Test.txt","contentType":"text/plain","size":870,"isInline":false
   ,"contentId":null,"contentLocation":null,"contentBytes":"VGVzdCBNZXNzYWdl"}]}

   

   JSON Parser Transform – using JSON example
7.  Now, in the JSON Parser, go to the Go-to columns Tab and set the contentBytes DataType to DT_IMAGE, and increase the other columns’ DT_WSTR lengths by 1000 and 1500. We set contentBytes to the DT_IMAGE  datatype, which is like varbinary(MAX).
   

   JSON Parser Transform Columns Tab fix the datatype

8. Furthermore, in the next step, we need to use an SSIS Derived Column to set the FilePath where we want to save the attachments, and then concatenate the local file path with the attachment name column.
   

   SSIS Derived Column: Add New Column

9. Finally, we need to use the Export Column Transform (Native) to export attachment bytes to a local file. It needs to know 2 upstream info Bytes and the target file path.
   

   Export Column configuration

10. Make sure to attach the export column to some destination (e.g., our ZS Trash Destination); the engine might remove it. At the same time, it optimizes the runtime. It may remove all transformations that don’t have a destination, such as deploying the package to SQL Server, or when you set optimize in Visual Studio.
11. That’s it, execute the package, and it will download all the email attachments.
    

    Downloaded Mail Attachments

Conclusion

After all, in this article, we learned how to make a Microsoft Graph API REST API call. We used the SSIS JSON / REST API Connector to extract data from Outlook Mail using OAuth. Made the Get attachment API call using the Web API Destination and parsed the JSON response using the JSON Parser. And using the Native SSIS Export Column, we saved the export attachment’s bytes to a local file. To explore many other scenarios not discussed in this article, download SSIS PowerPack from here (includes 70+ Components).

References

Finally, you can use the following links for more information:

• • Help File: JSON Source(REST API or File)
  • Help File: Web API Destination
  • Help File: JSON Parser Transform
  • Overview of the Microsoft Graph API

The post How to Get Office 365 Mail Attachments using SSIS appeared first on ZappySys Blog.

In our previous blog we saw How to write data into CSV file in SSIS (GZip / Split). Now in this blog, we will see How to Pivot CSV Data in SSIS using CSV Source. It also supports Pivot mode so you can convert single CSV string value into Rows. In this article we also see how make some CSV columns as parent and not consider it in pivot and load it as it is using JSON Generator and JSON Parser Transform Pivot Option.

In nutshell, this post will focus on how to Pivot CSV Data into multiple columns and rows.

How to Pivot CSV Data in SSIS using CSV File Source

Let´s start with an example. In this SSIS CSV Parser Transform example, we will Parse database column string into multiple columns and rows.

1. First of All, Drag and drop Data Flow Task from SSIS Toolbox and double click it to edit.
   

   Drag and Drop : SSIS Data Flow Task from SSIS Toolbox

2. Furthermore, drag and drop the CSV Source and Double click on it to configure it.
   

   Drag and Drop : CSV Source from SSIS Toolbox

3. Enter the CSV file path and select desire column delimiter most common in CSV is comma and check the First Row as headers and click on preview.
   

   CSV Source Preview

4. Now, go to Pivot Columns to Rows Tab and checked Enable Pivoting and click on Preview Data to view the data.
   

   CSV Source Pivoted Data Preview

5. That’s it we see how to read data from CSV File and also Pivot the CSV Data using CSV Source.
6. Now In the next section we will see how to how make some CSV columns as parent and not consider it in pivot and load it as it is.

How make some CSV columns as parent.

Sometimes you just need to make child columns in to rows and want to leave parent level columns as it is.

1. First of All, Drag and drop Data Flow Task from SSIS Toolbox and double click it to edit.
   

   Drag and Drop : SSIS Data Flow Task from SSIS Toolbox

2. Secondly, drag and drop the CSV Source and Double click on it to configure it.
   

   Drag and Drop : CSV Source from SSIS Toolbox

3. Furthermore, enter the file path and click on preview.
   

   CSV Source Preview

4. For example if we want to pivot Email and Phone only in pivoting and wants to exclude parent level columns FirstName and LastName as it is.
5. For that drag and drop JSON Generator Transform and Select Output Mode as Output One JSON Per Parent Input Row  and Add Unbound Nested Element and named it as Persons and click on OK.
   

   JSON Generator Add Unbound Nested Element

6. Now add that two parent level fields FirstName and LastName in the Persons.
   

   JSON Generator Add Multiple Elements

7. Now select Person Element and right click and Add Unbound Nested Element and named it as Contacts and click on OK.
   

   JSON Generator Add Unbound Nested Element

8. Now add that two child level fields Email and Phone in the Contacts.
   

   JSON Generator Add Multiple Elements

9. Therefore JSON Generator Transform looks like below screen. Now Copy that Sample Preview JSON String to use it in the JSON Parser Transom as sample string.
   

   JSON Generator Transform – Sample Data

10. Similarly, drag and drop JSON Parser Transform and select the Input JSON Column and Paste that copied Sample JSON String and select the Contacts filter to read the Child data and click on Include Parent.
    

    JSON Parser Transform – Select Filter

11. Now go to Pivot Columns to Rows Tab and checked the Enable Pivoting and click on the Preview.
    

    JSON Parser Transform – Enable Pivoting

12. Finally, we are ready to load this data.
    

    CSV Pivoted Data

13. Let’s load this data in the SQL Server.

Load Pivoted CSV File data into SQL Server

ZappySys SSIS PowerPack makes it easy to load data from various sources such as REST, SOAP, JSON, XML, CSV or from other source into SQL Server, or PostgreSQL, or Amazon Redshift, or other targets. The Upsert Destination component allows you to automatically insert new records and update existing ones based on key columns. Below are the detailed steps to configure it.

Step 1: Add Upsert Destination to Data Flow

1. Drag and drop the Upsert Destination component from the SSIS Toolbox.
2. Connect your source component (e.g., JSON / REST / Other Source) to the Upsert Destination.

SSIS - Data Flow - Drang and Drop Upsert Destination Component

Step 2: Configure Target Connection

1. Double-click the Upsert Destination component to open the configuration window.
2. Under Connection, select an existing target connection or click NEW to create a new connection.
   • Example: SQL Server, or PostgreSQL, or Amazon Redshift.

Step 3: Select or Create Target Table

1. In the Target Table dropdown, select the table where you want to load data.
2. Optionally, click NEW to create a new table based on the source columns.

Configure SSIS Upsert Destination Connection - Loading data (REST / SOAP / JSON / XML /CSV) into SQL Server or other target using SSIS

Step 4: Map Columns

1. Go to the Mappings tab.
2. Click Auto Map to map source columns to target columns by name.
3. Ensure you check the Primary key column(s) that will determine whether a record is inserted or updated.
4. You can manually adjust the mappings if necessary.

SSIS Upsert Destination - Columns Mappings

Step 5: Save Settings

• Click OK to save the Upsert Destination configuration.

Step 6: Optional: Add Logging or Analysis

• You may add extra destination components to log the number of inserted vs. updated records for monitoring or auditing purposes.

Step 7: Execute the Package

• Run your SSIS package and verify that the data is correctly inserted and updated in the target table.

SSIS Upsert Destination Execution

Conclusion

After all, we saw you how to read CSV file data, pivot the data and Similarly, using SSIS JSON Generator and Parser Transform Pivot the CSV Data excluding some columns from pivoting.  To explore many other scenarios not discussed in this article download SSIS PowerPack from here (includes 70+ Components).

References

Finally, you can use the following URL for more information.

• Help File: CSV Source

The post How to Pivot CSV Data in SSIS appeared first on ZappySys Blog.

Connect to YouTube API in SSIS (OAuth Connection)

Very first step to call any Google API is to create an API project for Google Service and then enable APIs you like to call in Console. In our case we need to enable YouTube API.

Check this article to learn how to create API Project and obtain Client ID and Client Secret to connect to YouTube API. For ease of use use Default Application provided by ZappySys but we still recommend using your own Custom Application  on OAuth Connection.

Before we try our first YouTube API example, we recommend you to get familiar with YouTube API here. Once you done with above steps you can perform the following steps to call YouTube API in SSIS.

1. Open existing SSIS Project or Create new One in Visual Studio (i.e. SSDT)
2. Create a new SSIS Package. Right click in the Connection Manager Panel and click “New Connection…”.
   
3. When Dialog box opens select ZS-OAUTH connection type.
   

   SSIS Connection Managers List: Select O-AUTH Connection Manager

4. Once OAuth UI opens, Configure Google connection like below.
   1. Select Service Type as Google
   2. You can select Default App for ease of use or Select Custom Application (Enter Client Id and Secret Obtained using these steps )
   3. In the Scope Enter below – One scope per line for calling correct YouTube API. Refer to YouTube API documentation incase you need extra permissions. For read only operation we just need below scopes. Last scope is only needed if you need to use YouTube Analytics Reports
      

      https://www.googleapis.com/auth/youtube
      https://www.googleapis.com/auth/youtube.readonly
      https://www.googleapis.com/auth/yt-analytics.readonly

5. Click Generate Token to obtain Refresh Token (You may see Login Prompt, and then Grant Permission Confirmation Screen)
   

   Connect to Google API using OAuth 2.0 (YouTube API Example)

6. Click OK to save UI.

Call YouTube REST API Example

Now lets look at how to call YouTube API REST API Task this task is more suitable to call GET, POST, DELETE, PUT requests without parsing data in Rows and Columns. If you need to parse data and load into Database table then see next section (Use JSON Source)

1. From SSIS Toolbox look for items starting with “ZS”. Drag and Drop [ZS Rest API Task] to Designer Surface.
   
2. Double click it to configure like below.
   1. Select URL From Connection option
   2. Select OAuth connection created in previous section.
   3. Enter URL like below (For example get all playlists). Replace xxxxxxxxxxx with your own channel ID (Its usually found in URL when you visit Youtube Channel Homepage)
      

      https://www.googleapis.com/youtube/v3/playlists?part=snippet,id&channelId=xxxxxxxxxxxxx

   4. Click Test Request Button
3. Here is how it looks like
   

   Call Youtube API in SSIS Example (Dynamic URL)

That’s it we have successfully configured Connection for YouTube API in SSIS. In the next section we will see how to use this connection and read various data from YouTube API.

Read YouTube Playlists in SSIS

Once we done creating OAuth Connection Manager we can move forward to read YouTube data inside Data Flow.

Configure SSIS JSON / REST API Source

1. Now, Drag and Drop SSIS Data Flow Task from SSIS Toolbox.
   
2. Double click on the Data Flow task to see Data Flow designer surface.
3. From the SSIS toolbox drag and drop JSON Source on the dataflow designer surface.
   
4. Double click JSON Source and enter URL as below (Use variable or Hardcode Channel Id). maxResults controls how many rows you want to get in each response. Rep
   

   https://www.googleapis.com/youtube/v3/playlists?part=snippet,id&maxResults=50&channelId={{User::varChannelId}}
   --OR-- 
   https://www.googleapis.com/youtube/v3/playlists?part=snippet,id&maxResults=50&channelId=xxxxxxxxxxxxxxxxxxxxxx

5. Check Use Credentials and select existing OAuth connection or Create New one
6. Select Array Filter (Items node) or type

   $.items[*]

     as below.
   

   Get Playlists for YouTube Channel

7. That’s it you can now Click Preview to see sample data. In the next section we will configure Pagination to read all records if you have more than 100 rows.

Configure YouTube REST API Pagination

Most REST APIs dont return data in one big response. So you have to make sure you implement looping / pagination to read more records. Luckily ZappySys supports many different Pagination Settings. Here is how to configure YouTube REST API Pagination.

1. Go to Pagination Tab found on JSON Source and select following 3 settings
2. Select Pagination Mode as

   Response Attribute Mode

3. Enter Next Link as

   $.nextPageToken

4. Enter Suffix for Next URL as

   &pageToken=<%nextlink%>

YouTube REST API Pagination Settings

Read YouTube Playlist Videos in SSIS

So in previous section we saw simple API call to read all playlists from YouTube. Now lets see how to get information about Playlist Videos.

For that everything should remain same as previous section except change URL as below. Where xxxxxxxxxxxxxxxxxxxxx is your Playlist ID obtained from Previous Call.

https://www.googleapis.com/youtube/v3/playlistItems?part=snippet,id&maxResults=50&playlistId=xxxxxxxxxxxxxxxxxxxxx

Get all Videos for YouTube Channel

If you want to get all video name and ID for specific channel then use below URL. Click here to read more. Replace xxxxxxxxxxxxxxxxxxx with your Channel ID.

https://www.googleapis.com/youtube/v3/search?channelId=xxxxxxxxxxxxxxxxxxx&part=snippet,id&order=date&maxResults=50

Read YouTube Video Views, Likes, Dislikes (Extended Videos)

Another common request from YouTube API is get Video Information such as Views, Likes, Dislikes, Comment Count etc. For that you can use this api . However the issue is this endpoint requires you to pass Comma separated Ids of Video. You can only pass 25 Ids in one requests so if you have 200 videos for which you need to get information then you have to make 8 requests (25 Ids in each Request). In below screenshot you can see we used Fiddler to debug web requests.

So here is how high level process to fetch extended information for all Videos in your channel. Full SSIS Package is attached here (SSIS 2012 Format).

1. Get all videos for your channel by calling (Assuming you will paginate in below request.
   

   https://www.googleapis.com/youtube/v3/search?channelId=xxxxxxxxxxxxxxxxxxx&part=snippet,id&order=date&maxResults=50

2. Build Video Id groups for 25 Ids from above result and submit requests. For example if above request returns 200 Videos then your request to fetch extended information may look like below.
   

   https://www.googleapis.com/youtube/v3/videos?part=snippet,contentDetails,statistics&id=ID1,ID2,ID3........ID25
   https://www.googleapis.com/youtube/v3/videos?part=snippet,contentDetails,statistics&id=ID26,ID27,ID28........ID50
   ........
   ........
   https://www.googleapis.com/youtube/v3/videos?part=snippet,contentDetails,statistics&id=ID176,ID177,ID178........ID200

3. Parse response coming from each Request Above…. Each Response will give you details about 25 videos you requesting.

Here is the flow of our complete data flow to read extended information for all videos. You can read extended information one by one too but it wont be good idea if you have many videos.

We have used Following Components to achieve Bulk Mode

• JSON Source (REST API Source)
• CSV Parser Transform
• Web API Destination
• JSON Parser Transform

Get YouTube Video View Clicks, Likes, Dislikes (Bulk Mode for all videos)

Read data from YouTube Analytics / Reporting API (Metrics, Dimensions Report)

So far we have seen basic YouTube APIs to extract your Channel information. However there is another very powerful API endpoint to query custom reports from YouTube. Check this YouTube Analytics API endpoint. In this section we will learn how to extract useful information from YouTube using Analytics API.

Step-By-Step

Let’s look at an example to read total views, estimated watch time, average view duration and some other metrics by date for channel you have access.

1. Go to Data Flow, Drag ZS JSON Source from SSIS Toolbox
2. Double click to configure.
3. Check Use Credentials. Select OAuth connection we created in previous section.
4. In the URL field, Enter Below Sample URL. Change Parameters as per your need or leave it default.
   For example:
   ids=channel==MINE   (where MINE can be replaced by other channel id or keep it as MINE to get data for your own channel)
   metrics (can be changed from any valid list here)
   dimensions (can be changed to any valid list from here)
   startDate (Must be YYYY-MM-DD format)
   endDate (Must be YYYY-MM-DD format)
   

   https://youtubeanalytics.googleapis.com/v2/reports?ids=channel==MINE&metrics=views,estimatedMinutesWatched,averageViewDuration,averageViewPercentage,subscribersGained&dimensions=day&startDate=2019-03-01&endDate=2019-04-01

5. Change Filter to

   $.rows[*]

6. Go to 2D Array Transform tab.
   Select Method as

   Simple 2-dimensional array

   EnterColumn Filter as 

   $.columnHeaders[*].name

   

   YouTube Analytics API Advanced Transformation

7. That’s it now Preview your data. See below over all steps.
   

   Read data from YouTube Analytics API (Custom Reports using Dimensions, Metrics Query)

Build YouTube Analytics / Reporting API Query (API Playground)

With Analytics API You can extract many interesting data. You can use Test Tool found on this page to build some interesting queries. Scroll to Common Use Cases section on that link. On the right side you can select Credentials Type as OAuth and click Execute.

https://developers.google.com/youtube/analytics/reference/reports/query

YouTube Analytics / Reporting API Explorer (Testing API Calls)

Loading YouTube API Data into SQL Server

ZappySys SSIS PowerPack makes it easy to load data from various sources such as REST, SOAP, JSON, XML, CSV or from other source into SQL Server, or PostgreSQL, or Amazon Redshift, or other targets. The Upsert Destination component allows you to automatically insert new records and update existing ones based on key columns. Below are the detailed steps to configure it.

Step 1: Add Upsert Destination to Data Flow

1. Drag and drop the Upsert Destination component from the SSIS Toolbox.
2. Connect your source component (e.g., JSON / REST / Other Source) to the Upsert Destination.

SSIS - Data Flow - Drang and Drop Upsert Destination Component

Step 2: Configure Target Connection

1. Double-click the Upsert Destination component to open the configuration window.
2. Under Connection, select an existing target connection or click NEW to create a new connection.
   • Example: SQL Server, or PostgreSQL, or Amazon Redshift.

Step 3: Select or Create Target Table

1. In the Target Table dropdown, select the table where you want to load data.
2. Optionally, click NEW to create a new table based on the source columns.

Configure SSIS Upsert Destination Connection - Loading data (REST / SOAP / JSON / XML /CSV) into SQL Server or other target using SSIS

Step 4: Map Columns

1. Go to the Mappings tab.
2. Click Auto Map to map source columns to target columns by name.
3. Ensure you check the Primary key column(s) that will determine whether a record is inserted or updated.
4. You can manually adjust the mappings if necessary.

SSIS Upsert Destination - Columns Mappings

Step 5: Save Settings

• Click OK to save the Upsert Destination configuration.

Step 6: Optional: Add Logging or Analysis

• You may add extra destination components to log the number of inserted vs. updated records for monitoring or auditing purposes.

Step 7: Execute the Package

• Run your SSIS package and verify that the data is correctly inserted and updated in the target table.

SSIS Upsert Destination Execution

Download Sample SSIS Package

SSIS Package is attached here (SSIS 2012 Format).

Conclusion

In this article we saw how to extract information from REST API such as YouTube REST API using OAuth 2.0. We also learned techniques like Pagination, JSON Parsing, Request Batching using multiple components to achieve full scale API integration. To explore many other scenarios not discussed in this article download SSIS PowerPack from here (includes 70+ Components).

The post How to read YouTube API data in SSIS (Videos, Channels, Playlists) appeared first on ZappySys Blog.

In this tutorial, we will cover the topics of how to perform geocoding on the addresses and reverse geocoding on the location coordinates using SSIS and Google Maps API. So what is geocoding, exactly? Geocoding is the process of translating an address (e.g. a street address) or a place to coordinates on the Earth’s surface. Simply put, geocoding takes a description of a location and gives back latitude and longitude values as a result. For example, 

1600 Amphitheatre Parkway, Mountain View, CA 94043, USA

(37.4224764, -122.0842499)

(37.4224764, -122.0842499)

1600 Amphitheatre Parkway, Mountain View, CA 94043, USA

To accomplish our goal we will use an add-on for SSIS – ZappySys SSIS PowerPack. It contains many additional SSIS Tasks and Data Flow Components that lets you accomplish what you cannot do with standard SSIS connectors. We will use these ZappySys SSIS PowerPack connectors:

Web API Destination

JSON Parser Transform

Let’s start!

Step-by-Step – SSIS Geocoding with Google Maps API

Before we start, let’s consider the use-case for our tutorial. Suppose, you have a CRM system filled with user-entered addresses of your leads. Since they are user-entered, it means that they can be incomplete, malformed, or just plain rubbish. Also, you have a marketing campaign and you want to send letters with offers to your leads so that they become your clients. Before sending them any letters, you have to make sure the addresses are valid and well-formed. You are also interested in the geographical location of your leads that you could do some business intelligence analysis on the coordinates of your leads and maybe future clientele. Here is where Geocoding API kicks in.

Thus in this example, we will take various street addresses – sometimes invalid, incomplete or unformatted – and submit them to Google Geocoding API for processing. As a result, we will get nicely formatted, cleansed, full addresses and their coordinates. We will take a look on how to determine if lookup worked successfully and to what extent you should trust Geocoding API’s response. Finally, we will save retrieved addresses and their individual components (e.g. street number, street name, city, country, etc.) to a SQL Server database table.

Load addresses from a SQL Server database table

We prepared the Google Geocoding API part, now we are ready to get to the place where the rubber meets the road – we will create an SSIS package and load addresses from a database table:

1. Create a new SSIS package.
2. Drag and drop Data Flow from SSIS Toolbox onto the Control Flow.
   

   Dragging and dropping Data Flow Task into Control Flow

3. Drag and drop OLE DB Source onto the Data Flow.
4. Configure OLE DB Source to retrieve addresses from your table. We will use a table from Microsoft’s Northwind’s modified database “Marketing” (download it):

Loading user-entered addresses to be looked up in Google Geocoding API using SSIS.

Construct lookup URL

1. Drag and drop Derived Column component onto the Data Flow.
2. Add a new column and construct Geocoding lookup URL in this format (just like it says in Geocoding API documentation):

https://maps.googleapis.com/maps/api/geocode/json?address=ADDRESS_TO_LOOKUP&key=YOUR_API_KEY

Adding Google Geocoding API lookup URL as Derived Column to be used for geocoding in SSIS.

Configure the Geocoding API request

1. Drag and drop Web API Destination onto the Data Flow and connect it with Derived Column. We will use Web API Destination to make HTTP requests to Geocoding API.
2. Then double-click on Web API Destination and create a new HTTP connection:
   

   Setting up HTTP Connection Manager for Web API Destination to make HTTP requests to Google Geocoding API using SSIS.

3. Finally, configure Input Column for URL, HTTP Request Method and input into URL field a sample lookup URL:
   

   https://maps.googleapis.com/maps/api/geocode/json?address=One Microsoft Way Redmond 98052-6399 USA&key=YOUR_API_KEY

   

   Configuring Web API Destination to make geocoding lookups in Google Geocoding API using SSIS.

   Once you are done, click Test Request/Response button. This will actually make a geocoding lookup for Microsoft headquarters:

4. In the Response (Raw) tab you see the actual response from Geocoding API. Within it –  cleansed, formatted and full Microsoft headquarters address together with geographical coordinates (even we looked it up using just the part of it). Now copy-paste the response body somewhere, so that we can use it in the next step while parsing:
   

   Checking Google Geocoding API HTTP response in Web API Destination using SSIS.

Parsing Geocoding API JSON response into columns

1. Drag and drop JSON Parser Transform onto the Data Flow and connect it to Web API Destination.
2. Then open it and configure Input JSON Column.
3. After that, set the Filter to $.results[*].
4. Continue by setting Sample JSON string to the one you saved in the previous step. Actually, before doing that let’s modify it and add partial_match node next to place_id so that exact and partial matches could be told apart (otherwise this property won’t be parsed):
   

   {
       "results": [{
           "address_components": [{
               "long_name": "One",
               "short_name": "One",
               "types": ["street_number"]
           }, {
               "long_name": "Microsoft Way",
               "short_name": "Microsoft Way",
               "types": ["route"]
           }, {
               "long_name": "Overlake",
               "short_name": "Overlake",
               "types": ["neighborhood", "political"]
           }, {
               "long_name": "Redmond",
               "short_name": "Redmond",
               "types": ["locality", "political"]
           }, {
               "long_name": "King County",
               "short_name": "King County",
               "types": ["administrative_area_level_2", "political"]
           }, {
               "long_name": "Washington",
               "short_name": "WA",
               "types": ["administrative_area_level_1", "political"]
           }, {
               "long_name": "United States",
               "short_name": "US",
               "types": ["country", "political"]
           }, {
               "long_name": "98052",
               "short_name": "98052",
               "types": ["postal_code"]
           }],
           "formatted_address": "One Microsoft Way, Redmond, WA 98052, USA",
           "geometry": {
               "location": {
                   "lat": 47.6423318,
                   "lng": -122.1369302
               },
               "location_type": "ROOFTOP",
               "viewport": {
                   "northeast": {
                       "lat": 47.6436807802915,
                       "lng": -122.1355812197085
                   },
                   "southwest": {
                       "lat": 47.6409828197085,
                       "lng": -122.1382791802915
                   }
               }
           },
           "partial_match": false,
           "place_id": "ChIJGxUEuHFtkFQRnokD7mkppQE",
           "plus_code": {
               "compound_code": "JVR7+W6 Redmond, Washington, United States",
               "global_code": "84VVJVR7+W6"
           },
           "types": ["establishment", "point_of_interest"]
       }],
       "status": "OK"
   }

   

   Configuring JSON Parser Transform to parse looked up addresses in Google Geocoding API HTTP JSON response using SSIS.

5. To proceed and parse individual address components from JSON response (e.g. number, street, city, country, etc.), you will need to specify columns for each address component manually, just like portrayed in Parse multi-dimensional JSON array in SSIS or Drivers article. Just open 2D Array Transform tab and select Multiple columns using expressions as Transform Type and configure them:
   

   Configuring JSON Parser Transform to parse individual address components from Google Geocoding API HTTP JSON response using SSIS.

   We specify these columns in our example:

   Number = $.address_components[?(@.types[0]=='street_number')].long_name
   Street = $.address_components[?(@.types[0]=='route')].long_name
   City = $.address_components[?(@.types[0]=='locality')].long_name
   State = $.address_components[?(@.types[0]=='administrative_area_level_1')].long_name
   Country = $.address_components[?(@.types[0]=='country')].long_name
   PostalCode = $.address_components[?(@.types[0]=='postal_code')].long_name

6. Finally, be sure to set Include all upstream columns to downstream (quite important), so that CustomerID is passed downstream and which we can save into a destination table. Later it can be a useful column to join the source table and the destination table on. In that way, you will be able to map user-entered addresses with looked up, fully formatted addresses (won’t be demonstrated in this tutorial).

Save geocoding results into a SQL Server database table

1. We are now ready to add OLE DB Destination, execute the package, make the lookups, and save the results into the database:
   

   Creating a database table for the Google Geocoding API looked up addresses in SSIS.

2. Execute the package!
   

   SSIS geocoding package execution and its results.

The results of SSIS geocoding

Viewing the results of SSIS geocoding in SQL Server destination table.

Once you query the table of looked up results, you will notice several things:

1. Perhaps there are more rows in the results table than in the source table
2. partial_match column is either 1 or NULL
3. geometry.location_type column has several values, e.g. ROOFTOP, RANGE_INTERPOLATED, APPROXIMATE, etc.

So to what extent should you trust the results? The answer is to take a look at the documentation and see what these values mean.

Basically, when partial_match is equal to 1, according to Google:

• Partial matches most often occur for street addresses that do not exist within the locality you pass in the request.
• Partial matches may also be returned when a request matches two or more locations in the same locality. For example, “21 Henr St, Bristol, UK” will return a partial match for both Henry Street and Henrietta Street. Note that if a request includes a misspelled address component, the geocoding service may suggest an alternative address. Suggestions triggered in this way will also be marked as a partial match.

While geometry.location_type column tells how accurate the lookup was. Again, according to Google:

• “ROOFTOP” indicates that the returned result is a precise geocode for which we have location information accurate down to street address precision.
• “RANGE_INTERPOLATED” indicates that the returned result reflects an approximation (usually on a road) interpolated between two precise points (such as intersections). Interpolated results are generally returned when rooftop geocodes are unavailable for a street address.
• “GEOMETRIC_CENTER” indicates that the returned result is the geometric center of a result such as a polyline (for example, a street) or polygon (region).
• “APPROXIMATE” indicates that the returned result is approximate.

Simply put, “ROOFTOP” is the most accurate lookup, while “APPROXIMATE” is the least accurate.

Another valuable piece of information is how many individual address components are filled. The more NULLs are there, the less accurate is the address. So if you have NULL in the Number column and in the Street column, most likely this is not a very accurate lookup:

Individual address components of looked up addresses in Google Geocoding API using SSIS.

Concluding, in the figure above, you can see how user-entered address can be validated, cleansed and formatted.

SSIS reverse geocoding with Google Maps API

The concepts presented above about “forward” geocoding will help you to make reverse geocoding requests too. The only difference is the request URL:

https://maps.googleapis.com/maps/api/geocode/json?latlng=40.714224,-73.961452&key=YOUR_API_KEY

For reference and help visit official Google Geocoding API documentation.

Things have gone bad: Error handling & debugging

Incidentally, bad things can happen. A remote server may go offline or your server may go out of memory. In any case, you may want to know when that happens and take actions accordingly. For that purpose, you have to redirect bad rows to some other destination. For this example, we will take and use Web API Destination, but basically, you can use any SSIS component:

Handling errors

1. Add a Derived Column above Web API Destination with expression "(DT_WSTR,4000)ZS_JSON_OUT" and name it "JsonAsString". This will let you see what JSON you are actually passing.
2. Then add a database or file destination or use another Trash Destination for debugging purposes and redirect the bad rows (red arrow) from Web API Destination into it. Don't forget to set Redirect row option for both, Error and Truncation columns:
   

   Redirected failed requests from Web API Destination to a desired destination when loading from SQL Server to REST API Service is failing. Derived Column JsonAsString added to be able to read JSON which was passed to Elasticsearch

3. Finally, add a Data Viewer for the red path, if you want to debug the flow. You will be able to see URL, JSON and the error message for each record. You may want to copy-paste ErrorMessage to Notepad if you want it to be more readable:
   

   Use Data Viewer to view HTTP requests that failed to be fulfilled.

NOTE: You can read more about redirecting rows in SSIS Error Handling (Redirect bad rows) article.

Debugging HTTP requests

A common thing you have to do when working with HTTP requests is to debug those requests; e.g. to check what headers, body or URL was passed. To test how things look behind the scenes we strongly suggest to use Fiddler - a popular web debugging tool.

Inside it, you can double-click the URL entry (Right side) to see Request and Response Panels. The top panel is Request (URL, Headers, Body) and Bottom Panel is Response. For https:// (secure URL) make sure you enable HTTPS option in Fiddler (Tools > Options > HTTPS > Check Decrypt https request):

Debugging Web API call using Fiddler in SSIS

Common errors

Parameterization and deployment to production 

Download a sample package

Be sure to download a sample SQL Server 2012 SSIS package, in case you want to try it right away (you can upgrade it to a higher version). If you want to use the database that was used in this tutorial, don’t forget to create the Marketing database from the script (a modified Microsoft’s Northwind database).

Conclusion

In this tutorial, we step-by-step covered how to make geocoding lookups using Geocoding API, part of Google Maps API. We learned how to cleanse, validate and format address just by submitting it to Geocoding API and parsing the results. We moved forward and looked at how to interpret the results and what geocoding results to trust. Then we touched the point that the same process could be applied to reverse geocoding. Concluding, we took a look at how to handle common errors and how to parameterize an SSIS package and deploy it to production.

The post SSIS Geocoding with Google Maps API appeared first on ZappySys Blog.

The scope of this article will be to show how to import records from SQL Server into Elasticsearch index as JSON documents using Elasticsearch Bulk API. In a nutshell, we will retrieve IDs and names of the products from a SQL table, transform each row into a JSON and lastly, index each JSON in Elasticsearch under record’s corresponding ID. This tutorial is going to be your stepping stone to use any Elasticsearch API as a destination.

We will be using these SSIS components of PowerPack to make things work:

	JSON Generator Transform
	Web API Destination

Let’s begin?

Prerequisites

1. SSIS designer installed. Sometimes it is referred as BIDS or SSDT (download it from Microsoft site).
2. Basic knowledge of SSIS package development using Microsoft SQL Server Integration Services.
3. Northwind database deployed on your machine (we will use it to load data from; download it).
4. Elasticsearch instance up and running.
5. ZappySys SSIS PowerPack installed.

<New ZS-OAUTH Connection>

Step-by-Step – Bulk loading data from SQL Server to Elasticsearch with SSIS

Load data from SQL Server database first

In this section you will perform several basic steps to start the package: load data from SQL Server and prepare it for further steps.

1. Create a new SSIS package and drag a Data Flow Task into the Control Flow from the SSIS Toolbox.
   

   Dragging and dropping Data Flow Task into Control Flow

2. Open Data Flow Task and then add OLE DB Source.
3. Configure OLE DB Source to take data from Products table, Northwind database.
4. Choose ProductID and ProductName as columns you will use.

Load data to Elasticsearch using SSIS and ZappySys Web API Destination component.

Transform data into JSON documents

To add data into Elasticsearch firstly we need to prepare it with JSON Generator Transform. It will help us convert table rows into JSON documents.

JSON String to Create Index data for the Bulk API call

1. Close the window and then drag Derived Column component from the SSIS Toolbox.
2. Add two new columns and name them 

   RowHeader

    and 

   RowFooter

   :
   

   Derived Column configuration to add prefix and header and footer to a JSON

   In the Expression column give them values:

   • RowHeader:
     

     "{ \"index\": { \"_index\": \"shop\", \"_type\": \"products\", \"_id\" : \"" + (DT_WSTR,100)ProductID + "\" } }\n"

   • RowFooter:  

     "\n"

3. Now drag JSON Generator Transform from the SSIS Toolbox and open it.
4. Right mouse click on item Mappings and select

   Add Element(s) (Below this node)

   :
   

   Convert SQL table rows into JSON using JSON Generator Transform

5. Once a dialog appears, select

   ProductName

    as Source Column and

   name

    as Output Alias:
   
   

   Map table column to JSON property using JSON Generator Transform

6. You should get a similar view:
   

   JSON Generator Transform configuration to create JSON from a SQL table

7. Then go to Header / Footer tab and:
   • Select

     Direct String

      option.
   • Input

     <%RowHeader%>

      in Header String option.
   • Input

     <%RowFooter%>

      in Footer String option.
   

   JSON Generator Transform configuration to convert rows into JSON suitable for Elasticsearch Bulk operation

8. Close the window.

JSON String to Update Index data for the Bulk API call

From the above section “JSON String to Create Index data for the Bulk API call” follows all the steps From Step-1 to Step-8.

Just for (2nd step) Step-2 use below expression for  

RowHeader

RowFooter

Derived Column configuration to add prefix and header and footer to a JSONIn the Expression column give them values:

• RowHeader:
  

  "{ \"update\": { \"_index\": \"shop\", \"_id\" : \"" + (DT_WSTR,100)ProductID + "\" } }\n{ \"doc\" : "

• RowFooter:  

  "}\n"

JSON String to Delete Index data for the Bulk API call

From the above section “JSON String to Create Index data for the Bulk API call” follows steps Step-1 and Step-2.

Just for (2nd step) Step-2 use below expression for  

RowHeader

Derived Column configuration to add prefix and header and footer to a JSON

In the Expression column give them values:

• RowHeader:
  

  "{ \"delete\": { \"_index\": \"shop\", \"_id\" : \"" + (DT_WSTR,100)ProductID + "\" } }\n"

Finally, Elasticsearch & SQL Server integration

1. Now drag Web API Destination SSIS component into the Data Flow and open it for editing.
2. In Select Connection section press

   <New ZS-HTTP Connection>

   .
3. Once HTTP Connection Manager window opens configure connection to your Elasticsearch instance:
   • Set Web Url, which points to your Elasticsearch instance.
   • Set Credentials Type to

     Basic - UserID/Password

      (or other appropriate authentication method).
   • Finally, set User Name and Password:
     
     

     Configure SSIS HTTP Connection to connect to Elasticsearch

4. Close configuration window. Now it’s time to configure Web API Destination.
5. All that is separating you from getting those rows into Elasticsearch:
   • Setting Input Column for Body to

     ZS_JSON_OUT

      for Create and Update Index Data and for Delete Index Data Input Column for Body to

     RowHeader [Derived Column]

     .
   • Setting URL to

     http://localhost:9200/_bulk

     .
   • Setting HTTP Request Method to

     POST

     .
   • Setting Body content type Method to

     JSON(application/json)

Index SQL Server Database data in Elasticsearch using SSIS component Web API Destination

1. Then open Batch Settings (For Body) tab:
   • Enable batch submission.
   • Set Body Batch Size, e.g. 1000. Make sure this number is even, otherwise you may get into problems (Elasticsearch Bulk request has special JSON request body format, as you perhaps noticed).
   

   Web API Destination batch settings configuration

2. Close the window and run the package! You should see green lights, telling you everything is OK:
   

   Successful data load from SQL Server to Elasticsearch

3. We can also query Elasticsearch by using one of its Search APIs – URI Search to see if we successfully indexed data:
   

   SQL Server data index in Elasticsearch

4. Rows number match in SSIS and in search results, thus everything is good. You are done.

What if I want more? After upserting data use Web API Destination further

Let’s say you have a requirement to do something with rows that were freshly indexed in Elasticsearch. Then you will need somehow to distinguish between created and updated records in Elasticsearch. And that won’t be difficult because Web API Destination acts not only as Destination but as Transformation as well. So one thing you have to do is connect JSON Parser Transform downstream to Web API Destination. It will parse Elasticsearch HTTP JSON response – which is returned by Web API Destination – into columns, which you can later easily redirect using Conditional Split:

1. Add JSON Parser Transform and connect it to Web API Destination:
   

   Use JSON Parser Transform to get Elasticsearch HTTP JSON response when integrating SQL Server & Elasticsearch

2. Make sure you have selected

   ResponseText

    as Select Input JSON Column.
3. Then go to Bulk API and copy/paste “the result of this bulk operation” JSON response:
   

   Using JSON Parser Transform to parse JSON response from REST HTTP request

4. Set

   $.items[*]

    as the filter.
5. Optional step. You won’t find a response sample for every Elasticsearch API call. In that case, you may want to use Postman to make an HTTP request to Elasticsearch and get a sample response:
   

   Use Postman to make a request to Elasticsearch and get a sample response to be used in JSON Parser Transform

   NOTE: Don’t forget to set the username and password in Authorization section!
6. Then use a Conditional Split and Trash Destination to redirect the rows:
   
   Input these clauses:
   • Inserted:
     

     REPLACENULL([index.result],"") == "created" || REPLACENULL([create.result],"") == "created"

   • Updated:
     

     REPLACENULL([index.result],"") == "updated" || REPLACENULL([create.result],"") == "updated"

7. As a result, new index records will be redirected to one destination, while updated records – to the other:
   

Overall you can use Web API Destination HTTP JSON response for other useful things as well, e.g. determine on how many replica shards record was indexed. JSON response will depend on which Elasticsearch API and which method you use.

Delete Index by making an API call.

If you want to delete the index by making Delete API call you can. Let’s make that call using the Rest API Task. Configure it like below screen and click on the Test Request button.

Rest API Delete Method

Things gone bad: Error handling & debugging

Incidentally, you may incorrectly construct JSON for Web API Destination body. Elasticsearch nodes may go offline or go out of memory. In any case you may want to know when that happens and take actions accordingly. For that purpose you have to redirect failed requests from Web API Destination to some other destination:

1. Add a Derived Column above Web API Destination with expression

   (DT_WSTR,4000)ZS_JSON_OUT

    and name it

   JsonAsString

   . This will let you see what JSON you are actually passing.
2. Then add database or file destination or use another Trash Destination for debugging purposes and redirect the bad rows (red arrow) from Web API Destination into it. Don’t forget to set

   Redirect row

    option for both, Error and Truncation columns:
   
3. Finally, add a Data Viewer for the red path, if you want to debug the flow. You will be able to see URL, JSON and the error message for each record. You may want to copy-paste ErrorMessage to Notepad if you want it to be more readable:
   

   Use Data Viewer to view HTTP requests that failed to be fulfilled in Elasticsearch

Call ElasticSearch API hosted on AWS

If your ElasticSearch instance is hosted as AWS Managed ElasticSearch Instance then select  

<New ZS-OAUTH Connection>

Configure SSIS OAuth Connection – Use Amazon AWS API Provider, Enter Access Key, Secret Key

Call AWS Hosted ElasticSearch REST API in SSIS (V4 Request Signing)

Common Errors

Deployment to Production

Download a sample package

Be sure to download a sample SQL Server 2008 SSIS package, in case you want to try it right away (you can upgrade it to a higher version).

Conclusion. What’s next?

In this article we have learned how to bulk load data from SQL Server and upsert it in Elasticsearch index. We used JSON Generator Transform to help us convert database table rows into JSON documents. In addition, Web API Destination was very helpful in automatically creating and making HTTP requests to Elasticsearch instance and indexing our data as a result. This tutorial was quite simple and straightforward, yet it is a good starter to use other Elasticsearch APIs and their features. From now on Elasticsearch reference is your best friend in learning what APIs exist, what are their methods and how HTTP requests should look like.

Keywords

How to import data from SQL Server to Elasticsearch | How to load data into Elasticsearch | Using Elasticsearch to index SQL Server | Elasticsearch and SQL Server integration | How to use Elasticsearch together with SQL Server | Upsert SQL Server data into Elasticsearch

The post How to call Elasticsearch API using SSIS appeared first on ZappySys Blog.