Google BigQuery is a fully managed Big Data platform to run queries against large scale data. In this article you will learn how to integrate Google BigQuery data into Microsoft SQL Server using SSIS. We will leverage highly flexible JSON based REST API Connector and OAuth Connection to import / export data from Google BigQuery API just in a few clicks.

If you are looking for a similar product inside Amazon AWS Cloud then check an article about Amazon Athena.

Prerequisites

Before we look into a Step-By-Step section to extract and load data from BigQuery to SQL Server let’s make sure you meet the following requirements:

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. ZappySys SSIS PowerPack installed. Click on the link to download a FREE trial.
4. You have basic familiarity with REST API concepts and Google BigQuery API. This post uses free Public Dataset to query BigQuery table so no billing is required to get your hands dirty 🙂 However, if you like to query your own dataset then make sure you have at least one BigQuery dataset and one table with some sample data created (this part does require billing enabled). Read a Google Quickstart article for more information on how to create a new BigQuery dataset and a table.

Understanding Google BigQuery Object Heirarchy

Google BigQuery has 3 main concepts below.

• Project
  • Dataset
    • Table
      • Query requests (each query creates a unique JobID – valid for 24 hours from which you can read data)

So in order to create BigQuery table you always need Project and then Dataset under that project. You can group objects around Project / Datasets. When you query you can supply fully qualified name of your table in FROM clause (e.g.

select count(*) from `bigquery-public-data.usa_names.usa_1910_2013`

So let’s get started.

Read data from Google BigQuery using SSIS

Basically you can query Google BigQuery data in two ways: In this article we will not cover 2nd method.

• Method-1: Query data using jobs/query method in BigQuery API. Use it if you expect to get a result in a fairly short amount of time. This API method generates a temp table which gets deleted after 24 hours. You can read data within that time frame using newly created JobId reference.
  
  NOTE: If you have records that sum up to more than 10MB of data or ~10,000 rows (assuming 1 row uses 1KB of data) then you need to proceed with two-step process as explained below.
• Method-2: Export SQL query result using jobs/insert method in BigQuery API. Use this method if you expect a query to take a long time to finish.

Now let’s see how to query Google BigQuery data using SSIS 🙂

Create Google API Project and Register OAuth App

To use any Google API firstly you have to finish two tasks:

1. Create Google API Project and obtain projectId (see next section).
2. Enable BigQuery API
3. Register your own Google OAuth App and obtain ClientId and Client Secret.

Check step-by-step instructions on how to create Google API Project and create OAuth App for Google. During instructions make sure you enable BigQuery API (screenshots may differ).

Get Google API Project ID

Once you have Google API Project created you can grab Project ID (see screenshot below). You will need Project ID in the next step when building API URL.

How to find Google API Project ID?

Configure OAuth Connection for Google BigQuery API

In order to call most of Google APIs you will need an OAuth App. If you want to use your own app then refer to how to register Google OAuth Application (Get ClientID and ClientSecret) article. For simplicity we will use Default OAuth App:

1. Right click inside Connection Managers area and click “New Connection…”
2. From the connection type list select “ZS-OAUTH” connection type.
   

   Create new SSIS OAuth API Connection Manager

3. For OAuth Provider select “Google”.
4. For OAuth App type options leave “Use Default OAuth App” selected. If you created custom OAuth App then you would need to select “Use Custom OAuth App” option.
5. For Scopes enter or select the following URLs (URL is just a permission name). Each URL must be in a separate line. For demo purposes we use only the first 3 scopes but we included a few more in case you like to test API in depth with a different permission set:
   

   https://www.googleapis.com/auth/bigquery
   https://www.googleapis.com/auth/cloud-platform
   https://www.googleapis.com/auth/cloud-platform.read-only
   https://www.googleapis.com/auth/bigquery.insertdata
   https://www.googleapis.com/auth/devstorage.full_control
   https://www.googleapis.com/auth/devstorage.read_only
   https://www.googleapis.com/auth/devstorage.read_write

6. Click Generate Token. Login using correct account if needed and then you will be prompted to click “Approve” OAuth App.
7. Once you click OK on the login screen you will see new tokens populated on the connection screen.
8. Click Test to make sure connection / tokens are valid and then click OK to save the connection.
   NOTE: Below screenshot uses Default App but we recommend you to to use Custom OAuth App (Your own Clientid / secret – Obtained in previous section)
   

   Create SSIS OAuth API Connection for Google BigQuery API

Start BigQuery Job and get JobId (Submit Query)

Once you have SSIS OAuth connection created for BigQuery API it’s time to read data from BigQuery. So, basically, there are two ways you can read BigQuery data: using query or insert method. For demo purposes we will use jobs/query method. If you want fire complex queries which can run for many minutes then refer to jobs/insert method.

If you are expecting more than 10MB of data (~10K records) then you have to split data extract in two steps:

1. Step-1 : Call jobs/query method using REST API Task to submit SQL query. This steps returns jobComplete: true if supplied query execution is done within timeoutMs parameter you included. By default its 10 seconds so if your query is going to take longer than 10 seconds then its good idea to add bigger timeout that way you dont need step#2. Once API call is done you will Get JobId in the response. Save that JobId in a variable for later use. This job result is valid for 24-hours only.
2. Step-2 (Optional) – You can add another optional REST API Task after previous step to Wait until Job is completed. For simplicity, this article will Omit setting up Status Check but its very simple….Use below settings
   1. Drag new REST API Task from toolbox. Connect it to Step-1 and double click to configure as below.
   2. Select URL From Connection mode,
   3. Select OAuth connection
   4. Enter URL as https://www.googleapis.com/bigquery/v2/projects/{{User::ProjectId}}/jobs/{{User::JobId}} , Method: GET
   5. On Response Settings Tab, Select Response Content Type JSON and for content filter enter $.status.state
   6. On Status Check Tab, check Enable Status Check Loop and enter DONE in SuccessValue field
   7. Doing this setup will make sure we do not query data until Job Status is DONE (System keeps checking every 5 seconds)
3. Step-2 : Read Job result using JSON Source in a Data Flow Task.

Now lets see how to configure REST API Task to submit query and extract JobId (which will be used in next section).

1. In the Control Flow designer drag and drop ZS REST API Task from the SSIS toolbox.
2. Double click the task to configure it.
3. Change Request URL Access Mode to [Url from connection].
4. From the connection dropdown select OAuth connection created in the previous section.
5. Enter URL as below (replace YOUR-PROJECT-ID with a valid Project ID obtained in the previous section):
   

   https://www.googleapis.com/bigquery/v2/projects/YOUR-PROJECT-ID/queries

6. Select Method as POST.
7. Enter Body (Request Data) as below (change your query if needed; we used Public dataset for the demo): If you do not specify timeout then default is 10 seconds only. Also in this call we only care about JobID from response so we just added maxResults=10 because in 2nd step we will get all rows by doing pagination.
   

   {
      "timeoutMs": 100000, 
      "maxResults": 10, 
      "query": "SELECT title id,language,wp_namespace,reversion_id ,comment ,num_characters FROM [bigquery-public-data:samples.wikipedia] LIMIT 100000"
   }

   To use a Standard SQL query instead of Legacy SQL, add useLegacySql property and set it to false:
   

   {
      "timeoutMs": 100000, 
      "maxResults": 10, 
      "query": "SELECT title id,language,wp_namespace,reversion_id ,comment ,num_characters FROM [bigquery-public-data:samples.wikipedia] LIMIT 100000",
      "useLegacySql": false
   }

   For all possible parameters refer to jobs/query method documentation.
   NOTE: You can also supply location Parameter in the Body JSON to indicate where job should run. For non EU / US datacenters we suggest you to supply this parameter. See details at https://cloud.google.com/bigquery/docs/locations#specifying_your_location.
   Example Use Of Location
   

   { "location":"us-east1",  "maxResults": 10, "query": "SELECT title FROM [bigquery-public-data:samples.wikipedia] LIMIT 10" }

8. Select Body Content Type as application/json.
9. Click Test Request/Response. If things go well you will see JSON content. Then just copy JobId from the response and save for later use:
   
10. Now go to Response Settings tab.
11. Select ContentType=Json.
12. Enter below expression to extract JobId from the response. You will need this Id to read SQL query output (this JobId is valid for 24 hrs):
    

    $.jobReference.jobId

13. Choose save response content to save to a variable. Select <New Variable…> option. When prompted give a name to your variable (i.e. vJobId) and for the value field paste JobId copied from the above step:
    
    
    NOTE: This JobId expires after 24-hrs so while you are designing a package and not running it then design time value is used for testing/previewing. So make sure your JobId is valid. If needed click Test Request/Response to grab a new JobId and update the variable with a new value so you can preview data without running a full package.
14. Click OK to save the UI.

Read BigQuery SQL result (for specified JobID)

Now let’s look at how to read data from BigQuery

Configure Google BigQuery Web Request (URL, Method, ContentType, Body etc.)

1. In the Control Flow designer drag and drop Data Flow Task from SSIS toolbox.
   
2. Double click Data Flow Task and drag and drop ZS JSON Source (For API/File) from SSIS toolbox.
   
3. Double click JSON Source to edit and configure as below
4. Enter URL as below. Replace YOUR-API-PROJECT-ID with the API Project ID obtained in the previous section.
   

   https://www.googleapis.com/bigquery/v2/projects/YOUR-API-PROJECT-ID/queries/{{User::vJobId}}?maxResults=10000

   –OR– (Use below if your Job ran outside US / EU data center)
   

   https://www.googleapis.com/bigquery/v2/projects/YOUR-API-PROJECT-ID/queries/{{User::vJobId}}?location=YOUR_REGION_ID&maxResults=10000

   NOTE: location Parameter indicates the geographic location of the job. This is Required parameter except for US and EU. See details at https://cloud.google.com/bigquery/docs/locations#specifying_your_location. You can supply same location parameter for first step when you submit the query

   Failure to supply location parameter for non US /EU users may result in 404 NotFound Error.
   

5. Check Use Credentials option and select OAuth Connection Manager created in the previous section.
6. For HTTP Request Method select GET.
   

Read data from Google BigQuery from Temp Job result

Configure Filter

We need to transform a single JSON response into multiple rows so we need to apply a correct filter.

1. On JSON Source go to Filter Options tab.
2. In the Filter field enter $.rows[*] or click [Select Filter] button to browse hierarchy you want to extract.

Select Filter for JSON Response

Configure BigQuery API Pagination Settings

Most of modern APIs usually implement some sort of pagination technique so you get a part of data in each request rather than all in one go. Thus if you want more you can paginate through pages until the last page is reached. You can also read Understanding REST API Pagination in SSIS article to learn more about pagination in SSIS.

BigQuery API returns pageToken attribute in response JSON if more data is found for requested query result. You can then pass pageToken in the next URL in this format: http://my-api-url/?pageToken=xxxxxxxxxx

Now lets configure JSON Source to automate this pagination for us. On JSON Source go to Pagination Tab and enter the following two settings:

1. Set Next Link as $.pageToken.
2. Set Suffix for Next URL as &pageToken=<%nextlink%>.

See the screenshot below to get more clarity:

Configure BigQuery API Pagination on SSIS JSON Source

Configure Array Transformation

Now the last thing we have to configure is special 2-dimensional JSON array format used by BigQuery API:

1. On the JSON Source UI go to 2D Array Transformation tab.
2. Enter the following settings:
   1. For Transformation Type select Transform complex 2-dimensional array.
   2. For Column Name filter enter $.schema.fields[*].name.
   3. For Row Values Filter enter $.f[*].v.

JSON Array Transformation Options

Preview Data and Save UI

That’s it click Preview to see some data. If you entered sample JobID for your User::vJobID variable then you will see some data. In the next section we will see how to load Google BigQuery data into SQL Server. You can click Columns Tab to review Metadata.

Click OK to save UI and generate Metadata.

Configure Target – Load Google BigQuery data into SQL Server

Now you can connect your JSON Source to any target such as ZS Trash Destination or a real database destination such as OLEDB Destination.

If you wish to dump data from Google BigQuery to a SQL Server table then just perform the following steps:

1. Drag and drop OLEDB Destination from SSIS Toolbox. Rename it to something like SQL Server Table.
2. Double click on OLEDB Destination.
3. Click New to create a new connection > Configure connection > Click OK.
4. Click on New to create a new table > Rename default table name > Click OK.
5. Click on Mappings tab and click OK to save UI with default mappings.

Configure SSIS OLEDB Destination – Google BigQuery to SQL Server Import

Execute Package – Loading BigQuery data into SQL Server

SSIS Package Execution –
Loading Google BigQuery Data into SQL Server

Create / Delete Google BigQuery Dataset using API call

As we mentioned before, you need dataset before you can create a table. Most common way to create dataset is via User Interface but what if you like to automate from your SSIS Package or other workflow? Here is how you can create or delete. Basically you can use REST API Task to send CREATE or DROP command for Dataset object

Create Google Dataset

To create dataset configure REST API Task using below settings (We will call this API)

1. Drag REST API Task from toolbox
   
2. Select URL from Connection mode and select connection as OAuth connection (Created in previous section)
3. Enter below URL (assuming you stored your ProjectID in a variable called ProjectId
   

   https://bigquery.googleapis.com/bigquery/v2/projects/{{User::ProjectId}}/datasets

4. Request Method as POST
5. Enter Request Body as below (change YOUR_DATASET_NAME – e.g. TestDataset )
   

   {"datasetReference": { "datasetId": "YOUR_DATASET_NAME", "projectId": "{{User::ProjectId}}"} }

6. Select Request Content Type as application/json from the dropdown
7. (Optional) – If you want to implement Continue if Dataset already exists then you can go to Error Handling Tab (See next section for screenshot) and check Continue On Error Code option and set 409 status code
8. That’s it. Click Test Request/Response see it works.

Delete Google BigQuery Dataset

For deleing dataset you have to choose same steps as above except two things

1. Request Method as DELETE
2. Request Body as blank

Create / Delete Google BigQuery Table using API call

Now let’s look at how to create /drop Table in Google BigQuery using API calls.

Create Google BigQuery Table

To send CREATE TABLE  SQL statement (DDL) we have to use same approach as we send normal SQL Query using this API call. So notice we used Standard SQL for this call by supplying  useLegacySql: false . DDL Statement must be all in one line

POST https://www.googleapis.com/bigquery/v2/projects/{{User::ProjectId}}/queries

Content-Type: application/json

>>>> BODY <<<<<

{
 "query": "CREATE TABLE TestDataset.Table1 (RecordID INT64,CustomerID STRING,CustomerName STRING);",
 "useLegacySql": false,
 "timeoutMs": 100000 
}

To create dataset configure REST API Task using below settings

1. Drag REST API Task from toolbox
   
2. Select URL from Connection mode and select connection as OAuth connection (Created in previous section)
3. Enter below URL (assuming you stored your ProjectID in a variable called ProjectId
   

   https://bigquery.googleapis.com/bigquery/v2/projects/{{User::ProjectId}}/queries

4. Request Method as POST
5. Enter Request Body as below
   

   {
    "query": "CREATE TABLE TestDataset.Table1 (RecordID INT64,CustomerID STRING,CustomerName STRING);",
    "useLegacySql": false,
    "timeoutMs": 100000
   }

6. Select Request Content Type as application/json from the dropdown
7. (Optional) – If you want to implement Continue if Table exists then you can go to enable Error Handling Tab and check Continue On Error Code option and set 409 status code
8. That’s it. Click Test Request/Response see it works.

Create Google BigQuery Table – API Call (Continue On Error Setting – Skip CREATE if Table / Dataset Already Exists)

Delete Google BigQuery Table

For deleing table you have to choose same steps as above except two things

1. Request Method as DELETE
2. Request Body as blank

Write data to Google BigQuery using SSIS – 1 Million row insert test (FAST)

Now let’s look at how easy it is to import data into Google BigQuery using SSIS. We will use the same OAuth connection we created before. To learn more about inserting data into BigQuery check tabledata/insertAll method documentation.

Make sure billing is enabled

Make sure that billing is enabled for your Google Cloud project. Learn how to confirm billing is enabled for your project.

Streaming is not available via the free tier. If you attempt to use streaming without enabling billing, you receive the following error: BigQuery: Streaming insert is not allowed in the free tier.

As long as your API calls fall under Free Tier  limit you wont be charged but you still need to enable billing if you wish to call Streaming insertAll API call (Write  demo).

Configure OAuth Connection / Permissions

If you want to perform data insert operation in BigQuery using API calls then include the following scopes in your OAuth Connection Manager and generate a token (see our first section of this article – We already included scopes for Write operation but incase you didnt do then regenerate token with below scopes):

https://www.googleapis.com/auth/bigquery
https://www.googleapis.com/auth/cloud-platform
https://www.googleapis.com/auth/bigquery.insertdata

Create BigQuery Dataset (From UI)

For this demo first create a test dataset and one table under it like shown below (billing must be enabled on your Google API Project). To do that go to https://bigquery.cloud.google.com/welcome and configure them:

Insert data into BigQuery using API call

And here is a REST API example to insert data into a BigQuery Table:

Request URL:

https://www.googleapis.com/bigquery/v2/projects/MY_PROJECT_ID/datasets/MY_DATASET_ID/tables/MY_TABLE_ID/insertAll

Request Headers:

Authorization: Bearer ya29.Gl0cBxxxxxxxxxxxxxxxxxxuEIhJIEnxE6GsQPHI
Content-Type: application/json

Request Body:

{
  "kind": "bigquery#tableDataInsertAllRequest",
  "rows": [
     {"json": {"RowId": 1,"CustomerName": "AAA"} }, 
     {"json": {"RowId": 2,"CustomerName": "BBB"} }
   ]
}

Here is our data flow setup to achive very high throughput for Google BigQuery Data Load. We will show you how to insert one million rows in Google BigQuery in less than a minute based on below setup. We will use Multi Threading option and New Compression Option (Added in v3.1.4)

1. Dummy Data Source – Generate sample records
2. JSON Generator Transform – Generates JSON documents to send as POST request for above /insertAll API call.
3. Web API destination  – Call /insertAll API call to submit our data to BigQuery
4. (Optional) JSON Parser Transform – Parse Error Message for any response
5. (Optional) Trash Destination – Save any errors to text file for review

Google BigQuery Data Load Demo in SSIS – 1 Million Rows Insert with Multi Threads and Compression ON (Fast Upload)

Configure SSIS JSON Generator – Generate JSON for BigQuery Table Insert

Now let’s see how to build an HTTP request with JSON body and send it to BigQuery:

1. Drag Data Flow Task and double click on it.
   
2. Drag and configure your Source (for this demo we use Dummy Data Source with Customer Template). See previous section for configuration of Dummy Data Source.
   
3. Drag and drop ZS JSON Generator Transform to produce JSON from a database or file records. If your source is already sending a valid JSON then you can skip this step (e.g. SQL query is
   returning JSON). You can also read raw JSON from a very large file (new-line separated JSON) using JSON Source with Output as Raw Data option checked.
4. Connect Source to JSON Generator. Double click JSON Generator Transform to start configuring it like below.
5. Select Output Mode as Single Dataset Array and enter Batch Size 10000 (This is Max limit allowed by Google BigQuery API insertAll)
6. First right click on Mappings node and select Add Static Element
   

   Generate JSON for Google BigQuery InsertAll API request – Batch 10000 rows in a single API call

7. Enter Name as kind and value as bigquery#tableDataInsertAllRequest
   
   

   JSON Generator – Add Static Element

8. Now right click on Mappings node and select Add Document Array option
   

   JSON Generator – Add Document Array

9. Enter rows as array title and click OK
   

   JSON Generator – Name array

10. Select newly added array node and right click > Add unbound nested element  enter Output alias as json and click OK.
    

    JSON Generator – Add unbound nested element

11. Select json node and right click > Select Add Elements below this node and select multiple columns you like to send to BigQuery. Click OK to save.
    

    JSON Generator – Add Multiple Elements

12. Now let’s preview our JSON (Copy preview JSON to try in the next step – Web API destination)
    NOTE: Table name and column names are case-sensitive so make sure your JSON attribute matches exact same way.
    Here is the finished JSON Structure for next Step
    

    Sample JSON Request body for Google BigQuery insertAll API request

13. Click OK to save UI.

Configure SSIS Web API destination – Insert data into BigQuery Table

Once you have Input JSON prepared,  now let’s configure destination for BigQuery.

1. Drag and drop ZS Web API Destination.
2. Connect your JSON Generator Transform to Web API destination.
3. Configure general properties:
   

   SSIS Web API Destination –
   Configure for BigQuery Data load

4. Make sure to enter URL in this format:
   

   https://www.googleapis.com/bigquery/v2/projects/MY_PROJECT_ID/datasets/MY_DATASET_ID/tables/MY_TABLE_ID/insertAll

   Make sure to replace 3 parts in above URL (MY_PROJECT_ID, MY_DATASET_ID, MY_TABLE_ID) with actual values from your Google Project and BigQuery dataset/table configuration.
5. Now you can enable Compression and Multiple Threads for higher throughput as below.
   NOTE: Compression Property was added in v3.1.4 so you may not see it if you have older version.
   

   Google BigQuery Data Loading Performance Optimization Options – Enable Multiple Threads and Compression Options

6. If you want to try test insert request from UI without running full package then go back to first tab and edit Body (Use Sample JSON generated by previous JSON Transform – You can grab from JSON Preview Panel on Generator Transform).Click Test Request / Response and confirm Success as below. You can go back to your BigQuery Portal and check one row is inserted after our test click. If everything looking good then run full package to insert all records.Sample JSON for Body:
   

   {
     "kind": "bigquery#tableDataInsertAllRequest",
     "rows": [
        {"json": {"RowId": 1,"CustomerName": "AAA"} }, 
        {"json": {"RowId": 2,"CustomerName": "BBB"} }
      ]
   }

   

   Test Google BigQuery Table Insert – SSIS Web API Destination UI

7. Hit OK to save UI.
8. Run the package and verify data in Google BigQuery Console:
   

   Loading data into Google BigQuery using SSIS

Error Handling for BigQuery Data Load (Bulk Insert API Calls)

There will be a time when some records you insert may not go well in Google BigQuery. In such case you can read output from Web API destination and parse further with JSON Parser Transform. Check for certain values in the output. You must use JSON Parser after Web API destination (Connect Blue Arrow from Web API destination – Since its Soft Error it won’t redirect in Red Arrow ).

For example here is the sample JSON in POST Body for testing which produces error due to bad column name. When bad row found in batch all records will be rejected. Notice that error returns index of record in batch so you can identify which row went bad. It also returns column name in location attribute.

NOTE: Bad column name in 2nd record

Test Body (Bad):

{
  "kind": "bigquery#tableDataInsertAllRequest",
  "rows": [
     {"json": {"RecordID": 1,"CustomerID": "X1"} }, 
     {"json": {"Bad_Column": 2,"CustomerID": "X2"} }
     {"json": {"RecordID": 3,"CustomerID": "X3"} }, 
   ]
}

Response (For Bad Input):

{
  "kind": "bigquery#tableDataInsertAllResponse",
  "insertErrors": [
    {
      "index": 1,
      "errors": [
        {
          "reason": "invalid",
          "location": "bad_column",
          "debugInfo": "",
          "message": "no such field."
        }
      ]
    },
    {
      "index": 0,
      "errors": [
        {
          "reason": "stopped",
          "location": "",
          "debugInfo": "",
          "message": ""
        }
      ]
    },
    {
      "index": 2,
      "errors": [
        {
          "reason": "stopped",
          "location": "",
          "debugInfo": "",
          "message": ""
        }
      ]
    }
  ]
}

To configure error detection perform following steps.

1. Drag and drop ZS JSON Parser Transform after Web API destination
2. Click on Web API destination. Connect Blue arrow  to JSON Parser Transform
3. Configure JSON Parser Transform like below
4. Connect JSON Parser TRansform to some Destination to save error information (e.g. SQL Table or Trans destination)

Handling BigQuery Insert Errors in SSIS

Other Common Errors in BigQuery API calls

In this section we will talk about many common API errors in BigQuery.

Error: The project XXXXXXX has not enabled BigQuery

Sometimes you might get below error. To fix this error make sure you go to your Project and Enable BigQuery API

Status Code: BadRequest

Response Body: {
  "error": {
    "code": 400,
    "message": "The project bigquerytest-281915 has not enabled BigQuery.",
    "errors": [
      {
        "message": "The project bigquerytest-281915 has not enabled BigQuery.",
        "domain": "global",
        "reason": "invalid"
      }
    ],
    "status": "INVALID_ARGUMENT"
  }
}

Error: 404 – Not Found: Table / Job xxxxxx

• Make sure you are setting GET request and not POST.
• Also make sure your jobId is valid because it expires after 24 hours.
• Make sure project-id supplied in URL is valid ID  (DO NOT Specify Alias, use internal ID for project)
• Make sure you supplied location parameter if your outside EU / US region. Reading data using this API call might fail if you failed to supply location in URL

{
 "error": {
  "errors": [
   {
    "domain": "global",
    "reason": "notFound",
    "message": "Not Found: Table xxxxxx"
   }
  ],
  "code": 404,
  "message": "Not Found: Table xxxxxxx
 }
}

Error: 403 – Access Denied: BigQuery BigQuery: Streaming insert is not allowed in the free tier

If you trying call certain APIs on sandbox mode or free tier then you might get below error. To overcome this error enable billing on google bigquery. As long as your API calls fall under Free Tier  limit you wont be charged but you still need to enable billing if you wish to call Streaming insertAll API call.

Response Url: https://www.googleapis.com/bigquery/v2/projects/bigquerytest-281915/datasets/TestDataset/tables/Table1/insertAll

Status Code: Forbidden

Response Body: {
  "error": {
    "code": 403,
    "message": "Access Denied: BigQuery BigQuery: Streaming insert is not allowed in the free tier",
    "errors": [
      {
        "message": "Access Denied: BigQuery BigQuery: Streaming insert is not allowed in the free tier",
        "domain": "global",
        "reason": "accessDenied"
      }
    ],
    "status": "PERMISSION_DENIED"
  }
}

Debugging Web API Requests

If you need to debug actual requests made to Google server then you can use a tool like Fiddler. It’s a very handy tool to troubleshoot JSON format issues. It will allow to see how a request is made to a server.

Using Fiddler to debug Google BigQuery API requests in SSIS

Common Errors

Deployment to Production

Download Sample Package

Click Here to Download SSIS Sample Package – Google BigQuery API Read Write Create Delete (SSIS 2019, 2017, 2012)

Conclusion. What’s next?

In this article we have learned how to load data from Google BigQuery into SQL Server using SSIS (drag and drop approach without coding). We used SSIS JSON / REST API Connector to extract data from Google BigQuery REST API using OAuth. JSON Source Connector makes it super simple to parse complex/large JSON files or any Web API response into rows and columns so you can load data into a database, e.g. SQL Server database. Download SSIS PowerPack to try many other automation scenarios that were not discussed in this article.

Keywords:

Google BigQuery Integration with SQL Server | How to extract data from google bigquery in SSIS? | How to read data from Google BigQuery API? | Loading BigQuery Data into SQL Server. | BigQuery to SQL Server | SSIS Google Big Query Integration | SSIS Google BigQuery Import  JSON File | SSIS Google BigQuery Export data

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In this article you will learn how to Setup Amazon Redshift Cluster in few clicks. You will also learn how to set Inbound and Outbound Firewall Rules so you can access Redshift Cluster from outside of AWS Network (e.g. from your corporate network or your home). By default Redshift Cluster cannot be access from outside of your AWS Virtual Network (referred as VPC – Virtual Private Cloud)

Once Redshift Cluster is setup you can follow these steps to Load data into Redshift (Using SSIS Redshift Data Transfer Task or Command line for Redshift)

What is Amazon Redshift

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. You can start with just a few hundred gigabytes of data and scale to a petabyte or more. This enables you to use your data to acquire new insights for your business and customers.

The first step to create a data warehouse is to launch a set of nodes, called an Amazon Redshift cluster. After you provision your cluster, you can upload your data set and then perform data analysis queries. Regardless of the size of the data set, Amazon Redshift offers fast query performance using the same SQL-based tools and business intelligence applications that you use today.

Setup Amazon Redshift Cluster

Add inbound rule for Redshift Cluster

Automate Redshift Cluster Creation

If you have need to automate Redshift Cluster Creation or any of the following things automatically then check Redshift Cluster management Task

• Automate Amazon Redshift Cluster Create Action in few clicks. You can also add Access Security Rule.
• Automate Amazon Redshift Cluster Delete Action
• Fetch Amazon Redshift Cluster Property to SSIS Variable (e.g. Fetch Cluster Status)
• Fetch all cluster and their properties as DataTable (Use ForEach Loop and iterate through all clusters)
• Automate Redshift Cluster Snapshot Creation
• Automate Redshift Cluster Snapshot Delete Action
• Support for Wait until Cluster operation is done

The post How to setup Amazon Redshift Cluster in few clicks appeared first on ZappySys Blog.

In our previous article we saw how to load data into Redshift using SSIS or load data into Redshift using ZappyShell Redshift Command Line

In this article we will walk through various steps to Extract/UNLOAD Redshift Data into SQL Server using Amazon S3 Storage Task and ExecuteSQL Task for Amazon Redshift. Below is the screenshot of actual SSIS Package to Extract Redshift Data and Load into SQL Server

Extract/Unload Redshift Data using SSIS and Load into SQL Server

Requirements for Extract Redshift Data using SSIS

Before you UNLOAD data from Redshift, you have to make sure few things.

1. Setup your Redshift cluster (Follow these instructions to setup redshift cluster)
2. Load some sample data to Redshift (Red more here: How to load data to Redshift)
3. Make sure you have correct connection settings to connect to Redshift cluster (Host name, Port, UserId, Password, DB name etc). You can get host name from AWS Console.
4. Make sure you have Access to S3 Bucket where files will be dumped from Redshift. You will need AccessKey and SecretKey to fetch files from S3

Step-1: Execute Redshift UNLOAD Command

Very first step would be to unload redshift data as GZip file using ExecuteSQL Task for Amazon Redshift
Below is SQL Command you can use to extract data from Redshift. Notice how we used variable placeholders in SQL Command. These placeholders are replaced at runtime with actual value stored in specified variable.

unload ('select * from (select * from customerdata limit 1000)')
to 's3://bw-rstest/stage/custdata'
credentials 'aws_access_key_id={{User::S3Accesskey}};aws_secret_access_key={{User::S3SecretKey}}'
ALLOWOVERWRITE

Export as GZip files (Compressed files)

If you exporting data as compressed files to save data transfer cost then use GZIP option as below.

NOTE: Make sure there are no spaces before and after AccessKey and SecretKey otherwise you may get error.

unload ('select * from (select * from customerdata limit 1000)')
to 's3://bw-rstest/stage/custdata_file_'
credentials 'aws_access_key_id={{User::S3Accesskey}};aws_secret_access_key={{User::S3SecretKey}}'
ALLOWOVERWRITE
GZIP

Common Errors / Troubleshooting

UNLOAD command issue with Region mismatch (S3 bucket vs Redshift Cluster)

If your S3 bucket is in different region than Redshift cluster then above command may fail with “301 permanent redirect error” in that case you have to change your S3 bucket region. Region can be changed in AWS console (See S3 bucket properties and change location to match region with Redshift cluster region. Both regions must be same.

ERROR: XX000: S3ServiceException:The bucket you are attempting to access must be addressed using the specified endpoint. Please send all future requests to this endpoint.,Status 301,Error PermanentRedirect

UNLOAD command issue with accesskey and secret key

If you specify invalid accesskey or secretkey –or– you have misspelled keywords related to credentials — or — you have spaces before or after accesskey or secret key then you may get following error.

ERROR: XX000: Invalid credentials. Must be of the format: credentials ‘aws_iam_role=…’ or ‘aws_access_key_id=…;aws_secret_access_key=…[;token=…].

Step-2: Download data files from Amazon S3 Bucket to local machine

Once files are exported to S3 bucket we can download then to local machine using Amazon S3 Storage Task

Step-3: Un-compress downloaded files

If you have exported Redshift data as compressed files (using GZIP option) then you can use ZappySys Zip File task to un-compress multiple files.

Or you can write Script to un-compress those files (see below code). You can skip this step if files are not compressed (not used GZIP option in command).

Here is sample C# code to un-compress GZip files

public void Main()
{
	System.IO.DirectoryInfo directorySelected = new System.IO.DirectoryInfo(@"C:\amazon\archive");

	foreach (System.IO.FileInfo fileToDecompress in directorySelected.GetFiles("custdata*_part_*"))
	{
		Decompress(fileToDecompress);
	}

	Dts.TaskResult = (int)ScriptResults.Success;
}
private static void Decompress(System.IO.FileInfo fileToDecompress)
{
	using (System.IO.FileStream originalFileStream = fileToDecompress.OpenRead())
	{
		string currentFileName = fileToDecompress.FullName;
		string newFileName = currentFileName.Remove(currentFileName.Length - fileToDecompress.Extension.Length);

		using (System.IO.FileStream decompressedFileStream = System.IO.File.Create(newFileName))
		{
			using (System.IO.Compression.GZipStream decompressionStream = new System.IO.Compression.GZipStream(originalFileStream, System.IO.Compression.CompressionMode.Decompress))
			{
				decompressionStream.CopyTo(decompressedFileStream);
				//Console.WriteLine("Decompressed: {0}", fileToDecompress.Name);
			}
		}
	}
}

Step-4: Loop through files using ForEachLoop Container

Once files downloaded from S3 bucket we can now loop through files using SSIS ForEach Loop Task and load into SQL Server (One file in each iteration)

Loop through files downloaded from Amazon S3 (Exported using Redshift UNLOAD Command)

Step-5: Data Flow – Load Redshift Data Files to SQL Server

Inside data flow you can use Flat File source and OLEDB Destination for SQL Server. Just map correct File columns to SQL Server fields and you should be good. If needed convert Unicode/Non-unicode columns using Data Conversion Transform (This is not needed if source is DT_STR and target also DT_STR.. or source is DT_WSTR and target is DT_WSTR i.e. Unicode).

Downloads

To download above SSIS Package click on the below links. In order to test below package you first have to download SSIS PowerPack
Download Demo SSIS Package – SSIS 2008
Download Demo SSIS Package – SSIS 2012/2014

Conclusion

amazon Redshift is great way to start your data warehouse projects with very minimum investment in a very simple pay as you go model but loading or unloading data from redshift can be challenging task. Using SSIS PowerPack you can perform Redshift data load or unload in few clicks.

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