LinkedIn Query

LinkedIn Query

This component uses the LinkedIn API to retrieve data and load it into a table. This stages the data, so the table is reloaded each time. You may then use transformations to enrich and manage the data in permanent tables.

The component offers both a Basic and Advanced mode (see below) for generating the LinkedIn API query. Note that although this is exposed in an SQL-like language, the exact semantics can be surprising - for example filtering on a column can return more data than not filtering on it, an impossible scenario with regular SQL.

Warning: This component is destructive as it truncates or recreates its target table on each run. Do not modify the target table structure manually.

 

Properties

Property Setting Description
Name Text The descriptive name for the component.
Basic/Advanced Mode Choice Basic - This mode will build a LinkedIn Query for you using settings from Data Source, Data Selection and Data Source Filter parameters. In most cases, this will be sufficient.
Advanced - This mode will require you to write an SQL-like query which is translated into one or more LinkedIn API calls. The available fields and their descriptions are documented in the data model.
Authentication Choice Select an authentication method, which must be setup in advance. LinkedIn uses the OAuth standard for authenticating 3rd party applications. More help is provided in the OAuth setup documentation.
Data Source Choice Select a data source, for example Likes.
Data Selection Choice Select one or more columns to return from the query.
Data Source Filter Input Column The available input columns vary depending upon the Data Source.
Qualifier Is - Compares the column to the value using the comparator.
Not - Reverses the effect of the comparison, so "equals" becomes "not equals", "less than" becomes "greater than or equal to", etc.
Comparator Choose a method of comparing the column to the value. Possible comparators include: 'Equal To', 'Greater than', 'Less than', 'Greater than or equal to', 'Less than or equal to', 'Like', 'Null'.
'Equal To' can match exact strings and numeric values while other comparators such as 'Greater than' will work only with numerics. The 'Like' operator allows the wildcard character (%) to be used at the start and end of a string value to match a column. The Null operator matches only Null values, ignoring whatever the value is set to.
Not all data sources support all comparators, thus it is likely only a subset of the above comparators will be available to choose from.
Value The value to be compared.
SQL Query Text This is an SQL-like query, written according to the LinkedIn data model.
Note: When referencing a specific account, use the form ('act_<ACCOUNTNUMBER>') where the account number should be a 17 digit number. For example:
SELECT * FROM ads where target in ('act_12345678901234567')
Combine Filters Text Use the defined filters in combination with one another according to either "and" or "or".
Limit Number Fetching a large number of results from LinkedIn will use multiple API calls. These calls are rate-limited by the provider, so fetching a very large number may result in errors.
Connection Options Parameter A JDBC parameter supported by the Database Driver. The available parameters are determined automatically from the driver, and may change from version to version.
They are usually not required as sensible defaults are assumed.
Value A value for the given Parameter. The parameters and allowed values for the LinkedIn provider are explained here.
Storage Account Select (Azure Only) Select a Storage Account with your desired Blob Container to be used for staging the data.
Blob Container Select (Azure Only) Select a Blob Container to be used for staging the data.
Staging Select (AWS Only) Snowflake Managed: Allow Matillion ETL to create and use a temporary internal stage on Snowflake for staging the data. This stage, along with the staged data, will cease to exist after loading is complete.
Existing Amazon S3 Location: Selecting this will avail the user of properties to specify a custom staging area on S3.
S3 Staging Area Text (AWS Only) The name of an S3 bucket for temporary storage. Ensure your access credentials have S3 access and permission to write to the bucket. See this document for details on setting up access. The temporary objects created in this bucket will be removed again after the load completes, they are not kept.
This property is available when using an Existing Amazon S3 Location for Staging.
Warehouse Select Choose a Snowflake warehouse that will run the load.
Database Select Choose a database to create the new table in.
Type Select Choose between using a standard table or an external table.
Standard: The data will be staged on an S3 bucket before being loaded into a table.
External: The data will be put into an S3 Bucket and referenced by an external table.
Schema Select Select the table schema. The special value, [Environment Default] will use the schema defined in the environment. For more information on using multiple schemas, see this article.
Note: An external schema is required if the 'Type' property is set to 'External'.
Target Table Text Provide a new table name.
Warning: This table will be recreated and will drop any existing table of the same name.
Location Text/Select When using an 'External' type table, Provide an S3 Bucket path that will be used to store the data. Once on an S3 bucket, the data can be referenced by the external table.
Distribution Style Select Even - the default option, distribute rows around the Redshift Cluster evenly.
All - copy rows to all nodes in the Redshift Cluster.
Key - distribute rows around the Redshift cluster according to the value of a key column.
Table-distribution is critical to good performance - see the Amazon Redshift documentation for more information.
Distribution Key Select This is only displayed if the Table Distribution Style is set to Key. It is the column used to determine which cluster node the row is stored on.
Sort Key Select This is optional, and specifies the columns from the input that should be set as the table's sort-key.
Sort-keys are critical to good performance - see the Amazon Redshift documentation for more information.
Project Text The target BigQuery project to load data into.
Dataset Text The target BigQuery dataset to load data into.
Cloud Storage Staging Area Text The URL and path of the target Google Storage bucket to be used for staging the queried data.
Sort Key Options Select Decide whether the sort key is of a compound or interleaved variety - see the Amazon Redshift documentation for more information.
Load Options Multiple Selection Comp Update: Apply automatic compression to the target table (if ON). Default is ON.
Stat Update: Automatically update statistics when filling a table (if ON). Default is ON. In this case, it is updating the statistics of the target table.
Clean S3 Objects: Automatically remove UUID-based objects on the S3 Bucket (if ON). Default is ON. Effectively decides whether to keep the staged data in the S3 Bucket or not.
String Null is Null: Converts any strings equal to "null" into a null value. This is case sensitive and only works with entirely lower-case strings. Default is ON.
Recreate Target Table:Choose whether the component recreates its target table before the data load. If OFF, the existing table will be used. Default is ON.
Load Options Multiple Select Clean Cloud Storage Files: (If On) Destroy staged files on Cloud Storage after loading data. Default is On.
Cloud Storage File Prefix: Give staged file names a prefix of your choice. Default is empty (no prefix).
Encryption Select (AWS Only) Decide on how the files are encrypted inside the S3 Bucket.This property is available when using an Existing Amazon S3 Location for Staging.
None: No encryption.
SSE KMS: Encrypt the data according to a key stored on KMS.
SSE S3: Encrypt the data according to a key stored on an S3 bucket
KMS Key ID Select (AWS Only) The ID of the KMS encryption key you have chosen to use in the 'Encryption' property.
 

Variable Exports

This component makes the following values available to export into variables:

Source Description
Time Taken To Stage The amount of time (in seconds) taken to fetch the data from the data source and upload it to storage.
Time Taken To Load The amount of time (in seconds) taken to execute the COPY statement to load the data into the target table from storage.
 

Strategy

Connect to the target database and issue the query. Stream the results into objects on S3. Then create or truncate the target table and issue a COPY command to load the S3 objects into the table. Finally, clean up the temporary S3 objects.

 

Example

In this example, we used the LinkedIn Query component to gather data on comments made in response to Matillion's LinkedIn status updates. By using the LinkedIn Query component in this way, we can ensure that we're not missing any comments on our updates, and we can analyse comments in bulk. Further to this, we can see an overview of the professions occupied by the LinkedIn users who are engaging with our status updates.

By analysing our LinkedIn status update comment data in this way, we can make decisions regarding how we approach our audience going forward with future posts, news, and content.

So, to load our data, we run the LinkedIn Query component. This job is shown below.

 

The LinkedIn Query component Properties are set up as shown in the next image. We've configured our Authentication using LinkedIn's developer site (in conjunction with our Manage OAuth feature). Our validated authentication enables us to choose Comments as our Data Source. For the Data Selection parameter, we chose the Items of data with which we want to populate our table's columns. Namely, the comment Text, the PersonHeadline (this is a LinkedIn user's bio headline), and the Date of the comments.

Please read through our LinkedIn OAuth documentation if you need assistance setting up OAuth authentication before you use the LinkedIn Query component.

 

Now that we have configured the component Properties, we can run the job. The length of time taken to perform a query will depend on the volume of data being loaded into a table. Once the job is finished, we can Sample our data using a Table Input component within a Transformation Job. See below.