Connect Data from Linkedin to Looker
You can get your LinkedIn data into Looker to make more informed business decisions. This simple and quick guide gives you easy instructions on how you can extract your data from LinkedIn and then analyze it in Looker Our LinkedIn to Looker connector allows you to access your LinkedIn data so you can easily manage various media objects, view metadata, and comments, and get reliable insights and metrics, such as reach, impressions, follower-like ratio, and likes. Integrate your LinkedIn with Sisense to turn your LinkedIn data into actionable insights.
LinkedIn is an extremely popular social media network that is focused on professional relationships, employment opportunities, and business listings. LinkedIn is a great employment-oriented and corporate social networking platform that is primarily used for professional communication and networking. With this platform, employers and job seekers can easily create profiles and form strong and lasting professional connections.
The website offers recruiters and marketers the ability to advertise on its platform via paid social posts and in-mail. With over 610 million members worldwide, which include executives from most Fortune 500 companies, LinkedIn is easily the world's largest and most used professional network on the Internet.
Looker is a popular analytics tool that helps bring data and business teams together by making it quick and easy for everyone to explore and comprehend the data which drives business. Looker Analytics can integrate with any SQL database and data warehouse, like Greenplum and Amazon Redshift. From agile startups to large corporations, savvy companies can easily leverage Looker's amazing analysis capabilities to monitor the overall health of their businesses and also make more data-driven and sound decisions.
Looker is great as it lets companies access, describe, control, explore, and visualize all their data across any web browser, on any device. Looker can help companies of all sizes use data to drive their business activities and decisions in the right direction.
Before loading your data into Looker, you will have to prep it first. If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them.
LinkedIn's documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes. Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. In these cases you'll likely have to create additional tables to capture the unpredictable cardinality in each record.
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Note that you can easily get your data off of Stripe's servers with the Stripe REST API. This API exposes information regarding payment methods, core resources, subscriptions, and a lot more. To get a comprehensive list of all your customers, for example, you could easily call GET /v1/customers and get the results.
Looker will connect to your company's data warehouse or database in order to perform its analyses. This is where the data you would like to analyze is stored and maintained. Some of the popular and commonly used data warehouses are Google BigQuery, Amazon Redshift, and Snowflake. Looker's documentation is helpful in this regard as it offers instructions on how you can configure and connect your database or data warehouse.
Note that in a majority of cases, it simply involves creating and copying your access credentials, such as a password, username, and server information. Then you can easily move your data into your data warehouse from your various data sources for Looker to use. As soon as you connect your data warehouse to Looker, you may build constructs called explores, and each is a comprehensive SQL view that contains a certain set of data for more analysis. An example can be "customers" or "orders."
Keeping data up to date
If you've made it this far, congrats! You probably have a written a program or script to extract your data and move it into Looker. Now it's time to think about how you will keep this data up-to-date by loading updated or new data. Of course, you can just replicate all your data every time you have updated your records, but that would be extremely manual and time-consuming.
Luckily there is a better way. The key is building your script so that it can sense incremental updates made to the data.
Thankfully, LinkedIn’s REST API results include fields so that you can identify those records which are new since the last update you made (or since the most recent record you have copied). Once you have taken new data into consideration, you can easily set your script either as a continuous loop or cron job to pull down new data as soon as it appears.
The Easiest And Fastest Way To Do It
If all this sounds a bit overwhelming, don't worry -- there is an easier way to get this done!
Thankfully, products like Improvado were developed to move data from LinkedIn to Tableau automatically. Using Improvado, you can easily combine the most crucial and relevant data from your ad campaign into a dashboard. You can then connect this data to Tableau.