Connect Data from Twitter to Looker

You can get your Snapchat 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 Snapchat and then analyze it in Looker. Our Snapchat to Tableau connector allows you to access your Snapchat 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 Snapchat with Looker to turn your Snapchat data into actionable insights.

Twitter is a popular online social networking as well as news service. It enables users to read, post and send short messages (140 characters in length) called "tweets". People frequently post Tweets, which can contain photographs, links, videos, and text. Note that these messages are posted to your Twitter profile, sent to your Twitter followers, and are also easily searchable on Twitter search.

Although registered users can both read and post tweets, unregistered users only have access to read. Twitter allows its users access through the website interface, the mobile app, and SMS. Twitter also allows marketers and business brands to advertise on its incredibly popular platform. You can capture data about popular retweets, content, social shares and a lot more for advanced analytics.

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.

Extracting data

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.

Twitter'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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Preparing data

Note that Twitter makes its data available to its users through its Standard search API. This API offers data on several things, such as ad insights, estimated positions, estimated bids, and many other types of data.

Loading data

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

The key is building your script so that it can sense incremental updates made to the data. Thankfully, Twitter’s Standard search 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 Twitter to Looker 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 Looker.