This page provides you with instructions on how to extract data from Amazon S3 CSV and analyze it in Looker. (If the mechanics of extracting data from Amazon S3 CSV seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)
What is Amazon S3?
Amazon S3 (Simple Storage Service) provides cloud-based object storage through a web service interface. You can use S3 to store and retrieve any amount of data, at any time, from anywhere on the web. S3 objects, which may be structured in any way, are stored in resources called buckets. One common use is to store files in comma-separated values (CSV) format, in which each record consists of multiple values separated by commas.
What is Looker?
Looker is a powerful, modern business intelligence platform that has become the new standard for how modern enterprises analyze their data. From large corporations to agile startups, savvy companies can leverage Looker's analysis capabilities to monitor the health of their businesses and make more data-driven decisions.
Looker is differentiated from other BI and analysis platforms for a number of reasons. Most notable is the use of LookML, a proprietary language for describing dimensions, aggregates, calculations, and data relationships in a SQL database. LookML enables organizations to abstract the query logic behind their analyses from the content of their reports, making their analytics easy to manage, evolve, and scale.
Getting CSV data out of S3
AWS has both a REST API and command-line utilities that you can use to get at resources stored in the platform. To retrieve objects you need to know the object and host names, as well as your AWS authorization information.
Preparing CSV data
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 each table, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them.
Loading data into Looker
To perform its analyses, Looker connects to your company's database or data warehouse, where the data you want to analyze is stored. Some popular data warehouses include Amazon Redshift, Google BigQuery, and Snowflake.
Looker's documentation offers instructions on how to configure and connect your data warehouse. In most cases, it's simply a matter of creating and copying access credentials, which may include a username, password, and server information. You can then move data from your various data sources into your data warehouse for Looker to use.
Analyzing data in Looker
Once your data warehouse is connected to Looker, you can build constructs known as explores, each of which is a SQL view containing a specific set of data for analysis. An example might be "orders" or "customers."
Once you've selected any given explore, you can filter data based on any column available in the view, group data based on certain fields in the view (known as dimensions), calculate outputs such as sums and counts (known as measures), and pick a visualization type such as a bar chart, pie chart, map, or bubble chart.
Beyond this simple use case, Looker offers a broad universe of functionality that allows you to conduct analyses and share them with your organization. You can get started with this walkthrough in Looker's documentation.
From Amazon S3 CSV to your data warehouse: An easier solution
As mentioned earlier, the best practice for analyzing Amazon S3 CSV data in Looker is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Amazon S3 CSV to Redshift, Amazon S3 CSV to BigQuery, Amazon S3 CSV to Azure SQL Data Warehouse, Amazon S3 CSV to PostgreSQL, Amazon S3 CSV to Panoply, and Amazon S3 CSV to Snowflake.
Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data from Amazon S3 CSV to Looker automatically. With just a few clicks, Stitch starts extracting your Amazon S3 CSV data via the API, structuring it in a way that is optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Looker.