This post will help you with export your Google Sheets to Amazon Redshift. If you think this needs time, you may use the Google Sheets Connector for Redshift from Blendo. With a few clicks, you will start collecting analytics-ready data, consistently into your Redshift instance. No need for scripts or engineering effort and resources, just replicate your data and focus on what matters – the analysis of your data.
Access your data on Google Sheets
The first step in loading your Google Sheets data to any kind of data warehouse solution is to access your data and start extracting it.
Google Sheets offers a REST API that can be used to programmatically interact with your account. Due to the nature of the application, there is no specific set of tables that are being extracted but instead each sheet of each spreadsheet is represented as a separate table.
In addition to the above, the things that you have to keep in mind when dealing with the Google Sheets API, are:
- Rate limits. Depending on the API version that is being used, Google Sheets API has rate limits per project and per user.
- Authentication. You authenticate on Google Sheets using either OAuth or the application’s API key.
- Paging and dealing with a big amount of data. Platforms like Google Sheets that are dealing with clickstream data tend to generate a lot of data, like events on your web properties.
About Google Sheets
Google Sheets is a free web-based spreadsheet software that is offered by Google as part of the Google Drive services.
Google Sheets allows users to create and modify spreadsheet files online while collaborating with others in real-time. For this, it is widely used by various businesses in order to maintain data consistency across departments and to ensure that every member of their team is on the same page.
As with any other spreadsheet app, the data included in the sheets can be of various types, from raw data to aggregated reports.
Transform and prepare your Google Sheets Data for Amazon Redshift Replication
After you have accessed your data on Google Sheets, you will have to transform it based on two main factors,
- The limitations of the database that the data will be loaded onto
- The type of analysis that you plan to perform
Each system has specific limitations on the data types and data structures that it supports. If for example, you want to push data into Google BigQuery, then you can send nested data like JSON directly, but keep in mind that the data you get from Google Sheets are in the form of a tabular report just like a CSV.
Of course, when you are dealing with tabular data stores, like Microsoft SQL Server, this is not an option. Instead, you will have to flatten out your data, just as in the case of JSON, before loading into the database.
Also, you have to choose the right data types. Again, depending on the system that you will send the data to and the data types that the API exposes to you, you will have to make the right choices. These choices are important because they can limit the expressivity of your queries and limit your analysts on what they can do directly out of the database. Google Sheets has a very limited set of available data types which means that your work to do these mappings is much easier and straightforward, but nonetheless equally important with any other case of a data source.
Amazon Redshift is built around industry-standard SQL with added functionality to manage very large data sets and high-performance analysis. So, in order to load your data into it, you will have to follow its data model which is a typical relational database model. The data you extract from your data source should be mapped into tables and columns. Where you can consider the table as a map to the resource you want to store and columns the attributes of that resource.
Also, each attribute should adhere to the data types that are supported by Redshift.
As your data is probably coming in a representation like JSON that supports a much smaller range of data types you have to be really careful about what data you feed into Redshift and make sure that you have mapped your types into one of the datatypes that are supported by Redshift.
Designing a Schema for Redshift and mapping the data from your data source to it is a process that you should take seriously as it can both affect the performance of your cluster and the questions that you can answer. It’s always a good idea to have in your mind the best practices that Amazon has published regarding the design of a Redshift database. When you have concluded on the design of your database you need to load your data on one of the data sources that are supported as input by Redshift, these are the following:
Export data from Google Sheets to Redshift
To upload your data to Amazon S3 you will have to use the AWS REST API, as we see again APIs play an important role in both the extraction but also the loading of data into our data warehouse. The first task that you have to perform is to create a bucket, you do that by executing an HTTP PUT on the Amazon AWS REST API endpoints for S3.
You can do this by using a tool like CURL or Postman. Or use the libraries provided by Amazon for your favorite language. You can find more information by reading the API reference for the Bucket operations on Amazon AWS documentation.
After you have created your bucket you can start sending your data to Amazon S3, using again the same AWS REST API but by using the endpoints for Object operations. As in the Bucket case you can either access the HTTP endpoints directly or use the library of your preference.
Amazon Redshift supports two methods for loading data into it. The first one is by invoking an INSERT command. You can connect to your Amazon Redshift instance with your client, using either a JDBC or ODBC connection and then you perform an INSERT command for your data.
The way you invoke the INSERT command is the same as you would do with any other SQL database, for more information you can check the INSERT examples page on the Amazon Redshift documentation.
Redshift is not designed for INSERT like operations, on the contrary, the most efficient way of loading data into it is by doing bulk uploads using a COPY command.
You can perform a COPY command for data that lives as flat files on S3 or from an Amazon DynamoDB table. When you perform COPY commands, Redshift is able to read multiple files simultaneously and it automatically distributes the workload to the cluster nodes and performs the load in parallel.
The best way to load data from Google Sheets to Amazon Redshift
So far we just scraped the surface of what can be done with Amazon Redshift and how to ingest data into it. The way to proceed relies heavily on the data you want to load, from which service they are coming from and the requirements of your use case. Things can get even more complicated if you want to integrate data coming from different sources.
A possible alternative, instead of writing, hosting and maintaining a flexible data infrastructure, is to use a product like Blendo that can handle this kind of problems automatically for you.
Easily use the Google Sheets connector from Blendo, along with multiple sources or services like databases, CRM, email campaigns, analytics and more. Quickly and safely ingest Sheets data into Amazon Redshift and start generating insights from your data.