Why is BigQuery essential today?

If you read our blog post on data lakes, some questions may have popped up in your mind. To refresh this topic:  a data lake is not your next dream destination. 

Let’s make it clear, it can be your data dream destination, but we won’t get carried away. It is BigQuery essential more of cloud storage that allows you to store your data in a standardized format. 
Remember the next step? Load it on a data warehouse. To keep it simple, it consists of data libraries. 

But what is the link between data lakes and BigQuery? What is it? Why does it stand out from the other analytics tools? When to use BigQuery? How does it work? So many questions that require clarifications.  In this article, you better understand BigQuery and its added value … not forgetting the dream destination for your data. 

What is Google BigQuery?

You want more business insights for your company but are left speechless when you listen to your IT team? BigQuery is what you need. 
BigQuery is Google’s serverless data warehouse. Why “serverless”? Because BigQuery does not depend on a server, which means that operations are faster. Google fully manages this warehouse so that you can focus only on the analysis of data and forget about coding tutorials. 

How to know if this tool is made for you?

BigQuery is directed at big companies that have to deal with large amounts of data.
For small companies, spreadsheets can be enough to classify and analyze small amounts of data manually. 

If you are a big company and do not use BigQuery yet, you might be facing some issues with the load time and analysis of your data that can take a while. Well, implementing BigQuery will allow you to load data at a large scale so that you can analyze it quicker. It becomes essential for businesses who struggle with those delays (with yours). 

How does it work?

Google’s warehouse is structured with SQL (Structured Query Language), a standard language used to analyze structured data and access to workable information thanks to querying. 

What does it mean? 
When you load a dataset on this server, you do it to query your data. The objective is to have a better understanding of your performance, your clients or even your website. But because some illustrations are worth thousands of words, you may prefer to see what BigQuery really looks like. When you load a dataset from your data lake on BigQuery this is what it looks like.

Once the data is loaded, you can start querying your dataset. For instance, you can create a query to census all the visitors of your website that leave without engaging with your business.

Here for instance you can see a column of 0. It means that the visitors did not bounce. A “1” would have been synonymous with one bounce.

Finally, you are able to classify the users according to the fact that they left your website without engaging or that they engage. A letter -attributed to them- corresponds to the likelihood of their interest in your business. 

Thanks to BigQuery, you can give helpful information to your marketing team so that they can target visitors that are the most likely to convert into customers. 

What can you do with BigQuery?

Google’s warehouse takes care of the process that goes from the storing of your data to querying by way of ingestion. 

• Storage: all you need to do to store your data on BigQuery is to log into your account from your browser and get started. In BigQuery, data is stored in a structured table. One of the most amazing (if you please!) advantages of using a data warehouse is that, unlike data lakes that only constitute storage, this tool helps you structure your data, making the analysis easier. 
  For instance, if you have 200 stores across your country and want to analyze their performance, you can divide them according to their region, size or products, to have a more precise analysis. If you have thousands of tables to analyze, you’ll be glad to have them organized. 
  
  
• Ingestion: because BigQuery is an analytics tool from Google, it can quickly ingest data from Cloud Storage and Cloud Dataflow. 
  
• Querying: once the platform has ingested your data, you can start querying. BigQuery offers you easy access to your tables of dataset. As said before, this tool supports SQL (Structured Query Language). Without further complexification detailing the difference between interactive queries and batch queries, you may be interested in knowing more about queries pricing. There are two types of pricing: 
  
  1. On-demand query pricing. If you choose this pricing model, you will be charged $5.00 per TB (the first TB per month is free). The more queries, the more you pay.
  
  2.Flat-rate pricing. Some people prefer flat-rate pricing as it allows you to pay for a specific capacity and to manage your budget accordingly.

You can choose to analyze BigQuery public datasets to make comparisons, for example. Those tables are free to use and hosted by Google. They are available for everyone and will make you skip the two first steps. But, if you want to analyze your dataset, you can also try BigQuery for free since the first 10GB of storage are free of charge to learn how to use these analytics and choose whether or not to go further in spendings. 

How is Google Cloud Storage linked with BigQuery?

Google Cloud Storage is Google’s data lake. It allows businesses to store large amounts of data that then can be analyzed thanks to BigQuery. Because Google conceived both tools, the bridges between the two make integration easier and faster. 
BigQuery will allow auto-integration with Google Cloud Storage thanks to its machine learning tool: a way to prevent bugs and save precious time. You can choose another warehouse such as Azure, but the integration will not be as easy as with BigQuery, which works directly from your Cloud Storage. 

Why should you use it?

Now you master the fundamentals of this tool, you may want to know if BigQuery is for you. After all, it’s an additional expense for your company and knowing how useful it can be is never too much to ask. 

• BigQuery allows fast insights into your business thanks to its quickness to load and start to query your datasets. 
  
  
• Using a data warehouse is more interesting than using a Customer Data Platform (CDP) when you analyze more than just your customer data. Querying large amounts of data offers you a complete insight into your business. 
  
  
• It allows data scientists to forecast performance and to help the board adapt their business plan.  
  
  

Bigquery:
an essential working tool

Congratulations! You have made it to the end of this article. 
Focusing on analytics and not on data infrastructure. This is what BigQuery promises you. This working tool helps you save time and get better insights into your data.  

Using BigQuery requires specific knowledge since it uses SQL and necessitates choosing the proper settings for an interesting analysis. If you want to be guided or driven on implementing or tracking your activities, Better&Stronger can help. Give us a call and we'd be glad to discuss it!