Now more than ever, data analysts and administrators are being challenged to keep up with the sheer quantity of data they are required to gather and process.
Why many new cloud-based data warehouses have become available to help solve this problem, they don’t scale quite like BigQuery.
“Google BigQuery is an enterprise data warehouse that solves this problem by enabling super-fast SQL queries using the processing power of Google’s infrastructure.”
To better understand how Google BigQuery helps businesses, we must start by looking at how it works and when it’s optimal to implement.
What’s different about BigQuery compared to other databases?
If you’re familiar with data warehouses you’ll know that formatting data and provisioning are what drains your company’s time. BigQuery does away with these time drains providing businesses with an edge over competitors that don’t process data as fast.
Can you think of industries where processing data fast could be a business advantage?
If your priority is speed of querying, then functionally BigQuery cannot be a miss. Of course, there are certain scenarios where provisioning is necessary for quality of data but it comes as the cost of time and resources.
Those that are impacted are data teams that run SQL queries regularly at the compromise of other analysis activities.
How does Google BigQuery work?
Google BigQuery can be broken down into two key components:
1. Dremel, the query engine
2. Borg, the cluster management system
Firstly, Dremel is described as a “cloud-powered massively parallel query service” by Google themselves. What Dremel does is translate the SQL queries in your file management system into simpler instructions. So every query is broken down and assigned to different contributors to process and moved to the next stage for processing.
What really differs Dremel from similar projects is that it can execute queries natively. So business users wouldn’t need to need to convert them into MapReduce jobs. For the business user, Dremel can run through read-only data and generate user-friendly results in seconds rather than MapReduce which could take hours.
The second component is Borg, it assigns server resources to jobs. For instance, if you need to process 1,000 CPUs, BigQuery will have no problem doing this with Borg the cluster management system. Google has made it easy by automating the assignment process with Borg making it a key advantage to competitor systems.
If your business is operating smoothly using other Google Cloud Services like Analytics, Cloud Storage and Sheets then BigQuery will be a strong addition to the puzzle.
When is using Google BigQuery for business a good idea?
Google BigQuery provides advantages that favours large enterprises. Because the database is designed based on abstraction for ease of user experience, small to medium size businesses (SMBs) can lose out on overlooked data analysis.
Where BigQuery wins is the faster than light setup times that traditionally wouldn’t be possible for enterprises. But it certainly can’t compare to the speeds of other databases in Amazon Redshift for which SMBs can also leverage appropriately.
With all this said, Google does a great job of identifying use cases for BigQuery. According to a Google Program Manage, BigQuery ideal for businesses running ad hoc queries across extremely large datasets.
Do you currently use Google BigQuery? Check out what Google BigQuery + Kloudio integration can do for your business.