Today’s data analysts and administrators must keep up with more data than ever. While many cloud-based data warehouses have popped up to help solve this problem, they don’t scale quite like Google BigQuery.
What is Google BigQuery?
According to Google itself, Google BigQuery is an enterprise data warehouse that enables super-fast SQL queries using the processing power of Google’s infrastructure. Common use cases include ad hoc and trial-and-error interactive queries of large datasets for quick analysis and troubleshooting.
To better understand how Google BigQuery helps businesses, we must look at how it works and when it’s optimal to implement.
If you’re familiar with data warehouses, you’ll know that data formatting and provisioning can drain your company’s time. BigQuery does away with these time-wasting activities by providing businesses with faster data processing.
If querying speed is important to your business, then the functionality of BigQuery is a good fit for you.
How to Use Google BigQuery
Google BigQuery can be broken down into two key components:
- Dremel, the query engine
- Borg, the cluster management system
Google describes Dremel as a “cloud-powered massively parallel query service.” Dremel translates the SQL queries in your file management system into simpler instructions. Thus, every query is broken down and assigned to different contributors to process and move to the next stage for processing.
Dremel differs from similar projects in that it can natively execute queries s business users don’t 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 can take hours.
Additionally, Borg assigns server resources to jobs. For instance, if you need to process 1000 CPUs, BigQuery has no problem doing this with the Borg cluster management system. With Borg, Google easily automates the assignment process, a key advantage compared to competitor systems.
If your business is already operating smoothly with other Google Cloud Services like Google Analytics, Google Cloud Storage, and Google Suite, then BigQuery will be a strong addition to your toolbox.
Why use Google BigQuery?
Google BigQuery favors 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 its faster-than-light setup times that are not traditionally possible for enterprises. But it can’t always compare to the speeds of other databases like Amazon Redshift, which SMBs can also leverage appropriately.
If you’re curious about what Google BigQuery looks like behind the curtain, our team at Kloudio pulled out a few potential pitfalls while building the Google BigQuery integration for Kloudio. Here’s what they found.
6 Google BigQuery Pitfalls
1. You can work with either classic or standard SQL.
Google BigQuery provides two forms of SQL—classic SQL similar to that of the BigQueryWeb and standard SQL.
Thankfully, if you prefer one over the other, the tool gives you an option to choose. Some data engineers like this, but for others, not so much.
2. You can’t save your own functions.
It’s Google BigQuery’s way or the highway! There’s no easy way to go about saving your own procedures.
Don’t worry about taking the time to perfect your functions as they’ll only exist temporarily for your session. Overall, this makes BigQuery a more limited application.
3. BigQuery is upper case sensitive.
Unlike other data warehouses, Google BigQuery is case sensitive for strings, object names, and more.
You can get rid of this when using string comparison—if you use the upper function for both sides. However, object names are not case sensitive with column names.
4. You can’t add projects to the object browser.
For any project in Google BigQuery, ensure that you have named access. If not, you’ll be unable to add this to your object browser, which can significantly limit your overall convenience.
5. There are no query estimates.
We think this is one of the biggest disadvantages of using Google BigQuery. This feature (or lack thereof) means that you can’t be sure whether your complex query will work before its performed.
This can create delays as you’re running a large number of queries. Once complete, the following steps are easy; but before this is done, Google BigQuery doesn’t estimate the outcome.
6. You can’t edit existing tables.
BigQuery provides little to no way to edit existing tables, and unfortunately, you can’t add or remove columns or rename your table or other fields.
Keep this in mind to avoid the common mistake of, “I’ll fix up this table later.”
Kloudio + Google BigQuery
It may appear that we’re discouraging Google BigQuery, but that couldn’t be further from the truth. No cloud solution comes without its own challenges.
There’s are just as many benefits (if not more) to using Google BigQuery. We simply wanted to share our findings as we built our Google BigQuery integration.
The Kloudio + Google BigQuery integration includes the following functionalities:
- Automated queries: Combine the power of serverless data analytics and easy reporting for every decision-maker. Kloudio automates queries from Google BigQuery so you can unlock more business insights using your current data stack.
- Fast reporting: Free up your developers’ time to focus on writing and deploying code—not compiling reports. Kloudio’s self-service report builder helps your growing team prioritize and build scalable products.
- Bi-directional data syncing: Encourage frequent data tracking and updates by connecting and syncing your Google BigQuery data to Google Sheets or Microsoft Excel. Have data in Google Sheets you’d like to put in Google BigQuery? Kloudio enables this with just two clicks without leaving Google Sheets.
The Kloudio + Google BigQuery integration helps enterprises easily and securely generate reports from your Google BigQuery captured data using Kloudio’s self-service report builder. Enabling your data analysts to gain powerful insights and more productivity in minutes.
If you’re currently in the process of working on your first Google BigQuery project, trust the process. There are a few pitfalls to get used to—more or less related to save functions. When completed, you’ll find that the benefits of cloud data warehousing and analytical reporting far outweigh the negatives.
Check out what Google BigQuery + Kloudio integration can do for your business.