Build your Analytics system on Google BigQuery
Updated: Feb 7
Google BigQuery is a Cloud Warehouse that enables Big Data analysis. SMEs will get a lot of benefits at a low cost from Google to start their data-driven journey, so should you build your Analytics capacity on BigQuery?
If you have a lot of data and need to explore them to answer complex business questions, BigQuery is a powerful solution that can provide you with the ability to deep-dive from petabytes of data and find hidden insights to improve customer experiences and drive competitive advantage. Google BigQuery is not the only choice, and there are also free & popular open-source options out there. However, BigQuery is getting more & more of the Data Platform market. Let's explore why small businesses use it, and should you consider BigQuery when building your Analytics process?
Table of content
1. What is an Analytics platform?
A Data Analytics platform, sometimes known as a Business Intelligence (BI) platform, is an ecosystem of services and technologies used to analyze voluminous, complex and dynamic data. It allows you to retrieve, combine, interact with, explore, and visualize data from various sources a company may have. Its objective is to turn every kind of data into actionable insights for real business outcomes.
A modern data stack will consist of 3 key functions:
Data warehouse to store the data.
A systematic way to clean, transform & analyze data, usually with SQL query.
A visualization tool to present the results.
Out of the 3, building a data warehouse requires the most engineering resource. However, modern cloud solutions like AWS (Amazon Web Service) & GCP (Google Cloud Platform) have simplified the process. There are also a lot of easy-to-use data visualization tools to choose from. Most of the time, data analysts & data engineers would spend on ETL & SQL analysis tasks, so choosing the right Analysis platform is the main factor for your Analytics stack.
2. What is Google BigQuery?
Google BigQuery is a fully managed and serverless data warehouse solution available in the Google Cloud Platform that allows anyone to analyze terabytes of data in a matter of seconds. It has advanced functionality and pre-built features like machine learning, geospatial analysis, and business intelligence tooling.
Now, let's break it down to see what BigQuery has to offer to SMEs:
Fully managed: This means businesses will need to do it themselves, with little support from Google. The documentation from Google is technical-heavy and not practical, but there are also many video tutorials on Youtube & a big community out there that can help you.
Serverless data warehouse: It's on Cloud, so beware of any data privacy & protection act in your country. With their auto-scaling model, you won't need to worry about the limitation of your analytics capacity.
Pre-built features: ML/AI, or compute engine...BigQuery is part of Google Cloud, so you can one-click enable the feature & transfer your data to the specific services. If you have data scientists & engineering resources, then the Data team can start right away with minimal environment configuration.
Analyze terabytes of data in a matter of seconds: this is the greatest thing when you're considering a Data Warehouse for Analytics. Google BigQuery manage to perform this task by breakdown your query into smaller batches and executing them on multiple machines.
Basically speaking, using BigQuery would fill 2 out of 3 functions in your Data Analytics platform.
3. The Advantage of Google Big Query
Quickly handle complex queries
We would dig deep into why Google BigQuery is fast, because explaining it will take a whole article. With Google's huge advantage in infrastructure resources, Big Query can utilize a powerful network of servers & storage. One query can be chopped down & performed on 100 nodes, so an hour of query on a single node can take 10 seconds.
Pay as you go
The more lean your data processing is, the more you can benefit from Google BigQuery. Google charges you for data managed & compute at an hourly rate. This means you pay nothing if no one is working on your account, and only pay for the data you store. The rate is very economical, for example, Google Cloud storage price is one of the cheapest in the Cloud Hosting market, at $0.02 per GB per month & the cost of data computing is $5 per Terabyte.
The overall Google Cloud Platform is not intuitive enough for business users, but an IT guy can easily set up an account after viewing several video tutorials. The process is much quicker than the time to build a Data Warehouse yourself.
Any analysis result can be directly transferred to Google Data Studio (now Looker Studio) for chart building. It's a very straightforward, one-click experience. By pairing with BigQuery, Looker Studio's abilities are enhanced ten folds. Remember that every time you view or filter the report, the query is executed & you get charged for the data amount computed.
4. Any reason BigQuery is not an ideal choice for Analytics
You need SQL capacity
All of the BigQuery analysis jobs are code-based, so even short-term usage will require some basic SQL query knowledge. Your organization will need to allocate an engineer occasionally to execute the data tasks when the business team has new reporting requests.
BigQuery is for Big & Complex Data
BigQuery is designed to handle complex and massive amounts of data at super-fast speeds. Therefore, BigQuery may not be the right solution for businesses and simple data sets, as you truly waste the potential of BigQuery and, more importantly, your money.
BigQuery works great on data sets larger than 100 million rows, where the 'initialization time' doesn't come into effect (or is negligible compared to the rest of the query).
However, if the data sets are under that number, the performance is on par with any Data Analytics solution out there, and you will not see any significant difference. You can use any other interactive BI tool for a quick analysis.
Lack of Customer Service
Fully managed in BigQuery means nothing needs to be installed, provisioned, or set up. You start the analysis by uploading data and creating the queries. This also means that users have to operate the analysis process themselves without any support.
BigQuery is a non-relational database
The disadvantage of a non-relational database is your raw data would be messy. BigQuery can store a wide variety of data structures but lacks standardization. This means you will need to spend a lot of time preparing data.
Did you know? Many Analytics platforms provide the ability to connect relationships between tables & analysts can use SQL or chart builders to freely explore cross-table information. In Rocket.BI, you can connect relational datasets at the Warehouse-level or dashboard level.
BigQuery belongs to the Google Cloud ecosystem
Support for 3rd parties outside the Google Cloud Platform ecosystem is often lacking or poorly documented. If you are using another Cloud Warehouse and want to utilize the analysis power from BigQuery, you need to pay double the costs and resources to store data in two places.
Google BigQuery is a great Database-as-a-Service (DBaaS) solution for cloud-native businesses and anyone working with machine learning application development or handling massive sets. To work more productively when analyzing data, you must take into account some of the problems mentioned above.