Are you stuck in a conundrum trying to decide between BigQuery vs. Bigtable? Well, you’re not alone. These two services may share the “Big” in their names, but they cater to very different needs in the vast realm of big data. Let’s dive in and explore the nuances to help you make an informed decision.
Big data in a nutshell
Data is the new gold – at least for all companies and organisations with views to expand. A business that is aware of the power hidden inside data carries out process analysis and introduces data-driven changes and improvements.
Some go even further – with the technology at their disposal they predict trends, probable market changes and the potential consequences of business decisions they have yet to make.
As your business grows, so do the swathes of data you collect – gigabytes turn into terabytes and petabytes. Your “gold mine” of large datasets could encompass:
- transactional data
- online transactional processing data stored in online analytical processing tools
- financial data
- aggregated historical data
- user queries, and more.
To keep costs and the time it takes to generate a report at a reasonable level, you need the right tools. Among Google Cloud solutions, two are the most common: BigQuery and Bigtable. Both are available within the Google Cloud platform. They can both handle big data efficiently, but they have been designed for somewhat different use cases.
Let’s take a closer look at what each of them does best and the key differences.
What is Google’s BigQuery?
It’s a serverless, scalable cloud data warehouse service. It allows you to service millions of queries and perform advanced analytics. It works seamlessly on petabytes of data in SQL (Structured Query Language). You don’t need to worry about costly maintenance of advanced infrastructure, scaling up or down and balancing traffic.
BigQuery is one of the services available on Google Cloud. Your datasets are stored and processed on a stable, secure and scalable Google Cloud. Here you can find out what is Google Cloud and how it can support your business.
Using BigQuery you can create your own data warehouse, which can track progress and changes within and outside the company. You can also take advantage of the built-in Machine Learning models within BigQuery. ML tools offer a range of predictive and analytics tools for different business scenarios. Let’s look at the specifics.
Analytical maverick
BigQuery reigns supreme as the go-to enterprise data warehouse for interactive analysis of structured data. It’s tailor-made for handling large-scale SQL-based queries and reporting, making it indispensable for organizations seeking actionable insights, including predictive analytics and machine learning applications.
It’s very efficient when you need to scan through an entire database to find the answer to your question. For example: a sum of all online revenue, the average time spent on a website etc. The most common use of BigQuery is when your datasets change mostly by appending and adding latest data to well structured large datasets.
Unleashing the power of SQL
BigQuery is a wizard when it comes to SQL-based analytics. It’s optimal for complex analytical interactive querying. Its petabyte-scale data warehouse is engineered to ingest, store, and analyze data with unmatched efficiency.
BigQuery is Google Cloud equivalent to other data warehouse solutions and SQL databases offered by major public cloud providers, like Microsoft Azure SQL data warehouse and AWS’s Redshift.
Whether you’re crunching numbers for business intelligence or delving into data analytics, BigQuery empowers you to extract valuable insights from your vast data repositories effortlessly.
Bigtable – The heavy lifter
Cloud Bigtable is based on the Google File System and it’s one of the most efficient semi-structured and unstructured data storage systems. It’s optimized for large-scale data storage and retrieval. It organizes data into scalable tables, allows for data changes and key-based searches.
Fast and furious
Bigtable is the powerhouse of NoSQL wide-column databases. It’s ideal for lightning-fast performance, handling massive datasets and volumes of reads and writes with ease.
Cloud Bigtable isn’t just your average NoSQL database service. It’s a powerhouse designed to fuel applications that demand scalability and performance. It’s particularly useful for huge quantities of single key data values. With its ability to handle billions of rows and thousands of columns, Bigtable is the perfect choice for massive read and write operations.
Bear in mind that Bigtable is not a relational database and does not support SQL queries. Neither does it support multi-row transactions.
Where is it most useful?
If you’re in industries like IoT, AdTech, or FinTech, where speed and scale are paramount, Bigtable could be your best choice. Think of it as the engine behind applications that demand high throughput and low latency. This way you can ensure your operations run smoothly even under heavy loads.
Also, it integrates seamlessly with big data tools like Hadoop and Dataflow. It’s one of the reasons that makes it a favorite among developers and data engineers alike.
However, we do not recommend it if you want to perform advanced analytical queries. Nor is it efficient in cases of small amounts of data.
Cost effectiveness
If you are still unsure which one is your best bet in the BigQuery vs Bigtable competition, let’s take a look at costs.
BigQuery charges are based on the amount of the data stored in tables and the data processed by queries. Google Cloud offers a flexible, pay-as-you-go pricing model. You are charged for resources consumed during every query and for storage capacity. It’s a good solution for companies that run complex SQL queries on an ad-hoc basis.
Bigtable charges are based on the volume of data stored and accessed. Cloud Bigtable charges users on the provisioned capacity, which includes the number of nodes and the amount of storage allocated.
Unlike BigQuery’s on-demand pricing, Bigtable users commit to a certain level of provisioned capacity, which makes it ideal for running apps with consistent performance and high throughput. It is a more economical option if you need to store data long-term. It is also more cost-effective if your data requires frequent access.
Common Ground – SLAs and Scalability
Both BigQuery and Bigtable share some common traits that set them apart in the cloud ecosystem. With industry-leading SLAs, unlimited scalability, and automatic failover mechanisms, they offer peace of mind in terms of reliability and performance.
Plus, their cloud-native architecture ensures seamless updates and maintenance, eliminating the headache of downtime and maintenance windows.
Bigtable vs BigQuery – Choose the right warehouse
In essence, choosing between BigQuery vs. Bigtable boils down to your specific use case and requirements. You could think of Bigtable as the fast and versatile tool and BigQuery as the perfect setup for in-depth predictive analytics and machine learning.
If you need to choose, focus on what type of data you work with and the speed you need to achieve:
- If you’re looking to analyze structured data and derive actionable insights, BigQuery is your best bet.
- On the other hand, if you need a high-performance database for real-time applications, unstructured data and heavy workloads, Bigtable has got you covered.
To simplify matters, think of BigQuery as the equivalent of a spreadsheet with all the functions and analytical tools to gain insight. Meanwhile, Cloud Bigtable will be the equivalent of a mobile app that allows you to access and read data very fast in real-time.
Now that you know all this, it’s time to assess your needs, weigh the pros and cons, and make the decision that aligns with your business goals and objectives. After all, in the world of big data, choosing the right tool can make all the difference in driving success and staying ahead of the curve.
If you need help deciding what type of warehouse is best for your business, ask our engineers.
