Organizations are gathering data at an all-time high right now. Therefore the demand for big data and analytics solutions has increased. Good tools and solutions that allow us to store and quickly analyze enormous volumes of data significantly impact our daily lives, ensuring that we get the most out of our data and make data-driven decisions.
Google BigQuery, a service provided by Google Cloud, offers the same functionality. Furthermore, as data increases, we want scalable and secure storage alternatives. The Google Cloud Storage service meets this requirement. This post will look into what Google BigQuery and Google Cloud Storage are. And how do they differ from one another?
What is Google Cloud Storage?
Google Cloud Storage is a service provided by Google as part of its GCP cloud computing platform. Google Cloud Platform (GCP) is a cloud computing and storage platform that allows the development, testing, and deployment of cloud-hosted applications.
It uses the same infrastructure as Google, and stability and security are two of the primary features heavily incorporated into GCP’s underlying architecture.
Because Google Cloud Storage is a paid GCP service, it offers greater functionality and independence than any other usual storage service. Google Cloud Storage can not only be used to store files but can also be integrated into an app or website. It is a server-based cloud storage solution that requires some technical knowledge. Programmers mainly use it.
What are the benefits of using Google Cloud Storage?
Here are some of the major advantages of using Google Cloud Storage:
- Simple documentation: Google Cloud Storage contains several features, and Google has provided well-organized documentation for it. As a result, it will be easy for your technical personnel to use it.
- Storage options: GCP offers several storage classes based on your requirements.
- Integration: Cloud storage is easily connected with other GCP services such as App Engine, Kubernetes, and Compute Engine.
- Cost-effective: Google Cloud Storage operates on a pay-as-you-go basis. You only pay for what you consume.
What is Google BigQuery?
Google BigQuery is a serverless and highly scalable data warehouse with a built-in query engine. BigQuery’s speed and scalability make it ideal for processing massive datasets.
SQL queries can be run on terabytes of data in seconds using the query engine. BigQuery provides this performance without requiring you to maintain infrastructure or rebuild or create indexes. It also has built-in machine learning capabilities that can assist you in better understanding your data.
With BigQuery, you can:
- Load and export data: You may easily import data into BigQuery. After BigQuery has processed your data, you may export it to study it further.
- Query and visualization: BigQuery supports interactive queries. You may also execute batch queries on your data and generate virtual tables.
- Data management: BigQuery allows you to create a list of projects, tasks, datasets, and tables. You may learn more about each of them and update or adjust your datasets as needed. BigQuery also allows you to remove and control the data that you enter.
The Google BigQuery architecture is built on Dremel, a distributed system developed by Google to query big datasets. Dremel splits query execution into slots to provide fairness when numerous users query data simultaneously. Dremel uses Jupiter, Google’s internal data center network, to access data stored on Colossus’s distributed file system.
BigQuery stores data in columnar format, which results in a high compression ratio and scan performance. BigQuery, on the other hand, may be used with data stored in other Google Cloud services such as BigTable, Cloud Storage, Cloud SQL, and Google Drive.
You can carry out large-scale analytics, and also, you can improve business decision-making based on data using Google’s BigQuery. BigQuery storage prices are based on the amount of data saved (the first 10 GB is free each month).
What are the benefits of using BigQuery?
- Machine Learning: BigQuery offers a tool called BigQuery ML, which allows users to develop, execute, and test machine learning models within the solution using ordinary SQL queries. The most frequent scenario for developing machine learning solutions with enterprise data is to export the appropriate data from our data warehouse solutions and then create the model.
- Cost-effective: Unlike other cloud-based data warehouse systems, BigQuery charges are based on consumption rather than a fixed fee, so your monthly bill reflects how much you use. You are charged for the storage you are using, and query fees are calculated on an as-needed basis based on the quantity of data processed by each query you execute.
- ETL and BI compatibility: It includes connections for ETL tools such as Informatica that enhance data using the built-in Data Transmission Service (DTS). BigQuery also natively supports the most popular business analytics/intelligence tools, such as Looker and Tableau.
Google Cloud Storage vs Bigquery
The main difference between Google Cloud Storage vs Bigquery is that BigQuery is a data warehouse like Snowflake and Redshift and Google Cloud Storage is a simple storage service like S3.
Google Cloud Storage is ideal for storing and playing huge media and other types of files and securely sharing them with those who are not part of your business. On the other hand, Big Query can be a beneficial tool for storing and querying your data.
If your company has a significant amount of data and has to handle it quickly, Google BigQuery is the strong option. You can see both services are entirely different in their functions. BigQuery allows you to query large datasets directly and run machine learning models on them. On the other hand, cloud storage is a simple object storage option to store media, text, and other files.
Final words
BigQuery is tremendously strong in taking data exploration and analysis skills from zero to hero in a matter of minutes. In a world where data capture is increasing at an alarming rate, solutions like BigQuery assist in generating value from data. And Google Cloud Storage is object storage.
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Amit Doshi is a Cloud Engineer who has experienced more than 5 years in AWS, Azure, and Google Cloud. He is an IT professional responsible for designing, implementing, managing, and maintaining cloud computing infrastructure, applications, and services.