Google BigQuery and Microsoft Azure Synapse are two famous Cloud Data Warehouse platforms that share common features, including Columnar Storage and MPP(Massively Parallel Processing) architecture. Here, we will see a detailed comparison between BigQuery and Synapse.
What is BigQuery?
BigQuery is a fully managed and serverless data warehouse service by Google that allows you to collect data on a large scale. It offers tools like business intelligence, machine learning, and geospatial analysis. BigQuery enables you to utilize SQL queries to solve your organization’s most pressing problems. You can query terabytes of data in seconds.
BigQuery separates compute and storage capacity. You can save and analyze your data in BigQuery, or analyze data stored externally. You can access BigQuery from the GCP console and the BigQuery command line. And you may utilize client libraries with known programming languages like Go, JS, Python, and Java. Further, you can upload petabytes of data in BigQuery machine learning to comprehend and extract information from it.
Advantages of Using BigQuery
BigQuery is a serverless offering. So you do not need infrastructure management. You can upload your data and get started. You can scale up and down your system based on demand.
Analytics in real-time
You can enter your most recent data and immediately examine it. This function is valuable to your organization because it assists you in comprehending your data as you compile it. BigQuery Machine Learning also supports it with a fast streaming insertion API.
Data backup and restoration
Your data is critical to your success. BigQuery automatically replicates and stores your data. It records changes for seven days. So you can recover earlier data and compare data from different points in time.
Easy to set up and use
Setting up a data center is costly, time-consuming, and difficult to manage. It frustrates you and might even waste your time while analyzing the data. BigQuery simplifies the procedure. You enter your data into the program and pay for just what you use. It’s an effective solution to help you analyze and interpret your data without the hassle of creating your own data center.
Next, you don’t want to spend days setting a tool to analyze data while you’re busy operating your business. BigQuery is simple and quick to set up. You may begin querying your data immediately after setting the data warehouse, which is a matter of minutes. You can analyze petabytes of data and billions of data rows in seconds. Everything will be done in real-time.
What is Azure Synapse?
Azure Synapse Analytics is an analytics platform that incorporates a data warehouse, integration, big data, and data visualization. Azure Synapse Analytics enables users of all skill levels to have access to instant insights into their data.
There is a dedicated pool of SQL Servers called Synapse SQL. It offers the infrastructure required for constructing a data warehouse and allows to running of T-SQL queries. It also empowers with the serverless approach for unplanned or ad-hoc workloads, allowing them to gain insights from their data repositories without going through the proper steps of building a data warehouse. In addition, synapse Pipelines for ETL and data integration from diverse sources can be used to churn data for analytics. Including big-data computation services like HDInsight for Hadoop and DataBricks makes this a more powerful ETL tool.
Advantages of using Azure Synapse
You can scale up Azure Synapse quickly and add different data resources. This helps you to handle any amount of data.
You can extract information from your data using machine learning models within Azure Synapse.
A single platform for all data work
You may significantly cut project development time with a single experience in designing end-to-end analytics solutions.
Power BI Integration
Power BI can be directly integrated with Azure Synapse. As a result, you can generate and access data reports anytime. In addition, you have all the standard data interfaces, and because SQL Serverless appears to be any ordinary SQL database, you can simply execute complex analytical queries during import.
Difference between Google BigQuery and Azure Synapse
In Google BigQuery, you do not need to worry about the infrastructure; it is handled by Google in terms of resource management, security, availability, and scalability, eliminating the need for a workforce to a certain level.
BigQuery has two price categories. Its on-demand solution for computational resources is based on the query. For the quantity of data scanned by their searches, users are charged $5 per terabyte of data processed. Instead of paying for individual inquiries, users can choose a flat-rate option enabling them to buy specialized query processing capabilities. The yearly package starts at 8,500 USD/ month, including 500 “flex slots,” which are dedicated query processing capacity commitments of 60 seconds. In addition, BigQuery charges 20 USD/terabyte of data storage every month.
Microsoft Azure Synapse Analytics charges for compute nodes known as Data warehouse Units (DWU). Everything from CPU, RAM, and IOPS are included in DWUs, except the storage. Microsoft offers a variety of DWUs with hourly fees ranging from $1.20 to $360. The cost of data storage is 122.88 USD/terabyte per month.
Keep in mind that cloud providers change their prices frequently
Both platforms have AES for data encryption in rest, customer-managed keys support, and role-based resource access. Google BigQuery has default encryption enabled but not in Azure Synapse Analytics. Both platforms give network security through VPC and Virtual networks.
Google BigQuery retains a complete seven-day history of table modifications. Therefore, you can reverse changes without requesting a backup recovery. Synapse takes snapshots of the data warehouse throughout the day to build restoration points accessible for seven days.
The administration handles roles, permissions, and data security. Google BigQuery needs less time than Azure Synapse in the administration process. BigQuery is a serverless offering, and compute, and storage capacity are independently scaled. Scaling an Azure Synapse needs administrator involvement, whereas other Azure services may be configured to autoscale. Administrators can also divide data structures and optimize efficiency in various ways.
When it comes to performance, Google BigQuery and Azure Synapse both Analytics perform really well under varying load levels due to their ability to scale up and down.
Both Azure Synapse and Google BigQuery offer similar features like data analysis, machine learning, data reports, etc. Choosing one requires identifying which option best fits your data strategy. Like other cloud data warehouse systems, Google BigQuery and Azure Synapse Analytics provide free trials and proof-of-concept assistance to let organizations gain personal experience with how their solutions deliver value. You can test both of them and choose the best one per your requirements.
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.