Introduction:
Each business is different and requires specific tools, especially when it comes to IT solutions. When selecting a cloud data warehouse, many factors need to be taken into account. You have to determine whether the tools you plan to use in your company fit your existing data analytics infrastructure and what the costs are, just to name two.
Big Data and Analytics are very important for modern companies, which seek to become more data-driven. Although data-related processes can be handled on-premise, yet more and more IT organisations choose to handle them in the cloud. It ensures high scalability and flexibility and therefore answers the demand for a robust platform that can efficiently accommodate huge amounts of data. This article introduces two cloud solutions: Azure Synapse from Microsoft and BigQuery from Google.
In this article, we'll discuss Google BigQuery vs Azure Synapse to help you choose the one you need!
Google BigQuery and Microsoft Azure Synapse Analytics, two modern Cloud Data Warehouse platforms, share many features, including Columnar Storage and Massively Parallel Processing (MPP) architecture. However, each has distinct characteristics that may better suit a specific organization's data analytics infrastructure.
So let's have a look at each of these and the key difference between them
Intro to Google BigQuery
Google BigQuery offers serverless, cost-effective, and highly scalable data warehouse capabilities, along with built-in Machine Learning features. This platform supports ANSI SQL, enabling users to efficiently run SQL queries on massive datasets, facilitating tasks like managing business transactions and performing data analytics.
One of the significant advantages of Google BigQuery is its automated resource allocation process. It utilizes a columnar storage structure, which allows for seamless querying and aggregation tasks. Additionally, the platform prioritizes data security, providing identity verification and access status checks for clients.
Due to its numerous advantages, Google BigQuery is gaining popularity among businesses, with even major companies like Twitter utilizing it for forecasting package volumes across various offerings.
Key Features of Google BigQuery:
Scalable Architecture: Google BigQuery offers a petabyte scalable system, allowing users to dynamically scale their resources up or down based on demand.
Faster Processing: The scalable architecture enables Google BigQuery to process petabytes of data in remarkably less time compared to traditional systems. Users can confidently query processing analyze millions of rows without worrying about scalability issues.
Fully Managed: Being part of the Google Cloud Platform, Google BigQuery provides fully managed and serverless systems, relieving users of infrastructure management burdens.
Security: Google BigQuery places a strong emphasis on data security, ensuring protection both at rest and in transit, bolstering data integrity and privacy.
Real-time Data Ingestion: With real-time data analysis capabilities, Google BigQuery has earned recognition across IoT and Transaction platforms.
Tolerance for Errors: Google BigQuery offers the ability to replicate data across multiple zones or regions, ensuring consistent data availability even if specific regions encounter issues.
Auto-Backup: Data security is further reinforced as Google BigQuery automatically generates backup and recovery options.
BigQuery ML and Beyond:
Google BigQuery extends its capabilities through various solutions, such as BigQuery ML, which empowers users to develop machine learning models using just SQL. This integrated solution includes Data Studio for Business Intelligence and supports real-time data analysis across IoT and Transaction platforms.
BigQuery Omni takes data analysis a step further, enabling users to analyze data stored across multiple clouds using Anthos. With the addition of the service called BigLake, organizations can unify Data Warehouses and Data Lakes, benefit from uniform fine-grained access control, and enhance query performance across multi-cloud storage and open formats.
Intro to Azure Synapse
Azure Synapse is a powerful and comprehensive platform that integrates Big Data Analytics, Data Lake, Data Warehousing, and Data Integration, offering an End-to-End Analytics Solution. By running intelligent distributed queries among fault-tolerant backend nodes, it can handle petabyte-scale relational and non-relational data queries.
The architecture of Azure Synapse consists of four key components: Synapse SQL, Spark, Synapse Pipeline, and Studio. Synapse SQL facilitates SQL query execution, while Apache Spark handles batch/stream processing for Big Data. Synapse Pipeline provides ETL (Extract-Transform-Load) and Data Integration capabilities, and Synapse Studio serves as a secure collaborative cloud-based analytics platform, combining AI, ML, IoT, and BI functionalities.
Azure Synapse also offers T-SQL analytics with both 'Dedicated' and 'Serverless' SQL pools. The dedicated pool allows the implementation of Data Warehouses, while the serverless model allows for unplanned or ad-hoc workloads without the need to set up data warehouses.
The major components of Azure Synapse can be summarized as follows:
SQL Pool and SQL On-demand - Ideal for enterprise data warehousing.
Synapse Pipelines - Used for data integration, ETL, and ELT.
Apache Spark - Utilized for Big Data processing.
Synapse Design Studio - A user-friendly interface for an unparalleled user experience.
Key Features of Azure Synapse include:
Centralized Data Management: Powered by Massively Parallel Processing (MPP), enabling fast processing of large workloads.
HTAP Integration: Real-time integration with Azure Databases for seamless data processing.
Machine Learning Integration: Capabilities to predict and score ML models for generating insights within your data scope using Azure Machine Learning.
Data Exchange: Easy sharing of Data Lake and Data Warehouse with internal or external parties through Azure Data Share.
While administration might be slightly more involved than in the Google Cloud, connecting BI tools like Power BI and Machine Learning services in Azure is straightforward. Azure Synapse also offers advanced security and privacy features, such as column-level and row-level security and dynamic data masking, ensuring data protection.
In essence, Azure Synapse is an analytics service that brings together Big Data analytics and enterprise data warehousing. It offers the flexibility to query data using provisioned resources or serverless on-demand, making it suitable for ingesting, preparing, serving, and managing data for machine learning and immediate BI needs.
Key Differences between Big Query and Azure Synapse:
Pricing and Architecture:
BigQuery: It follows a serverless approach, managing all resources and automating scalability and availability. It offers on-demand and flat-rate pricing options for compute resources, where users are charged based on the amount of data processed in queries. Data storage is also billed separately.
Azure Synapse: It is not primarily a serverless data warehouse and charges for compute nodes (Data Warehouse Units or DWUs). DWUs include CPU, memory, and IOPS, but not storage. It offers various DWU options at different hourly rates. Unlike BigQuery, it does not charge per query. Data storage is billed separately.
Maintenance, Administration, and Scaling:
BigQuery: It is fully managed and handles scaling "under the hood," allowing independent scaling of compute and storage resources. Administrators have less burden in terms of scaling decisions.
Azure Synapse: Scaling a data warehouse requires administrator attention, and administrators can partition better data warehouse structures for performance optimization.
Data Protection and Security:
Both platforms support data encryption (BigQuery uses AES, and Azure Synapse uses Transparent Data Encryption or TDE) and allow administrators to manage user roles, access management and permissions.
BigQuery maintains a complete seven-day history of changes to tables, allowing administrators to revert changes without requiring a backup recovery.
Azure Synapse takes automatic snapshots of the data warehouse throughout the day, creating restore points available for seven days. Up to 42 user-defined snapshots can also be manually triggered.
Performance and Scalability:
Both BigQuery and Azure Synapse can scale up and down to handle varying workloads and perform well under heavy loads. Actual performance may vary based on specific data and query scenarios.
Data Integration and Analytics:
Both platforms integrate well with various analytical tools and machine learning platforms, allowing users to handle structured and semi-structured data types.
Compliance and Governance:
Both platforms satisfy compliance requirements for HIPAA, ISO 27001, PCI DSS, SOC 1 Type II, and SOC 2 Type II, among others.
Flexibility and User Costs:
BigQuery offers a more flexible pricing model with on-demand and flat-rate options, allowing customers to choose the most suitable billing method for their usage patterns.
Azure Synapse provides a range of DWU options, offering flexibility in compute power but may involve more complex cost management compared to BigQuery's query-based query based pricing model.
In Conclusion:
Google BigQuery can be a good choice for businesses that need a fully managed data warehouse with high scalability and performance.
Azure Synapse Analytics is a good choice for businesses that have cloud data destinations need a hybrid data warehouse with more flexibility and control.
So the best choice for you will depend on your specific needs and requirements.
Hope this helped!
