The Future of Data Warehousing: Trends in BigQuery, Snowflake, and Autonomous Databases

The Future of Data Warehousing: Trends in BigQuery, Snowflake, and Autonomous Databases

Every firm is now a data company, and as more users within these companies find new applications for previously unusable data, the infrastructure and tools already in place can fulfill this need and generate new demands. The data warehouse, the cornerstone of any modern data stack, sits at the center of it all. This article examines these technologies’ likely future developments and how data warehousing might be impacted.

Which is superior, Snowflake vs BigQuery

Let’s examine the properties of BigQuery vs Snowflake to determine which is superior.

  • Pricing

Snowflake offers a time-based pricing approach for computing resources, where customers are charged according to the amount of execution time. While BigQuery employs a query-based pricing model, where customers pay for data to compute the resources. Storage using BigQuery is less expensive than storage with Snowflake.

  • Architecture

Snowflake’s architecture combines a shared-nothing database with a conventional shared-disk design. Additionally, Snowflake uses the central data repository from all the compute nodes for persisted data. Like shared-nothing architectures, Snowflake does all query processing by computing the clusters in massively parallel processing (MPP).

  • Performance

Under various loads, BigQuery and Snowflake both function well. The benchmarks must be done using the user’s data. They can also be utilized to efficiently manage the workloads of numerous businesses. BigQuery lost to Snowflake in raw speed, 10.74 seconds on average. BigQuery clocked in at 14.32 seconds per query, a known fact. Snowflake’s performance is superior to BigQuery’s, according to independent third-party benchmarks.

  • Effortless Use

Regarding ease of usage, BigQuery and Snowflake are both user-friendly. On the G2 website that rates business software, Snowflake has a 9.2 rating.

  • Scalability
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Snowflake frequently enables users to scale their storage and computing resources separately. To reduce query times, it has automatic performance optimization and workload monitoring. In contrast, Bigquey fully manages scalability issues from the inside out. BigQuery can automatically provide the compute resources. It makes processing the data in a matter of minutes relatively simple.

  • Security

\AES encryption is used on the data by BigQuery and Snowflake to support customer-managed keys. They further rely on the roles to grant access to the resources. Frequently, Snowflake supports federated user access via Microsoft Active Directory (ADFS) and providers that comply with SAML 2.0. Active Directory is another method used by BigQuery to support federated user access. Granular permissions for views, schemas, procedures, tables, and other objects are provided by Snowflake.

  • Management and Upkeep

Because automatic administration occurs in the background, BigQuery and Snowflake require little upkeep. This may suggest, in Snowflake, that queries are occasionally tweaked and optimized in the background. Customers of Bigquery are seldom aware of these factors because of the serverless nature of the platform.

The Evolution of Independent Databases

Autonomous databases have received a lot of interest recently because of their potential to reduce administrative burdens and increase productivity. By automating mundane processes like patching, tuning, and backups using these databases’ automation and machine learning, database managers may focus on strategic goals rather than laborious tasks.

Future development of autonomous databases is predicted to be even more rapid. They’ll presumably have advanced analytics capabilities, enabling predictive and real-time insights. They may also connect to external data sources and APIs, facilitating smooth data fusion and enabling businesses to utilize a wider range of data.


Trends of data warehousing in 2023

  • The open database has great value because its size has increased by a factor of roughly two during the past few years.
  • Estimates of what makes a very large database continue to increase as database sizes increase.
  • A significant amount of data cannot be kept online with the gear and software that are now available. For instance, 10TB of data, or around one month’s worth of records, must be stored online for a Telco call record. The size will exceed 100 TB if it’s necessary to maintain track of sales, marketing, customers, staff, etc.
  • Both textual information and some multimedia data are present in the record. As text data, multimedia data is more difficult to handle. While textual data may be retrieved by relational software today, searching multimedia data is not simple.
  • In addition to scale planning, it isn’t easy to create and maintain data warehouse systems that are getting bigger and bigger. The size of the data warehouse grows along with the number of users. Additionally, these users will need access to the system.


BigQuery, Snowflake, and autonomous databases are paving the way for a dynamic and exciting future in data warehousing. Through further development, these technologies will provide capabilities that improve performance, scalability, and security. The data warehousing industry will adapt with cutting-edge capabilities and ground-breaking solutions as businesses seek more real-time insights, automated processes, and seamless integration. Businesses should closely monitor these trends to stay ahead in this always-changing industry and use the strength of these technologies to fully realize the potential of their data.

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Shankar is a tech blogger who occasionally enjoys penning historical fiction. With over a thousand articles written on tech, business, finance, marketing, mobile, social media, cloud storage, software, and general topics, he has been creating material for the past eight years.