Snowflake vs Teradata comparison

Snowflake is a cloud-based data warehousing solution for storing and processing data, generating reports and dashboards, and as a BI reporting source. It is used for optimizing costs and using financial data, as well as for migrating data from on-premises to the cloud. The solution is often used as a centralized data warehouse, combining data from multiple sources.

Snowflake has helped organizations improve query performance, store and process JSON and XML, consolidate multiple databases into one unified table, power company-wide dashboards, increase productivity, reduce processing time, and have easy maintenance with good technical support.

Its platform is made up of three components:

1. Cloud services - Snowflake uses ANSI SQL to empower users to optimize their data and manage their infrastructure, while Snowflake handles the security and encryption of stored data.
2. Query processing - Snowflake's compute layer is made up of virtual cloud data warehouses that let you analyze data through requests. Each of the warehouses does not compete for computing resources, nor do they affect the performance of each other.
3. Database storage - Snowflake automatically manages all parts of the data storage process, including file size, compression, organization, structure, metadata, and statistics.

Snowflake has many valuable vital features. Some of the most useful ones include:

• Snowflake architecture provides nearly unlimited scalability and high speed because it uses a single elastic performance engine. The solution also supports unlimited concurrent users and workloads, from interactive to batch.
• Snowflake makes automation easy and enables enterprises to automate data management, security, governance, availability, and data resiliency.
• With seamless cross-cloud and cross-region connections, Snowflake eliminates ETL and data silos. Anyone who needs access to shared secure data can get a single copy via the data cloud. In addition, Snowflake makes remote collaboration and decision-making fast and easy via a single shared data source.
• Snowflake’s Data Marketplace offers third-party data, which allows you to connect with Snowflake customers to extend workflows with data services and third-party applications.

There are many benefits to implementing Snowflake. It helps optimize costs, reduce downtime, improve operational efficiency, and automate data replication for fast recovery, and it is built for high reliability and availability.

Below are quotes from interviews we conducted with users currently using the Snowflake solution:

Sreenivasan R., Director of Data Architecture and Engineering at Decision Minds, says, "Data sharing is a good feature. It is a majorly used feature. The elastic computing is another big feature. Separating computing and storage gives you flexibility. It doesn't require much DBA involvement because it doesn't need any performance tuning. We are not doing any performance tuning, and the entire burden of performance and SQL tuning is on Snowflake. Its usability is very good. I don't need to ramp up any user, and its onboarding is easier. You just onboard the user, and you are done with it. There are simple SQL and UI, and people are able to use this solution easily. Ease of use is a big thing in Snowflake."

A director of business operations at a logistics company mentions, "It requires no maintenance on our part. They handle all that. The speed is phenomenal. The pricing isn't really anything more than what you would be paying for a SQL server license or another tool to execute the same thing. We have zero maintenance on our side to do anything and the speed at which it performs queries and loads the data is amazing. It handles unstructured data extremely well, too. So, if the data is in a JSON array or an XML, it handles that super well."

A Solution Architect at a wholesaler/distributor comments, "The ability to share the data and the ability to scale up and down easily are the most valuable features. The concept of data sharing and data plumbing made it very easy to provide and share data. The ability to refresh your Dev or QA just by doing a clone is also valuable. It has the dynamic scale up and scale down feature. Development and deployment are much easier as compared to other platforms where you have to go through a lot of stuff. With a tool like DBT, you can do modeling and transformation within a single tool and deploy to Snowflake. It provides continuous deployment and continuous integration abilities. There is a separation of storage and compute, so you only get charged for your usage. You only pay for what you use. When we share the data downstream with business partners, we can specifically create compute for them, and we can charge back the business."

Teradata is a scalable data analytics platform designed to meet enterprise demands for large-scale data management and processing, focusing on performance, scalability, and security for complex query executions.

As a leading data warehousing solution, Teradata integrates advanced analytics enabling organizations to derive insights from massive datasets. It supports high-volume data workloads with its architecture optimized for analytical queries. Users benefit from its robust scalability, allowing seamless expansion as data grows. Teradata's SQL engine is compatible with a wide range of data types, ensuring flexibility in data analysis. With advanced security measures, it protects sensitive data across various environments, providing peace of mind to users handling critical information.

• Linear Scalability: Easily expands data processing capabilities to handle increasing data loads without performance degradation.
• Advanced Analytics: Supports complex analytical operations integrating machine learning and AI for predictive insights.
• SQL Compatibility: Offers a comprehensive SQL interface that supports complex queries across diverse data types.
• Data Security: Implements rigorous security protocols to protect data integrity and confidentiality.

• Improved Decision-Making: Derive actionable insights quickly for strategic business decisions.
• Cost Efficiency: Optimized resource usage reduces operational costs associated with data processing.
• Increased Productivity: Streamlined data operations save time, allowing teams to focus on analysis.
• Enhanced Scalability: Seamless growth without significant additional investment as data volumes increase.

Teradata is widely used in industries like finance, telecommunications, and healthcare, where data-driven decisions are critical. Companies leverage its robust analytics capabilities to enhance customer experiences, streamline operations, and ensure compliance with regulatory requirements. In these sectors, quick access to data insights can significantly impact competitive advantage.