Parameters Snowflake Teradata
Process Methodology Executes as a cloud-based solution as everything is on the cloud and hence no hardware/software need Makes use of software and hardware components that must be installed on-premise
Architecture It is a combination of shared-nothing and shared-disk database architecture with nodes being accessed in both It is a single data store that takes up simultaneous requests from multiple clients and executes in parallel
Gaining Data Access Makes use of micro partition and metadata for accessing data Makes use of hashing technique for getting data from the system
Organizations Using It Microsoft, Amazon, Google, Warner Music Group, DoorDash, JetBlue, CapitalOne, Allianz, Western Union, etc. Verizon, Vodafone, American Airlines, GroupOn, Volvo, Unilever, Macy’s, Cleveland Clinic, etc.
Integration Capabilities Snowflake Integration Teradata Integration
Sharing of Data All nodes work individually and don’t share disks since it is a shared-nothing architecture All resources have availability to the combined data since it is not a shared-nothing architecture
Major Components Cloud services, Database storage, Query processing layer Parsing engine, AMP, Node, Message parsing layer
Capability and Sizing Unlimited compute sizing and storage capability with cloud-based service that is scalable anytime Fixed sizing and capability with an option to buy additional infrastructure as needed
Indexing No existence of a secondary or joint index Makes use of primary, secondary, and joint index
Compiling Statistics Automatic compilation of statistics without any user intervention Compilation of statistics done by the user through instructions
Managing Workloads Makes use of virtual warehouse for managing workloads Effective workload management systems through virtual partitioning
Support for APIs and Access Methodologies Supports JDBC, CLI Client, ODBC, etc. Supports .NET Client API, JDBC, ODBC, OLE DB, HTTP REST etc.
Support for Languages Node.js, JavaScript, Python Ruby, C, C++, Python, R, Cobol
Data Warehouse Costing Minimal payment to be made for storage Payment for storage and compute power is higher
Maintenance and Time Management Since it has automatic features like statistic collection and workload management, it is more effective Since it is DBA oriented, it needs more time and effort to be managed