Teradata and Snowflake are leading data warehousing solutions. Read this feature comparison of Teradata and Snowflake.
What is Teradata?
Teradata is a connected multicloud data platform with a highly scalable relational database management system suitable for data warehousing purposes. The Teradata system is based on various symmetric multiprocessing technologies and communication networking, resulting in huge parallel processing systems acting as data stores that accept vast concurrent requests from many clients.
Teradata’s database system is supported by the Massively Parallel Processing architecture. This architecture splits tasks among its processors and executes them in parallel to guarantee each task is completed quickly. The splitting of tasks ensures workload is distributed evenly across the system. Teradata is reinforced as a parallel processing system through its optimizer.
Teradata is linearly scalable as the capacity of the system can be increased alongside the number of Access Module Processors. Teradata can scale up to 2048 nodes.
Teradata Vantage provides users with the freedom to deploy on hybrid and multicloud environments, public clouds like AWS, Google Cloud and Azure, as well as on-premises through Teradata IntelliFlex. It also allows deployment on commodity hardware using VMware. Teradata Vantage offers a pay-as-you-go pricing model and portable licenses between deployment options.
What is Snowflake?
Snowflake is a cloud-native data warehousing and analytics platform that empowers users to carry out analysis and storage of data using cloud-based software and hardware. It is more flexible than traditional data warehouses and offers high-speed data storage, processing and analytics capabilities. Snowflake is built on Azure Cloud and AWS infrastructure. Snowflake is suitable for enterprises without dedicated maintenance, setup and support for in-house servers.
All of Snowflake’s components run on public cloud infrastructure by Google Cloud, Azure and AWS because Snowflake runs fully on the cloud. Snowflake can be integrated into a user’s existing cloud infrastructure with the option of selecting where data should be stored.
Structured and semi-structured data support
Since users can load data into the cloud database without converting or transforming it into a fixed schema, Snowflake allows the combination of structured and semi-structured data for analysis. Snowflake can automatically parse data and extract data attributes before storage.
Snowflake enables almost zero administration, as enterprises can set up and manage their solution without IT team involvement. Processes such as auto-scaling, software updates and increasing clusters and virtual warehouses are automated to reduce human intervention.
With Snowflake, users can easily scale resources when there are high data volumes to improve performance without introducing service interruptions.
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Head-to-head comparison: Teradata vs. Snowflake
Teradata provides fixed capacity. When this capacity is exceeded, users must restructure their systems by obtaining additional hardware and performing upgrades. Snowflake offers unlimited compute sizes and storage, which are made feasible through a cloud service that can be automatically scaled at any time.
The Teradata system uses a shared-nothing architecture where each node is not only independent but also self-sufficient. Teradata’s AMPs and disks work independently. Each AMP is only in charge of its own subdivision of the database. Through an automatic distribution system, Teradata evenly shares data among disks in the absence of human intervention.
Teradata accepts many concurrent requests from numerous client applications to execute them in parallel while distributing the load across the system. It acts as a single data store comprising nodes, a parsing engine, a message parsing layer and virtual processors.
Snowflake also combines a shared-nothing and traditional shared-disk database architecture. Its architecture contains database storage, cloud service and a query processing layer. Snowflake separates computing and storage resources through its multi-layered, shared-data architecture to avoid concurrency.
Unlike in traditional data warehouses where many users trying to access the service would introduce latency, Snowflake matches workloads to the correct virtual warehouses to ensure that queries from one virtual data warehouse do not impact queries from another.
Teradata uses hashing to retrieve data stored in its system; Snowflake uses micro-partitions to store data as every micro-partition has metadata. Accessing data in Snowflake is done by searching metadata.
Teradata has advanced workload management and partition systems where any virtual partition has access to the CPU when not needed by other partitions. Snowflake employs virtual warehouses to separate and manage workloads.
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Choosing the right data warehousing solution
The two solutions are effective data warehousing solutions, so the choice between Teradata and Snowflake is dependent on factors such as your budget, business goals, compute size and storage requirements.