Difference Between Teradata And Snowflake: Top 10 Differences

By: Published: October 8, 2021

There are various Data Warehouses available in the market today, knowing their attributes and strengths will go a long way in helping you to decide which one best fits the purpose of your business, your financial capability, and the magnitude of your operation. When it comes to the field of Data Warehouses, the choice of Teradata vs Snowflake is a relatively tough one.

Teradata is a software company that provides various types of database and Analytics-related products and services. it was formed in 1979 in Brentwood, California as a collaboration between researchers at Caltech and Citibank’s advanced technology group. Snowflake is a Cloud-based Data Warehousing company based in San Mateo, California. It was founded in July 2012 and launched in October 2014. Both these technologies are leveraged by organizations of all scales, both big & small, and depending on the situation, one can dominate over the other. 

This article provides you with a comprehensive analysis of both Data Warehouses and highlights the major differences between them to help you make the Teradata vs Snowflake decision easy. It also provides you a brief overview of both Data Warehouses along with their features. Read along how you can choose the right Data Warehouse for your organization.

Table of Contents

What is Teradata?

Teradata Logo
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Teradata is a popular Relational Database Management System (RDBMS) produced by Teradata Corp. It is suitable for large data warehousing operations, it has the capabilities of handling large volumes of data and it is highly scalable.

Teradata database system is based on a combination of symmetric multiprocessing technology and communication networking, to form large parallel processing systems which act as a data store that can accept a large number of concurrent request from multiple clients at the same time.

Key Features of Teradata

Teradata has the following features:

  • Unlimited Parallelism: The database system of Teradata is based on Massively Parallel Processing (MPP) architecture. The MPP architecture divides the workload on the system evenly across the system by splitting tasks among its processes and runs them in parallel hence ensuring that each task is completed swiftly. Teradata also uses an optimizer that is designed to be parallel in its function, therefore, enhancing Teradata’s reputation as a parallel processing system.
  • Connectivity: Teradata connects to channel-attached systems like mainframes and network attached-based systems. Teradata also supports the usage of standard SQL to connect to data stored in tables and has several extension capabilities.
  • Shared Nothing Architecture: The type of architecture that Teradata uses is called Shared Nothing Architecture, that is, each Teradata node works independently with its Access Module Processors (AMPs) as they do not share their disks. 
  • Scalability: The Teradata system is highly scalable and can be scaled up to about 2048 Nodes by simply doubling the capacity of the system by increasing the number of AMPs. 
  • Automatic Distribution: Teradata has an automatic distribution system that shares data evenly to the disks without any human interference.
  • Utility: Teradata has a wide range of usage and it is suitable for any type of user be it organizations, enterprises, or private application users. It can handle various tasks such as import and export to and from other database systems.

To learn more about Teradata, click this link.

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Download the Guide to Select the Right Data Warehouse
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Learn the key factors you should consider while selecting the right data warehouse for your business.

What is Snowflake?

Snowflake Logo
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Snowflake is a Cloud-based Data Warehousing and Analytics system which gives users access to store and analyze data using cloud-based hardware and software. It is a Software-as-a-Service company powered by an advanced data platform. It is built on Amazon Web Services and Microsoft Azure Cloud infrastructure and owned by Snowflake Inc. 

Snowflake allows fast data storage, processing, and easy Analytic operations like traditional Data Warehouses but it is flexible than most. Its flexibility is derived by combining new SQL query engines with its captivating architecture specific for its cloud service. It is ideal for businesses that do not want to have dedicated resources for setup, maintenance, and support for in-house servers as every operation is done on the Cloud.

Key Features of Snowflake

Snowflake has the following features:

  • Cloud Service Provider: Snowflake runs completely on cloud infrastructure as all the components of Snowflake’s service run in public cloud infrastructures offered by Amazon Web Service, Microsoft Azure, and Google Cloud. You can easily fit Snowflake into your already existing cloud architecture and have the option of choosing the geographic region where your data should be stored.
  • Scalability: Snowflake gives you the ability to scale up resources when there is a large amount of data to be loaded to increase speed and to scale down when the process of loading is finished without creating an interruption to the service. 
  • Separation of Computing and Storage Resources: This is made possible with Snowflake’s multi-layer shared data architecture as it separates the compute and storage resources to avoid concurrency. Workloads to be executed are matched against its compute clusters called a virtual warehouse to ensure that queries from one virtual data warehouse will not affect queries from another, unlike traditional data warehouses where a large number of users would try to access the service, therefore, leading to delays because too many queries are competing for resources. 
  • Near-Zero Administration: Snowflake’s Software-as-a-Service offers data warehousing and enables companies to set up and manage their solution without significant involvement from IT teams as it does not require additional software or hardware to be installed in its operations. It automates most of its processes such as auto-scaling, increasing clusters and virtual warehouses, software updates, etc for you without human intervention.
  • Security: Snowflakes has a range of security features from the way you access snowflake to how data is stored as you can manage network policies by restricting some IP addresses, use various authentication methods like two-factor authentication and encryption across all network communications.
  • Support for Structured and Semistructured Data: Snowflake gives room for the combination of structured and semi-structured data for analysis as you can load it into the cloud database without the need for conversion or transformation into a fixed schema first. It automatically parses the data and extracts the attributes of the data before storing it in a columnar format.
  • Data Sharing: With Snowflake’s architecture, you can easily share data among Snowflake users. It allows you to even share data with other people that are not part of the Snowflake customers through reader accounts that can be created from the user interface.

To learn more about Snowflake, click this link.

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Factors that Drive the Teradata vs Snowflake Decision

Teradata and Snowflake both function using Massively Parallel Processing (MPP) in querying data and have lots of similarities but they also have some differences. Below is a list of key differences between Teradata and Snowflake:

1) Teradata vs Snowflake: Architecture

Teradata acts as a single data store that accepts a large number of concurrent requests from multiple client applications and executes them in parallel along with load distribution among several users. Teradata’s architecture is made up of the following components: 

  • Node – this is a unit in Teradata as each server is referred to as a node that consists of its operating system, CPU, memory, a copy of Teradata’s RDBMS software, and disk space. 
  • Parsing Engine – the parsing engine is responsible for receiving queries from clients and preparing an execution plan. 
  • Message Parsing Layer – this is the networking layer in Teradata that allows communications between the parsing engine (PE) and Access Module Processor (AMP) and also between the nodes. It is referred to as BYNET. 
  • Access Module Processor (AMP) – It is called a virtual processor and its purpose is to store and retrieve data. When a client wants to store data, the parsing engine sends the records to BYNET which in turn sends the row to the target AMP, then the AMP stores this record on its disk. For retrieval, when a client runs a query to get records, the parsing engine sends a request to BYNET which in turn sends a retrieval message to the AMPs. The AMP searches the disk in parallel to identify the record for forwarding to BYNET and from BYNET the record is sent to the parsing engine and then the user.

The architecture of Teradata is shown below.

Teradata vs Snowflake: Teradata Architecture
Image Source

Snowflake’s architecture is a combination of the traditional shared-disk and shared-nothing database architectures. It consists of nodes that access a central data repository like in a shared-disk architecture but also has nodes in a cluster where each node stores a portion of the entire data set locally using MPP to process queries. This combined approach allows for a shared-disk architecture but with the performance and benefits of a shared-nothing architecture. This unique arrangement of Snowflake consist of the following parts and their functions are explained below: 

  • Database Storage – When data is loaded into Snowflake, it rearranges the data into its internal columnar format before storing this optimized version in its cloud storage. It manages how data is stored in it and the data objects stored by Snowflake are not visible nor accessible by customers but can be accessed through SQL queries sent using Snowflake. 
  • Query Processing Layer – This is where query execution takes place and it’s done using virtual warehouses. This virtual warehouse is an independent compute cluster that functions using MPP, it is made up of multiple compute nodes allocated by Snowflake from a cloud provider, therefore, each virtual warehouse does not share compute resources with other virtual warehouses and has no impact on the performance of others. 
  • Cloud Services – This layer is a collection of services that coordinates the activities across the platform as it processes user’s requests. Its functions range from authentication, query parsing, and optimization, access control, metadata management, infrastructure management, etc.

The architecture of Snowflake is shown below.

Snowflake Architecture
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2) Teradata vs Snowflake: Mode of Operation

Teradata uses hardware and software components that need to be installed on-premises for optimal usage of their services. Despite having a cloud service, it is not as popular as the usage of its propriety hardware and software service, whereas Snowflake runs a cloud solution as everything resides in the cloud. The data, software, and the SQL client used to access the Snowflake warehouse is stored and runs on cloud infrastructure. So basically, there are no hardware/software installations, configurations or maintenance, management of data, upgrades, etc. and SQL tuning are handled by Snowflake as it uses AWS, Azure, GCP hardware, and its propriety layer to manage resources and users.

3) Teradata vs Snowflake: Size and Capacity

Teradata operates with a fixed size and capacity and if the need arises for more capacity, you will need to purchase additional hardware and upgrade the system thereby restructuring it.

Snowflake comes with unlimited storage and computes size as it offers a cloud service that can be scaled automatically at any time.

4) Teradata vs Snowflake: Indexes

Teradata uses primary, secondary, and joint indexes. On Snowflakes, there is no such thing as a secondary or joint index.

5) Teradata vs Snowflake: Collection of Statistics

The collection of statistics on Teradata is done by the user. You have to instruct Teradata to carry out the operation but Snowflake collects required statistics on its own without a user having to do anything.

6) Teradata vs Snowflake: Access to Data

Teradata uses hashing to gain access to data stored within its system while Snowflake stores data in a micro-partition and within each micro partition, the data columnar are stored as they are compressed. Each micro partition has metadata, and access is gained by looking up the metadata.

7) Teradata vs Snowflake: Workload Management

Teradata offers sophisticated workload management and partition systems. Any virtual partition can access the CPU if they are not needed by other partitions.

Snowflake uses the concept of a virtual warehouse to separate the workload and manages it for you.

8) Teradata vs Snowflake: Data Distribution

Teradata is a shared-nothing architecture and each Teradata node works independently as they do not share their disks.

Snowflake is not a shared-nothing architecture rather the computing resources have access to shared data.

9) Teradata vs Snowflake: APIs and Other Access Methods

Teradata has the following APIs and access methods: .NET Client API, HTTP REST, JDBC, JMS Adapter, ODBC, OLE DB.

Snowflake has the following APIs and access methods: CLI Client, JDBC, ODBC.

10) Teradata vs Snowflake: Supported Programming Languages

The following programming languages are supported by Teradata: C, C++, Cobol, Java (JDBC-ODBC), Perl, PL/1, Python, R, and Ruby.

The following programming languages are supported by Snowflake: JavaScript, Node.js, and Python.


This article gave a comprehensive analysis of the 2 popular Data Warehousing technologies in the market today: Teradata and Snowflake. It talked about both the Data Warehouses and their features. It also gave the parameters to judge each of the Data Warehouses. Overall, the Teradata vs Snowflake choice solely depends on the goal of the company and the resources it has.

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Share your experience of learning about Teradata vs Snowflake in the comments section below.

Ofem Eteng
Freelance Technical Content Writer, Hevo Data

Ofem is a freelance writer specializing in data-related topics, who has expertise in translating complex concepts. With a focus on data science, analytics, and emerging technologies.

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