Moving your organization’s data into a data warehouse is a big step; however, the number of available warehouse solutions can easily overwhelm you. Teradata and Snowflake are two of the most popular options. Since each platform offers unique benefits, a detailed comparison can help you determine which option suits your warehousing requirements.
Teradata offers both on-premise and cloud warehousing capabilities, providing more control over data infrastructure. On the flip side, Snowflake offers improved scalability and flexibility to fluctuating workload demands.
Let’s start with a brief overview of both platforms before discussing the key differences between Teradata vs Snowflake.
Teradata Overview
Teradata is an enterprise-level relational database management system (RDBMS) suitable for data warehousing operations. It allows you to store, process, and analyze large datasets from multiple sources in varied formats. Teradata offers multiple tools, including Teradata Data Fabric, Teradata Studio, and ClearShape Analytics, enabling you to manage structured, semi-structured, as well as unstructured data.
Key Features of Teradata
- Parallel Processing: Teradata supports a Massively Parallel Processing (MPP) system that helps optimize performance, throughput, and reliability by integrating storage and compute nodes in a single system.
- Shared Nothing Architecture: It uses shared-nothing architecture where each processor has its own memory and disk storage, allowing it to work independently without any interference.
- Complex Workload Management: Teradata Active Service Management (TASM) offers a collection of products that allow you to analyze and organize complex workloads involving critical business intelligence.
- Scalability: Teradata systems can scale up to 2048 CPUs and 2048 GB of memory, supporting massive databases over 128 TB with advanced interconnect technology.
- Fault Tolerance: To handle hardware and software faults, Teradata offers features such as vproc migration, AMP clusters, fallback tables, and journaling.
- Programming Language Support: With Teradata, you can interact with data and perform advanced analytics using various programming languages, including C++, C, Python, Java, Ruby, and Perl.
Snowflake Overview
Snowflake is a cloud-based data analytics and warehousing platform that enables you to perform complex queries on large amounts of data, producing impactful insights. Its application spans numerous technology domains, including cybersecurity, data science, and application development.
All the major cloud platforms—AWS, GCP, and Azure—support Snowflake, providing you the flexibility to work in your existing environment. This is one of the main reasons why Snowflake is a top choice for insight generation for major corporations, from financial services to the public sector, among others.
Key Features of Snowflake
- Scalability: For scaling warehouses, Snowflake provides two methods: scale up, which allows you to resize a warehouse, and scale out, adding clusters to a multi-cluster warehouse.
- Concurrency: Snowflake’s multi-cluster architecture allows you to scale computing resources efficiently while managing user and query concurrency, which is helpful, especially during peak and off-hours.
- Data Backup: Snowflake offers a Time Travel feature that allows you to access historical data, whether deleted or altered, at any point within a 90-day window.
- Data Governance: With multiple governance features, such as data classification, object tagging, and row- and column-level security, Snowflake maintains robust data integrity and compliance.
- Snowsight: Snowflake provides a web interface, Snowsight, that you can use to query, analyze, visualize, and validate data.
- Security: Snowflake offers multiple security features, such as access controls, end-to-end encryption, and network security, to safeguard your data.
Teradata vs Snowflake
Teradata vs Snowflake: Architectural Difference
Teradata’s architecture revolves around MPP, a shared-nothing architecture that allows you to perform advanced analytics functions while offering high performance. In this type of architecture, the workload is distributed across multiple virtual processors (vprocs).
The query processing vprocs are referred to as the Access Module Processors (AMPs), which are database server instances that are useful for storing and retrieving data. Each AMP is isolated from the others, executing the queries in parallel to process large volumes of data instantaneously.
The key architectural components of Teradata Vantage include Parsing Engines (PEs), BYNET, AMPS, and Virtual Disks (Vdisk), with the Vdisks specifically assigned to AMPs. In the VantageCloud Lake environments, the primary cluster hosts the Vdisks.
Here’s an overview of the main components of Teradata:
- Parsing Engines (PE): Whenever you execute an SQL query in Teradata, it first passes through the parsing engine. This layer manages user sessions and cross-checks the SQL object’s existence and user privileges. The PE layer is also responsible for parsing and optimizing the queries, preparing query execution plans, and forwarding responses from the AMPs to you.
- BYNET: It is the source of component communication, providing bi-directional, broadcast, multicast, and point-to-point communication. BYNET coordinates multi-AMP queries, data reading from multiple AMPS, processing platform throughput, and preventing congestion by regulating message flows.
- Parallel Database Extension: It is an intermediary layer between the operating system and the Teradata database, allowing MPP systems to use BYNET and shared disk features.
- Access Module Processor (AMP): AMP is responsible for data storage and retrieval. It provides multiple functionalities, including lock management, accounting, row sorting, and recovery processing.
- Virtual Disk (Vdisk): Vdisks are storage units that hold user data within table rows. It maps to physical disk locations.
- Node: A node is a server that works as a processing unit, executing operations under a single operating system. In Teradata Cloud, these physical nodes are replaced by virtual machines (VMs).
On the other hand, Snowflake has a hybrid architecture consisting of shared-disk and shared-nothing architectures.
Like a shared disk system, all the compute nodes in the database share a single storage system. While this method is efficient in accessing data and makes data management easy, it can become a bottleneck when multiple nodes access data simultaneously.
Alternatively, in shared-nothing architecture, each compute node has its own storage, allowing efficient scalability as every node is independent. However, managing data across each node can become a challenging task.
By combining these two architectures, Snowflake offers the scale-out benefits of shared-nothing architecture with the data management simplicity of shared disk architecture.
Snowflake’s architecture consists of three layers:
- Database Storage: This layer stores data in a compressed, columnar format that is not directly visible or accessible. However, you can execute SQL queries to access this data.
- Query Processing: In this layer, Snowflake uses virtual warehouses, which are independent MPP compute clusters, to effectively process queries without affecting the performance of other warehouses.
- Cloud Services: This layer combines all the services that manage Snowflake’s operations, coordinating numerous components to handle user requests, from login to query dispatch.
Teradata vs Snowflake: Indexing
Indexing is a crucial factor that allows you to effectively organize data for effortless retrieval. Creating indexes on a column enables the database to quickly access relevant data without searching through the entire table.
Teradata offers four types of indexes, including primary, secondary, join, and hash indexes:
- Primary Indexes: Primary indexes establish a column with unique, non-null values that identify each row in the entire table.
- Secondary Indexes: Secondary or non-unique indexes enable querying on columns other than the primary key.
- Join Indexes: It eliminates the need to access and join the base tables by offering an indexing structure containing columns from one or two base tables. Join indexes can be defined in many ways, including single-table or multi-table, complete or sparse, hash-ordered or value-ordered, and simple or aggregate.
- Hash Indexes: The hast indexes have similar functions as single-table join indexes. Although join indexes are more complex to define than hash indexes, they offer better functionality than hash indexing.
Comparatively, Snowflake does not support traditional indexing techniques. It relies on features like clustering keys, micro-partitioning, query pruning, and automatic clustering to deliver effective query performance.
- Micro-partitioning: This feature enables Snowflake to store data in small storage with a 50-500 MB capacity. These partitions contain a subset of rows in columnar format, which are easily accessible.
- Query Pruning: Snowflake leverages advanced query pruning techniques to eliminate micro-partitions based on query filter conditions, reducing the data to be processed.
- Clustering: Snowflake clusters related rows into single micro-partitions, improving pruning efficiency by consolidating similar rows at the same location.
Factors to Consider When Choosing Teradata or Snowflake
Let’s explore the key factors you must consider before choosing a data warehouse solution between Snowflake vs Teradata.
Scalability & Performance
When selecting the perfect tool that resonates with your workflow, scalability and performance are essential factors to assess. These two factors decide how well your system can handle growing data volumes while maintaining speed and efficiency.
Why Choose Teradata?
Teradata relies on more traditional methods to achieve better performance and scalability. It depends on MPP shared-nothing architecture data distribution, with each node containing memory, CPUs, storage, and networking facilities.
The user queries are distributed among nodes for parallel execution, and Teradata can also scale up nodes to achieve high performance. However, there’s an upper limit to Teradata’s scalability; it can scale only up to 2,048 nodes.
Why Choose Snowflake?
Snowflake offers a more hybrid architecture to enhance performance and scalability. It uses clusters of virtual warehouses to provide computing power resources for running queries. Snowflake provides complete separation between computing and storage resources, allowing you to scale warehouses independently of storage capacity.
Scaling on demand allows you to choose resources based on business requirements, saving money as you pay only for the resources you use.
Security
With increasing data volumes, securing data from unauthorized access becomes necessary. Security features ensure the protection, reliability, and compliance of your data storage and accessibility. Both Teradata and Snowflake provide robust features in this regard.
Why Choose Teradata?
Teradata offers multiple security features to safeguard your data. Some of the vital security features it provides include:
- Authentication: This involves user verification before allowing access to the data resources. Authentication in Teradata includes user-level security controls, LDAP authentication, single sign-on, IP filters, and trusted sessions.
- Authorization: This feature ensures that only authorized users can access the system and resources. It involves establishing security roles and directory integration.
Why Choose Snowflake?
Similar to Teradata, Snowflake offers numerous security features to protect user identity. Here are some of the security features it provides:
- Federated Authentication and SSO: This feature separates user authentication from service access. It uses an external provider to verify if the login credentials are correct. After verification, Single Sign-On (SSO) allows users to utilize a single ID to access various services.
- Access Control: Snowflake provides two access control models: Discretionary Access Control (DAC) and Role-based Access Control (RBAC). In the former model, each data object has an owner who can grant access permissions. In RBAC, specific roles contain access privileges assigned to the users.
Pricing
When choosing any technology, you must assess the total costs according to your budget. Let’s explore how the associated costs differ from Teradata to Snowflake.
Why Choose Teradata?
Teradata has two pricing plans: VintageCloud Lake and VintageCloud Enterprise. Pricing for VintageCloud Lake starts at $4,800/month, and it includes compute, storage, data transfer, and services packages. On the other hand, VintageCloud Lake+ pricing starts at $5,700/month.
VintageCloud Enterprise pricing starts at $9,000/month and includes packages similar to VintageCloud Lake, with additional features. The Enterprise+ plan starts at $10,500/month.
Why Choose Snowflake?
Snowflake offers four different pricing options: Standard at $2/credit, Enterprise at $3/credit, Business Critical at $4/credit, and Virtual Private Snowflake. You can contact the Snowflake team for custom pricing of Virtual Private Snowflake. All these options have a pay-for-usage option where you pay $2 per terabyte of data/month.
Migrate Data to Your Preferred Warehouse with Airbyte
Integrating data from your in-house or cloud databases into Teradata or Snowflake can be complex and require technical expertise. A no-code tool like Airbyte can help streamline the data replication process.
Airbyte is a data replication platform that allows you to connect multiple data sources to destinations of your choice. It offers 350+ pre-built data connectors that can handle structured, semi-structured, and unstructured data. With these connectors, you can effortlessly extract data and consolidate it into a single repository.
If the connectors you seek are unavailable, you can build a custom connector in minutes using the Connector Development Kit (CDK). This allows you to import data from any platform into your chosen warehouse, between Teradata Cloud vs Snowflake.
Here are some of the critical features of Airbyte:
- Developer-Friendly: PyAirbyte, an open-source library, allows you to develop data pipelines using Python. It enables you to leverage Airbyte connectors to extract data from sources and then develop data-driven applications.
- Change Data Capture (CDC): The CDC feature lets you keep track of the updates made to the source data, replicating these changes in the destination system.
- Deployment Flexibility: Airbyte offers three different deployment methods. Self-managed to run the platform on local environments. Cloud-hosted, enabling Airbyte to manage the infrastructure while you focus on migration and Hybrid.
- Compliance with Industry-specific Regulations: Airbyte adheres to prominent security certifications and standards, including SOC 2, ISO 27001, HIPAA, and GDPR, securing your data from unauthorized access.
- Community Support: Airbyte has an active community with 15,000+ users and 800+ contributions, which allows you to access community-driven connectors, plugins, and support.
Conclusion
Understanding the key differences between Teradata vs Snowflake will help you choose the platform which is best suited for your organizational needs. Before making a choice, it’s essential to weigh the factors that differentiate these platforms.
Snowflake offers impressive scalability, making it ideal if your business undergoes rapid growth. On the other hand, Teradata lets you handle on-premise operations. This works well if your organization has well-established data centers.
A good understanding of these differences can result in the choice of the right platform based on your analytical and operational requirements.