Business organizations are becoming increasingly focused on utilizing their analytical abilities to gather crucial insights from their data. Business Intelligence operations are carried out using the organization’s analytics stack. Selection of the Cloud Datawarehouse and the analytical tools associated is a crucial decision for an effective Data-driven approach of the organization. This selection can be based on various factors and can be decisive in determining the efficiency of the company.
In this article, you will be introduced to Amazon Redshift, Teradata, and their key features, and then a comparison will be provided depending upon different parameters to help you make an informed decision. Redshift Vs Teradata comparison will be done on the parameters of Data Model, Scalability, Performance, Pricing Model, and Security.
Table of Contents
- Introduction to Amazon Redshift
- Introduction to Teradata
- Factors that Drive the Redshift vs Teradata Decision
- Redshift Vs Teradata: Scalability
- Redshift Vs Teradata: Performance
- Redshift Vs Teradata: Pricing and Effort
Introduction to Amazon Redshift
Amazon Redshift is a Cloud Data Warehouse Solution provided by AWS [Amazon Web Services]. It is a cost-effective platform that provides organizations with analytical services that can help turn organizations into Data-Driven enterprises. Pricing is around $1000 per TB per year which is significantly lesser than deploying and maintaining on-site solutions. It is based on modern solutions and approaches which are aimed to provide better performance than an OLTP Data Warehouse, such as Columnar Storage, Data Compression, Zone Mapping. Amazon Redshift fits in perfectly with other solutions provided by AWS and supports multiple connectors and integrations.
Official documentation regarding Amazon Redshift can be found here.
Key Features of Amazon Redshift
- End-to-End Encryption: All data handled on the Cloud is encrypted to ensure the privacy and security of the users. There are multiple ways to implement Key sharing for encrypted data.
- Parallel Processing: Implementing parallel processing is done through a distributed design approach using multiple CPUs to process large data jobs.
- Error Tolerance: Organisations can rely on executing mission-critical work in the Cloud due to the ability of fault and error tolerance of the Data Warehouse to ensure continuous working.
- Isolation of the Network: Parts of the deployment can be isolated from the network and the internet, and are made accessible via IPsec VPN.
- Easy Scalability: The AWS platform provides petabyte-level scaling on data warehousing with limitless concurrency on queries across Redshift and other AWS platforms.
Introduction to Teradata
Teradata is an integrated platform that provides functionality to store, access, and analyze organizational data on the Cloud as well as On-Premise infrastructure. Teradata Database provides an information repository system. It also has support for various tools and utilities, making it a complete and active relational database management system. On the integrated platform, there is Teradata Vantage, which is an analytical platform and enables customers to leverage their data. Various sources cite Teradata Vantage as simply Teradata. Users can analyze any data and deploy it anywhere using the features provided by Teradata.
Official documentation regarding Teradata can be found here.
Key Features of Teradata
- Multiple Tools Support: Teradata provides support for multiple programming languages such as Python, R, SQL, and external tools such as Jupyter, R Studio, helping users to implement their desired operations easily.
- Fixed Transparent Service Structure: There are no locked-in licenses and no penalties or fees for users wanting to make a change in their deployment.
- Flexibility with Deployment: It provides support to deploy the solution both on fixed Public Cloud platforms and On-Premise infrastructure.
- Unified Environment: The integrated SaaS platform provides functionality to perform Compete for descriptive, predictive, and prescriptive analytics on the stored data.
- Linear Multi-Dimensional Scalability: It provides users the ability to scale performance according to Data Volume, Concurrency and Number of Users accessing the platform.
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Factors that Drive the Redshift vs Teradata Decision
- Redshift Vs Teradata: Data Models
- Redshift Vs Teradata: Scalability
- Redshift Vs Teradata: Performance
- Redshift Vs Teradata: Pricing and Effort
- Redshift Vs Teradata: Security
- Redshift Vs Teradata: Pros and Cons
1) Redshift Vs Teradata: Data Models
Comparison of Data Models is crucial in the decision-making process of analytics-based Cloud Data Warehouses. Descriptions of the different Data Models leveraged by these platforms are provided below.
Redshift Vs Teradata: Redshift Data Model
Amazon Redshift Data Model is designed keeping in mind the Data Warehousing requirements.
- It offers a fully managed Cloud-based Data warehouse in which the user need not worry about setting up a Database.
- The Backup and Restore functionality offered are fully automatic. Due to integration with other AWS services, Redshift automatically backs up data into Amazon S3 based Data Lakes.
- Security and privacy of the data are ensured due to Encryption. Inbound security rules and SSL connections are established.
- The Data is stored in a columnar format which enables faster query times as only the specific column is searched through rather than the entire row while the query executes.
- It further supports column compression which can automatically tell which compression strategy would be ideal for that table. (using common ANALYZE COMPRESSION)
- The Redshift cluster automatically maintains multiple copies of the users’ data as a part of fault tolerance.
Redshift Vs Teradata: Teradata Data Model
Teradata is based on a parallel Data Warehouse with shared-nothing architecture.
- Data is stored in a row-based format.
- It supports a hybrid storage model in which frequently accessed data is stored in SSD whereas rarely accessed data is stored on HDD.
- The platform supports a Table partitioning feature and enforces Primary and Secondary Indexes.
- It uses the Hash algorithm to distribute data into various disk units.
- The Data Warehouse can scale up to 2048 nodes, thus offering data storage ability up to 94 petabytes which is a higher storage capacity than Redshift.
- The Data Model is designed to be fault-tolerant and be scalable with redundant network connectivity to ensure reliability for critical use cases.
2) Redshift Vs Teradata: Scalability
Scalability is an important factor for various Web platform-oriented organizations, as the ability to scale up and down depending upon the customer load can help optimize the deployment and thus reduce overall costs.
Depending upon the Node type the Amazon Redshift has a limitation on the scalability of the deployment. This limitation arises from the limitation on the number of nodes. On the lower end, while using dc1.large nodes, the upper limit is 32 nodes which limits the total capacity to 5.12 TB whereas on the upper end the ds2.8xlarge nodes have an upper limit of 128 nodes limiting the capacity to 2 PB.
Teradata can be scaled up to 2048 nodes. The total storage capacity due to this implementation can be from 10 TB to 94 PB [depending upon the configuration of each node] which is significantly higher than Amazon Redshift.
3) Redshift Vs Teradata: Performance
A large part of the performance of the deployment depends upon the processing power of the Advanced SQL Engine of the nodes of the deployment. The higher-end nodes in Amazon Redshift deployment which is ds2.8xlarge offer the equivalent of 36 CPUs per node. The performance of the On-Site deployment of Teradata will depend on the performance of the hardware chosen, whereas for the cloud deployment for Teradata, the highest-end node which is r5.24xlarge offers equivalent to 96 CPUs per node.
The performance of the queries performed also depends on the way in which the data is stored on the storage. Amazon Redshift uses Columnar Database in which the queries are processed faster as the whole row is not required to be accessed and processed to run the query thus providing faster results, whereas the Teradata database stores the data row-wise which requires longer processing times.
4) Redshift Vs Teradata: Pricing and Effort
Teradata requires complex set-up procedures whereas Redshift is easily accessed on the cloud. Redshift offers a low cost per TB for deployment but has limited scalability in comparison to Teradata. Both the platforms try to provide solutions using a different strategy and are catered to different use cases.
Redshift Vs Teradata: Amazon Redshift Pricing Model
For smaller Data Warehouses, Amazon Redshift suggests that users opt for DC2 [Dense Compute] nodes for deployment.
The DC2 nodes come in two variants dc2.large and dc2.8xlarge. The dc2.large nodes cost around $0.33/hour for which you will be allotted equivalent to 2 CPUs and 15 GB of memory. dc2.8xlarge nodes cost around $6.40/hour for which you will be allotted equivalent to 32 CPUs, 244 GB memory, and 2.56 TB of storage.
For Larger Data Warehouses, Amazon Redshift suggests that users opt for RA3 nodes, under this system the users are required to pay separately for computing power and storage.
The RA3 nodes come in two variants ra3.4xlarge and ra3.16xlarge. The storage is charged at $ 0.0271 per gigabyte per month. ras3.4xlarge nodes cost $ 3.0606/hour for which you will be allotted the equivalent of 12 CPUs and 96 GB of memory. ra3.16xlarge nodes cost $ 14.424/hour for which you will be allotted the equivalent of 48 CPUs and 384 GB of memory.
Redshift Vs Teradata: Teradata Pricing Model
Teradata offers two different pricing models for its customers, namely the Blended pricing model and the Consumption pricing model.
For On-Site deployment, the pricing is provided to the customer on a case-by-case basis, but for Cloud deployment of the Data Warehouse (such as AWS Cloud Services), the pricing is decided based on the two models.
Under the Consumption Pricing model, the processing power [Advances SQL Engine] is provided using Amazon EC2 instances which costs $ 5 on-demand per Vantage Unit. The primary storage is provided at $ 0.291 per Vantage Unit and the backup at $ 0.044 per Vantage Unit.
Under the Blended Pricing model, the cost is distributed on an hourly basis and depends on the computer power of the Advanced SQL Engine, and the storage is offered on EBS [Elastic Block Store] at $0.194 per hour.
5) Redshift Vs Teradata: Security
Security and Privacy of data are paramount to all organizations as the customer’s trust in its services relies on it. There are different methods utilized by these platforms to ensure security for their users and developers.
In Amazon Redshift the data is backed up regularly to the AWS S3 Data lake internally to prevent any loss of data in case of a failure. The data is also fully secure by inbound security rules and SSL connections. It uses Virtual Private Cloud for VPC mode and inbound security rule for classic mode cluster.
The Teradata deployments are designed to be fault-tolerant and offer redundant network connectivity to ensure connectivity even in the cases of scaling up. The data distribution between various disks is done through the Hash algorithm and the platform forces Primary and Secondary Index on the Database to ensure Data Integrity.
6) Redshift Vs Teradata: Pros and Cons
Redshift Vs Teradata: Pros of Amazon Redshift Platform
- You can load and unload data incredibly fast due to parallel mode on the platform.
- You have the option to choose the node type. The selection usually depends on the data needs and business requirements.
- You can choose to scale the performance of the cluster’s storage by scaling CPU and storage, without impacting the functioning of the cluster.
- The automatic backup and restore function of AWS ensures stability in unforeseen circumstances.
Redshift Vs Teradata: Cons of Amazon Redshift Platform
- There is no support for conventional concepts of Triggers, Functions, and Procedures.
- The Data model does not enforce Primary and Foreign Keys which can cause data integrity issues.
- Distribution keys cannot be changed once they’re created.
- There is a limit on nodes, databases, tables, etc. on a cluster, thus limiting the scalability of the solution.
Redshift Vs Teradata: Pros of Teradata Platform
- The platform provides support for pre-built utilities i.e. Fastload, Multiload, TPT [Teradata Parallel Transporter], etc.
- Teradata supports a failback feature under which the secondary AMP [Access Module Processor] starts to work on the failure of the primary AMP.
- The Teradata Visual Explain provides an interactive way to showcase the execution plan of the queries in a graphical manner.
- Ferret Utility on the platform helps to set up and display storage space utilization.
Redshift Vs Teradata: Cons of Teradata Platform
- It requires to be set up and installed on on-site infrastructure or scaled up on Third-Party Cloud such as Azure.
- The queries processed scan the whole row which increases query time in comparison to a columnar database.
- The platform supports a maximum of 128 JOINs in a single query.
- Teradata has lower performance in analytical use cases.
In this article, you learned about Amazon Redshift, Teradata, and their key features. Subsequently, a comparison between the two platforms was made depending upon different parameters to help users make a better decision.
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