EDUCBA

EDUCBA

MENUMENU
  • Free Tutorials
  • Free Courses
  • Certification Courses
  • 360+ Courses All in One Bundle
  • Login
Home Data Science Data Science Tutorials Head to Head Differences Tutorial Hadoop vs Teradata
Secondary Sidebar
Head to Head Differences Tutorial
  • Differences Tutorial
    • Scikit Learn vs TensorFlow
    • Azure Functions vs Logic Apps
    • Azure Data Factory vs Databricks
    • SHA1 vs MD5
    • Azure SQL Database vs Managed Instance
    • Azure SQL Database vs SQL Server
    • PostgreSQL vs MySQL
    • PostgreSQL vs MySQL Benchmark
    • ArangoDB vs MongoDB
    • Cloud Computing vs Big Data Analytics
    • T-SQL vs SQL
    • PostgreSQL vs MariaDB
    • Spark vs Impala
    • Datadog vs Splunk
    • Domo vs Tableau
    • Data Scientist vs Data Engineer vs Statistician
    • Big Data Vs Machine Learning
    • Predictive Analytics vs Business Intelligence
    • AI vs Machine Learning vs Deep Learning
    • Business Intelligence vs Data Warehouse
    • Apache Kafka vs Flume
    • Data Science vs Machine Learning
    • Business Analytics Vs Predictive Analytics
    • Data mining vs Web mining
    • Data Science Vs Data Mining
    • Data Science Vs Business Analytics
    • Analyst vs Associate
    • Apache Hive vs Apache Spark SQL
    • Apache Nifi vs Apache Spark
    • Apache Spark vs Apache Flink
    • Apache Storm vs Kafka
    • Artificial Intelligence vs Business Intelligence
    • Artificial Intelligence vs Human Intelligence
    • Al vs ML vs Deep Learning
    • SQL vs SQLite
    • Assembly Language vs Machine Language
    • AWS vs AZURE
    • AWS vs Azure vs Google Cloud
    • Big Data vs Data Mining
    • Big Data vs Data Science
    • Big Data vs Data Warehouse
    • Blu-Ray vs DVD
    • Business Intelligence vs Big Data
    • Business Intelligence vs Business Analytics
    • Business Intelligence vs Data analytics
    • Business Intelligence VS Data Mining
    • Business Intelligence vs Machine Learning
    • Business Process Re-Engineering vs CI
    • Cassandra vs Elasticsearch
    • Cassandra vs Redis
    • Cloud Computing Public vs Private
    • Cloud Computing vs Fog Computing
    • Cloud Computing vs Grid Computing
    • Cloud Computing vs Hadoop
    • Computer Network vs Data Communication
    • Computer Science vs Data Science
    • Computer Scientist vs Data Scientist
    • Customer Analytics vs Web Analytics
    • Data Analyst vs Data Scientist
    • Data Analytics vs Business Analytics
    • Data Analytics vs Data Analysis
    • Data Analytics Vs Predictive Analytics
    • Data Lake vs Data Warehouse
    • Data Mining Vs Data Visualization
    • Data mining vs Machine learning
    • Data Mining Vs Statistics
    • Data Mining vs Text Mining
    • Data Science vs Artificial Intelligence
    • Data science vs Business intelligence
    • Data Science Vs Data Engineering
    • Data Science vs Data Visualization
    • Data Science vs Software Engineering
    • Data Scientist vs Big Data
    • Data Scientist vs Business Analyst
    • Data Scientist vs Data Engineer
    • Data Scientist vs Data Mining
    • Data Scientist vs Machine Learning
    • Data Scientist vs Software Engineer
    • Data visualisation vs Data analytics
    • Data vs Information
    • Data Warehouse vs Data Mart
    • Data Warehouse vs Database
    • Data Warehouse vs Hadoop
    • Data Warehousing VS Data Mining
    • DBMS vs RDBMS
    • Deep Learning vs Machine learning
    • Digital Analytics vs Digital Marketing
    • Digital Ocean vs AWS
    • DOS vs Windows
    • ETL vs ELT
    • Small Data Vs Big Data
    • Apache Hadoop vs Apache Storm
    • Hadoop vs HBase
    • Between Data Science vs Web Development
    • Hadoop vs MapReduce
    • Hadoop Vs SQL
    • Google Analytics vs Mixpanel
    • Google Analytics Vs Piwik
    • Google Cloud vs AWS
    • Hadoop vs Apache Spark
    • Hadoop vs Cassandra
    • Hadoop vs Elasticsearch
    • Hadoop vs Hive
    • Hadoop vs MongoDB
    • HADOOP vs RDBMS
    • Hadoop vs Spark
    • Hadoop vs Splunk
    • Hadoop vs SQL Performance
    • Hadoop vs Teradata
    • HBase vs HDFS
    • Hive VS HUE
    • Hive vs Impala
    • JDBC vs ODBC
    • Kafka vs Kinesis
    • Kafka vs Spark
    • Cloud Computing vs Data Analytics
    • Data Mining Vs Data Analysis
    • Data Science vs Statistics
    • Big Data Vs Predictive Analytics
    • MapReduce vs Yarn
    • Hadoop vs Redshift
    • Looker vs Tableau
    • Machine Learning vs Artificial Intelligence
    • Machine Learning vs Neural Network
    • Machine Learning vs Predictive Analytics
    • Machine Learning vs Predictive Modelling
    • Machine Learning vs Statistics
    • MariaDB vs MySQL
    • Mathematica vs Matlab
    • Matlab vs Octave
    • MATLAB vs R
    • MongoDB vs Cassandra
    • MongoDB vs DynamoDB
    • MongoDB vs HBase
    • MongoDB vs Oracle
    • MongoDB vs Postgres
    • MongoDB vs PostgreSQL
    • MongoDB vs SQL
    • MongoDB vs SQL server
    • MS SQL vs MYSQL
    • MySQL vs MongoDB
    • MySQL vs MySQLi
    • MySQL vs NoSQL
    • MySQL vs SQL Server
    • MySQL vs SQLite
    • Neural Networks vs Deep Learning
    • PIG vs MapReduce
    • Pig vs Spark
    • PL SQL vs SQL
    • Power BI Dashboard vs Report
    • Power BI vs Excel
    • Power BI vs QlikView
    • Power BI vs SSRS
    • Power BI vs Tableau
    • Power BI vs Tableau vs Qlik
    • PowerShell vs Bash
    • PowerShell vs CMD
    • PowerShell vs Command Prompt
    • PowerShell vs Python
    • Predictive Analysis vs Forecasting
    • Predictive Analytics vs Data Mining
    • Predictive Analytics vs Data Science
    • Predictive Analytics vs Descriptive Analytics
    • Predictive Analytics vs Statistics
    • Predictive Modeling vs Predictive Analytics
    • Private Cloud vs Public Cloud
    • Regression vs ANOVA
    • Regression vs Classification
    • ROLAP vs MOLAP
    • ROLAP vs MOLAP vs HOLAP
    • Spark SQL vs Presto
    • Splunk vs Elastic Search
    • Splunk vs Nagios
    • Splunk vs Spark
    • Splunk vs Tableau
    • Spring Cloud vs Spring Boot
    • Spring vs Hibernate
    • Spring vs Spring Boot
    • Spring vs Struts
    • SQL Server vs PostgreSQL
    • Sqoop vs Flume
    • Statistics vs Machine learning
    • Supervised Learning vs Deep Learning
    • Supervised Learning vs Reinforcement Learning
    • Supervised Learning vs Unsupervised Learning
    • Tableau vs Domo
    • Tableau vs Microstrategy
    • Tableau vs Power BI vs QlikView
    • Tableau vs QlikView
    • Tableau vs Spotfire
    • Talend Vs Informatica PowerCenter
    • Talend vs Mulesoft
    • Talend vs Pentaho
    • Talend vs SSIS
    • TensorFlow vs Caffe
    • Tensorflow vs Pytorch
    • TensorFlow vs Spark
    • TeraData vs Oracle
    • Text Mining vs Natural Language Processing
    • Text Mining vs Text Analytics
    • Cloud Computing vs Virtualization
    • Unit Test vs Integration Test?
    • Universal analytics vs Google Analytics
    • Visual Analytics vs Tableau
    • R vs Python
    • R vs SPSS
    • Star Schema vs Snowflake Schema
    • DDL vs DML
    • R vs R Squared
    • ActiveMQ vs Kafka
    • TDM vs FDM
    • Linear Regression vs Logistic Regression
    • Slf4j vs Log4j
    • Redis vs Kafka
    • Travis vs Jenkins
    • Fact Table vs Dimension Table
    • OLTP vs OLAP
    • Openstack vs Virtualization
    • Cluster v/s Factor analysis
    • Informatica vs Datastage
    • CCBA vs CBAP
    • SPSS vs EXCEL
    • Excel vs Tableau
    • Cassandra vs MySQL
    • RabbitMQ vs Kafka
    • SAAS vs Cloud
    • RabbitMQ vs Redis
    • AMQP vs MQTT
    • Forward Chaining vs Backward Chaining
    • Google Data Studio vs Tableau
    • ActiveMQ vs RabbitMQ
    • Cloud vs Data Center
    • Cores vs Threads
    • Inner Join vs Outer Join
    • ZeroMQ vs Kafka
    • Mxnet vs TensorFlow
    • Redis vs Memcached
    • RDBMS vs NoSQL
    • AWS Direct Connect vs VPN
    • Cassandra vs Couchbase
    • Elegoo vs Arduino
    • Redis vs MongoDB
    • Chef vs Puppet
    • GSM vs GPRS
    • Keras vs TensorFlow vs PyTorch
    • Cloudflare vs CloudFront
    • Bitmap vs Vector
    • Left Join vs Right Join
    • IaaS vs PaaS
    • Blue Prism vs UiPath
    • GNSS vs GPS
    • Cloudflare vs Akamai
    • GCP vs AWS vs Azure
    • Arduino Mega vs Uno
    • Qualitative vs Quantitative Data
    • Arduino Micro vs Nano
    • PIC vs Arduino
    • PRTG vs Solarwinds
    • PostgreSQL vs SQLite
    • Metabase vs Tableau
    • Arduino Leonardo vs Uno
    • Arduino Due vs Mega
    • ETL Vs Database Testing
    • DBMS vs File System
    • CouchDB vs MongoDB
    • Arduino Nano vs Mini
    • IaaS vs PaaS vs SaaS
    • On-premise vs off-premise
    • Couchbase vs CouchDB
    • Tableau Dimension vs Measure
    • Cognos vs Tableau
    • Data vs Metadata
    • RethinkDB vs MongoDB
    • Cloudera vs Snowflake
    • HBase vs Cassandra
    • Business Analytics vs Business Intelligence
    • R Programming vs Python
    • MongoDB vs Hadoop
    • MySQL vs Oracle
    • OData vs GraphQL
    • Soft Computing vs Hard Computing
    • Binary Tree vs Binary Search Tree
    • Datadog vs CloudWatch
    • B tree vs Binary tree
    • Cloudera vs Hortonworks
    • DevSecOps vs DevOps
    • PostgreSQL Varchar vs Text
    • PostgreSQL Database vs schema
    • MapReduce vs spark
    • Hypervisor vs Docker
    • SciLab vs Octave
    • DocumentDB vs DynamoDB
    • PostgreSQL union vs union all
    • OrientDB vs Neo4j
    • Data visualization vs Business Intelligence
    • QlikView vs Qlik Sense
    • Neo4j vs MongoDB
    • Postgres Schema vs Database
    • Mxnet vs Pytorch
    • Naive Bayes vs Logistic Regression
    • Random Forest vs Decision Tree
    • Random Forest vs XGBoost
    • DynamoDB vs Cassandra
    • Looker vs Power BI
    • PostgreSQL vs RedShift
    • Presto vs Hive
    • Random forest vs Gradient boosting
    • Gradient boosting vs AdaBoost
    • Amazon rds vs Redshift
    • Bigquery vs Bigtable
    • Data Architect vs Data Engineer
    • DataSet vs DataTable
    • dataset vs dataframe
    • Dataset vs Database
    • New Relic vs Splunk
    • Data Architect and Management Designer
    • Data Engineer vs Data Analyst
    • Grafana vs Tableau
    • MySQL text vs Varchar
    • Relational Database vs Flat File
    • Datadog vs Prometheus
    • Neo4j vs Neptune
    • Data Mining vs Data warehousing
    • DocumentDB vs MongoDB
    • PostScript vs PCL
    • QRadar vs Splunk
    • Qlik Sense vs Tableau
    • DigitalOcean vs Google Cloud
    • PostgreSQL vs Elasticsearch
    • Redshift vs blueshift
    • Gitlab vs Azure DevOps

Related Courses

Online Data Science Course

Online Tableau Training

Azure Training Course

Hadoop Certification Course

Data Visualization Courses

All in One Data Science Course

Hadoop vs Teradata

By Priya PedamkarPriya Pedamkar

Hadoop vs Teradata

Differences Between Hadoop vs Teradata

Hadoop

Hadoop is an open source Apache project which provides the framework to store, process and analyze the large volume of data. Hadoop’s core components are the Java programming model for processing data and HDFS (Hadoop distributed file system) for storing the data in a distributed manner. The data is divided into chunks and is distributed among the multiple nodes present in the same cluster.

Hadoop cluster consists of 1 ton (may vary as per the requirement) number of nodes of commodity (less expensive) hardware and the task is performed on the same node on which data is present and if suppose the data is distributed on 10 different nodes than the same job will run on all 10 nodes.

Hadoop works on the principle that if one node (computer) will complete a task in 10 hours than 10 nodes should complete the task in one hour.

Start Your Free Data Science Course

Hadoop, Data Science, Statistics & others

Hadoop does not increase the processing of task rather it distributes the task to multiple nodes and all nodes work in parallel to complete the task in much lesser time, once all the jobs are completed the data from each node is collected and combined back to give the output.

By default, Hadoop creates 3 replicas in HDFS of original data on each different node and since it uses commodity hardware, hardware failure is very common and if some node goes down while processing the data then there are always two other nodes present with same data to process it.

Teradata

Teradata is

All in One Data Science Bundle(360+ Courses, 50+ projects)
Python TutorialMachine LearningAWSArtificial Intelligence
TableauR ProgrammingPowerBIDeep Learning
Price
View Courses
360+ Online Courses | 50+ projects | 1500+ Hours | Verifiable Certificates | Lifetime Access
4.7 (86,171 ratings)
programming language

a product of Teradata company and is one of the well known RDMS (Relational Database management system) best suited for database warehousing application dealing with a very huge amount of data. Teradata consists of tables as like any other traditional database and can be queried using query language similar to traditional databases.

Teradata has a patented software PDE (Parallel database extension) which is installed on Teradata hardware component, this PDE divides the processor of a system into multiple virtual software processors where each virtual processor acts as an individual processor and is capable of performing all tasks independently. In similar fashion, the hardware disk component of Teradata is also divided into multiple virtual disk corresponding to each virtual processor.

Now, whenever data is queried each processor will look for the data only in its corresponding virtual memory and all virtual processors will work in parallel to search the data in their corresponding virtual memory. Since the process is carried out in parallel it is called as possessing Massively Parallel Processing (MPP) architecture. Due to its parallel processing, the Teradata is faster with a great margin as compared to traditional databases.

Head to Head Comparison Between Hadoop and Teradata (Infographics)

Below is the top 11 Comparison Between Hadoop and Teradata:

Teradata vs Hadoop

Key Differences Between Hadoop and Teradata

Below is the key differences between Hadoop and Teradata :

Technology difference:
Hadoop is a Big data technology, which is used to store the very large amount of data in a distributed fashion among the nodes, whereas Teradata is Relational database warehouse implemented in single RDBMS which acts as a center repository.

Cost factor:
Hadoop is an open source framework and there is no licensing cost for it and is freely available also the hardware used in the Hadoop Ecosystem is commodity hardware, so the overall cost of the Hadoop ecosystem is very less, on the other hand, Teradata has a licensing cost and hardware used is also comparatively expensive which makes the Teradata more expensive than Hadoop.

Type of data:
Hadoop can store and process any type of data by using multiple open source BigData tools specially designed for Hadoop ecosystem. Hadoop has a very huge variety of tools to process structure, semi-structured as well as unstructured data whereas Teradata mainly deals with the structured tabular format data, it can also store and process unstructured and semi-structured data but processing unstructured and semi-structured data is not that easy as the data has to be processed using query language.

Multiple languages support:
Hadoop supports multiple programming language executions in parallel in Hadoop ecosystem unlike Teradata, which uses a query language to perform the operations over data.

Performance:
Hadoop has its own data warehousing tool called hive which is used to query the structured data present in flat files in a distributed file system but is comparatively slower than Teradata. Hive also does not have any concept of a primary key while Teradata here gets the advantage as it supports primary key which also pushes the performance of querying data using Teradata.

Latency:
Teradata has low latency and provides the results faster as compared to Hadoop and due to low latency of Teradata, it is used where time is the major factor of requirement.

Data security:
Teradata is much more secure as compared to Hadoop.

Schema:
A well-defined schema is required before loading the data into Teradata whereas there is no such concern in Hadoop.

Comparison Table Between Hadoop and Teradata

Below are the lists of points, describe the Differences between Hadoop and Teradata :

Basis of Comparison Teradata Hadoop
Parallel Processing Workload is divided across the system and evenly among the processors in the system.

 

Workload is divided among the different nodes on which relevant data is present and each node processes the task individually in parallel which reduces the overall time taken to complete the task.
Share-nothing Architecture Teradata task executing in a virtual processor is independent of the tasks in other virtual processors.

 

Task execution on any node of the Hadoop is independent to tasks executing on other nodes.
Highly Scalable More nodes/disks can be added but will increase the licensing cost. More number of nodes/disks can be added as and when required to increase the processing and storage power.
Automatic Data Distribution In Teradata the hashing operation is performed over the primary key of a table to distribute the data evenly over the disks. In Hadoop, the data is distributed among the nodes as per the space available in the data nodes.
Multiple Copies of Data Yes Yes
Hardware Fault Tolerance If a job fails, then the same job is triggered on a different processor with a different replica of data.

 

If a job/node fails, then the same job is triggered on a different node on which the replica of data is present.
Capital Investment Huge( Software Licensing + hardware )

 

Less ( Commodity hardware ( less expensive )  and no license ).
Speed of Processing Comparatively faster than Hadoop. Comparatively slower than    Teradata.
Handles type of Data Storage Can store Structured, Semistructured as well as unstructured data.

 

Can store Structured, Semistructured as well as unstructured data.
Difficulty in processing Unstructured and Semi-structured data Comparatively difficult than Hadoop.  Comparatively easier than Teradata.
Ease of Code Development Easy to use as SQL query needs to be written. Bit difficult as coding needs to be done in languages like Java/python etc for writing mapper and reducers.

Conclusion

So, here now we can conclude on whether one should go for Hadoop vs Teradata based on three major factors, i.e. investment cost, execution time and type of data dealing with.

If less investment cost is the major factor and user can compromise with execution time, then one must choose Hadoop over Teradata.

If fast execution is a priority of the user and can invest in the licensing cost of Teradata then one must go for Teradata.

If the user has to deal with unstructured or semi-structured data, then Hadoop is preferred as it is comparatively easy to process unstructured and semi-structured data due to a variety of tools available for Hadoop.

Recommended Articles

This has been a guide to Hadoop vs Teradata. Here we have discussed Hadoop vs Teradata head to head comparison, key difference along with infographics and comparison table. You may also look at the following articles to learn more –

  1. Find Out The Best 6 Comparisons Between Hadoop Vs SQL
  2. Learn The 10 Useful Difference Between Hadoop vs Redshift
  3. Hadoop vs Spark: What are the Differences
  4. Laravel vs Codeigniter: What are the Benefits
Popular Course in this category
Hadoop Training Program (20 Courses, 14+ Projects, 4 Quizzes)
  20 Online Courses |  14 Hands-on Projects |  135+ Hours |  Verifiable Certificate of Completion
4.5
Price

View Course

Related Courses

Data Scientist Training (85 Courses, 67+ Projects)4.9
Tableau Training (8 Courses, 8+ Projects)4.8
Azure Training (6 Courses, 5 Projects, 4 Quizzes)4.7
Data Visualization Training (15 Courses, 5+ Projects)4.7
All in One Data Science Bundle (360+ Courses, 50+ projects)4.7
3 Shares
Share
Tweet
Share
Primary Sidebar
Footer
About Us
  • Blog
  • Who is EDUCBA?
  • Sign Up
  • Live Classes
  • Corporate Training
  • Certificate from Top Institutions
  • Contact Us
  • Verifiable Certificate
  • Reviews
  • Terms and Conditions
  • Privacy Policy
  •  
Apps
  • iPhone & iPad
  • Android
Resources
  • Free Courses
  • Database Management
  • Machine Learning
  • All Tutorials
Certification Courses
  • All Courses
  • Data Science Course - All in One Bundle
  • Machine Learning Course
  • Hadoop Certification Training
  • Cloud Computing Training Course
  • R Programming Course
  • AWS Training Course
  • SAS Training Course

ISO 10004:2018 & ISO 9001:2015 Certified

© 2022 - EDUCBA. ALL RIGHTS RESERVED. THE CERTIFICATION NAMES ARE THE TRADEMARKS OF THEIR RESPECTIVE OWNERS.

EDUCBA
Free Data Science Course

SPSS, Data visualization with Python, Matplotlib Library, Seaborn Package

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA Login

Forgot Password?

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA
Free Data Science Course

Hadoop, Data Science, Statistics & others

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

Let’s Get Started

By signing up, you agree to our Terms of Use and Privacy Policy.

This website or its third-party tools use cookies, which are necessary to its functioning and required to achieve the purposes illustrated in the cookie policy. By closing this banner, scrolling this page, clicking a link or continuing to browse otherwise, you agree to our Privacy Policy

Loading . . .
Quiz
Question:

Answer:

Quiz Result
Total QuestionsCorrect AnswersWrong AnswersPercentage

Explore 1000+ varieties of Mock tests View more