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.

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

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:

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.

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