Redshift Vs Teradata - An In-Depth Comparison

EBOOK
REDSHIFT VS TERADATA
AN IN-DEPTH COMPARISON
AMAZON REDSHIFT
TERADATA

Table of Contents
Redshift Vs Teradata 1
Redshift Architecture & Its Features 1
Teradata Architecture & Its Features 2
Redshift Data Model 4
Teradata Data Model 7
Pros 8
Cons 9
Teradata Pros and Cons 12
Pros 12
Cons 13
Features supported only by Teradata, not Redshift 15
Redshift Vs Teradata In A Nutshell 16
Pricing and Effort Comparison 20
When and How to Migrate data from Teradata to Redshift 21
Summary 22
ETL Challenges While Working With Amazon Redshift 23

1
Redshift Vs Teradata
Redshift versus Teradata has been one of the most debatable data
warehouse comparisons. In this ebook, we will cover the detailed
comparison between Redshift and Teradata.
Redshift Architecture & Its Features
Redshift is a fully managed petabyte scale data warehouse on the cloud.
You can even start working from a few Gigabytes or Terabytes of data.
Additionally, you can also scale it up to petabytes depending upon your
business requirement. Redshift engine is also called a cluster and it is
built up from one or more nodes. There are two types of nodes called
Compute and Leader node. Compute node contains 2 or more slices
depending upon node types. Leader node does multiple roles which
include communicating with JDBC/ODBC client and creating the query
execution plan to transfer it to compute node(s). Also, the cluster is
incomplete without a Leader node.
You can check out our blog for a detailed article on ​Redshift Architecture​.

2
Teradata Architecture & Its Features
Teradata is an RDBMS, meant for a data warehouse with an on-premise
setup. It requires installation since it is unavailable on cloud platforms.
Although Teradata is not over the cloud, you can spin up a Teradata
instance on a cloud VM. Teradata is designed on MPP shared nothing
architecture.
Here is a diagrammatic representation of Teradata Architecture.

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The four major components of Teradata are as follows:
1. Node:​ The primary component of Teradata is called Node, which is a
basic unit of Teradata. It has its own OS, CPU, RAM, disk space etc.
2. Parsing Engine:​ Parsing Engine or PE is responsible for preparing the
query execution plan.
3.​ ​BYNET:​ BYNET receives query execution plan from PE and transfers it
to AMPs aka Virtual Processor and vice versa. It is also called as Message
Parsing layer.
4. Access Module Processor (AMP):​ AMP is an important component of
Teradata. AMP manages the processing of data by storing it in vDisks.
Data can be stored in any AMP depending on the hash algorithm. In case
the first BYNET fails there is an additional BYNET to take over. BYNET is
responsible to communicate between the AMPs. In multi-node systems,
Teradata will have at least two BYNETs to make the system fault tolerant.

4
Redshift Data Model
Redshift data model is designed for Data warehousing purposes. The
unique features of Redshift make it a smart Data warehouse choice.
1. Redshift is a fully managed data warehouse. You don't have to worry
about setting up and installing the database. You just have to spin up
your cluster and the database is ready.
2. Redshift’s backup and restore are fully automatic. Through automatic
snapshots, data in Redshift automatically gets backed up in S3 internally
at regular intervals.
3. Data is fully secured by inbound security rule and SSL connection. It
has VPC for VPC mode and inbound security rule for classic mode cluster.
4. Redshift stores data in the columnar format, unlike other data
warehouses storage. For example, if you hit your query for a specific
column, Redshift will exclusively search in that specific column instead of
the entire row. This saves an enormous amount of time in query
processing.
5. Data is stored in blocks of 1 MB instead of typical blocks of 8 KB or 64
KB which helps Redshift to store more data in a single block.
6. Redshift does not have the concept of indexes. Instead, it has zone
maps. With the help of zone map Redshift easily identifies which block
has lowest and highest value for that column. Zone maps inform the
cluster about all the blocks that are needed to be read.
7. Redshift has column compression (encoding). ANALYZE
COMPRESSION command automatically tells what compression strategy
to apply for that table. Redshift provides various encoding techniques.
Refer ​AWS documentation​ for more details on encoding.

5
8. Redshift has a feature of caching the result of repeat queries for faster
performance. To check whether your query has used cache, you can see
the output of column source_query available in SVL_QLOG. If your query
has used cache it will store the value of query id of which was run by the
specific user id.
Example:
SELECT​ ​USERID, QUERY, ELAPSED, SOURCE_QUERY from SVL_QLOG ​WHERE
USERID in (600, 601);
In the below example, QUERY ID 853219 of USERID 601 has used the
cache. (QUERY ID 123456 of USERID 600). Also, ​ ​QUERY ID 853219 ran
by userid → 601 has utilized the cache and elapsed time in microseconds
has reduced drastically.
USERID | QUERY ID
| ELAPSED | SOURCE_QUERY
--------+-------------+----------+---------------
600 | 123456 | 90000 | NULL
600 | 567890 | 80000 | NULL
601 | 853219 | 30 | 123456
9. Redshift data model is similar to a typical data warehouse when it
comes to analytical queries. You can create fact tables, dimension tables,
and views. It supports all major query execution strategy i.e., Inner join,
Outer join, Subquery, and Common Table Expressions (with clause).
10. From a storage perspective Redshift cluster maintains multiple copies
of your data as part of fault tolerance.

6
Teradata Data Model
1. Teradata is a massive parallel Data warehouse with shared-nothing
architecture. However, unlike Redshift, the data is stored in a row-based
format.
2. Teradata uses a different kind of indexes for fast data retrieval. Indexes
include Primary, Secondary, Join, and Hash Indexes, etc. Please note that
Secondary Index does not affect the distribution of rows across AMPs.
Although, the secondary index takes extra processing overhead.
3. Teradata supports and enforces Primary and Secondary index.
4. Teradata has a hybrid storage concept where frequently used data is
stored in SSD while the less accessed data is stored in HDD. Teradata
has a higher storage capacity than Redshift.
5. Teradata does support Table partitioning feature, unlike Redshift.
6. Teradata uses the Hash algorithm to distribute data into various disk
storage units.
7. Teradata can scale up to 2048 nodes. It has a storage capacity ranging
from 10 TB to 94 petabytes thus providing higher storage capacity than
Redshift.
8. Teradata supports all kinds of major SQL related features (Primary
Index, Secondary Index, Sequences, Stored Procedures, User Defined
Functions, and Macros etc) which are compulsorily needed as part of Data
Warehouse RDBMS.
9. Teradata's data model is designed to be fault tolerant. It is also
designed to be scalable with redundant network connectivity to ensure
throughout data connectivity and availability.

7
Redshift Pros and Cons
Pros
1. Loading and unloading of data is exceptionally fast. You can load data
in parallel mode. Redshift, even for a high volume of data, supports data
loading from the zipped file. Redshift recommends loading the data from
the COPY command for faster performance.
2. You can load data from NoSQL database service, AWS DynamoDB.
Refer ​AWS documentation​ for more detailed information about
DynamoDB.
3. You have an option to choose the node type (Dense Storage or Dense
Compute) of your cluster depending upon your data needs and business
requirements.
4. You can scale your cluster's storage and CPU for better performance at
any instant without any impact to the cluster.
5. You can migrate your data from various data warehouses into Redshift
without much hassle. AWS does provide a service for the same called
Database Migration Service (DMS). Refer to ​AWS documentation​ for
more detailed information.
6. You do not have to worry about the security as you can build your
cluster inside a VPC and also use SSL encryption for further protection.
7. Redshift backup and restore feature is pretty simple. Through
automatic snapshots, your data is automatically backed up regularly.
Snapshots are incremental, so you do not have to worry about any
misses. You can also copy data to another region in case of any business
need. Kindly refer ​AWS documentation​ for more details on working with
snapshots.

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8. Redshift has an advanced feature called Redshift Spectrum. Using
Redshift Spectrum you can query huge amounts of data directly from S3.
While doing so, you can skip the loading of data through COPY command
or any other method. You can refer to the detailed guide on ​Redshift
Spectrum​ for more information.
9. Using Sort Keys, data can be pre-sorted based on specific columns.
Also, the query performance can be improved automatically.
10. Using Distribution Keys, data can be easily distributed across nodes
equally to increase the query performance.
11. Redshift provides various pre-built system tables and views to help
developers and designers to help out during ETL and other processes.
12. Setup related commands can be run through various modes such as
AWS console, Command Line Interface (CLI), API, etc.
13. AWS Redshift applies some patches and upgrades to the cluster
automatically through maintenance window (configurable value). ence
you do not have to worry about applying patches.
Cons
1. In Redshift, there is no concept of function, triggers, and procedures.
2. There is no concept of sequence column in Redshift. You need to
handle it through your ETL logic in case you need to generate sequence
number of your column.
3. Unlike other common data warehouses, Redshift does not enforce
Primary keys or Foreign keys which can create data integrity issues.

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4. Only S3, DynamoDB, and EMR support a parallel load in Redshift. In
case you want to load data from other services you need to write ETL
scripts or use ETL solutions such as ​Hevo​.
5. It requires a good understanding of Sort and Dist key. There are some
basic ground rules to set for sort and dist keys. If set improperly then it
could lead to hampering of performance.
6. Distribution keys cannot be changed once it is created. You need to be
extremely careful while designing your tables. Wrong distribution keys
could hamper the overall performance.
7. In Redshift, there is no concept of DBLink, you cannot directly connect
to another database/data warehouse tables for your queries.
8. In Redshift, VACUUM and ANALYZE are mandatory on key tables. It
can hamper the performance badly if run during business hours. Hence it
needs to be handled carefully.
9. In Redshift cluster, there is a limit on the number of nodes, databases,
tables, etc. Maximum storage limit is still lesser than data warehouses like
Teradata. Here is the node limitation list:
Node Type vCPU Storage per Node Node Range
dc1.large 2 160 GB SSD 1-32
dc1.8xlarge 32 2.56 TB SSD 2-128
dc2.large 2 160 GB NVMe-SSD 1-32
dc2.8xlarge 32 2.56 TB NVMe-SSD 2-128
ds2.xlarge 4 2 TB HDD 1-32
ds2.8xlarge 36 16 TB HDD 2-128
You can refer to ​AWS documentation​ to know more about the limits in
Amazon Redshift.

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10. Although Redshift in classic mode is still in use, its cluster
performance is relatively modest.
11. Redshift still supports only a single AZ environment and does not
support multi-AZ environment.
12. Redshift has a limit on query concurrency of 15. You can have a
maximum of 8 queues in a cluster. If your queues are unmanaged, then it
hinders the performance.
13. Your design should make sure that the cluster is not in use during the
maintenance window period, else job will fail.
14. There is no concept of table partitioning in Redshift.
15. In Redshift, you do not have a concept of SET and MULTISET tables
(SET tables are the tables that do not allow duplicates). This needs to be
handled programmatically else it could lead to reporting errors if handled
inappropriately.
You can refer to Hevo’s blog which talks about the ​Pros and Cons of
Amazon Redshift​ in complete detail.

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Teradata Pros and Cons
Pros
1.​ ​Teradata is a massively parallel data warehouse with shared nothing
architecture.
2. Teradata has provided pre-built utilities i.e. Fastload, Multiload, TPT,
BTEQ etc.
3. Teradata is linearly scalable. If data volume rises, AMPs or Nodes can
also be increased.
4. Teradata also has fallback feature. In case one AMP is down, another
AMP will take over for data retrieval.
5. Teradata provides an impressive tool called Teradata Visual Explain. It
visually shows the execution plan of queries in a graphical manner. This
helps developers/designers to fine-tune their queries.
6. Teradata provides Ferret utility to set and display storage space
utilization.

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Cons
1. One of the biggest cons of Teradata is that it is not cloud-based unless
scaled up to run over the cloud. It requires some initial setup or you need
to integrate with other cloud service providers i.e, AWS or Azure.
2. It is not a columnar data warehouse.
3. Since Teradata is not a columnar DB, it runs entire row even if you
search over a single column. You may end up with performance issues
unless your data warehouse is properly designed.
4. If a query runs on a set of different columns over the bigger dataset, it
could lead to performance issues; unless query has been run on the
indexed columns.
5. Teradata only supports a maximum of 128 joins in a single query. If you
want to perform more joins, you need to break them into chunks and
handle it accordingly.
6. Redshift outperforms Teradata in Analytical performance, Visualisation
on storage, & CPU utilization visualization. Everything can be viewed in a
single AWS console or through the Cloudwatch monitor in Redshift. On
the other hand, Teradata provides separate visual tools while for few
others checks and commands need to be hit in Teradata client.
7. Teradata has no default column compression mechanism. Column
compression needs to be done manually, and you can perform up to 256
unique column value compression per column.
8. There are a lot of limitations on the number of columns, table value, and
table name length in Teradata. You can refer to ​Teradata documentation
for more detailed information.

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Features supported only by Redshift, not
Teradata
1. The most valuable feature of Redshift is that it is cloud-based and fully
managed. Although, Teradata has a Teradata Database Developer (Single
Node) a full-featured data warehouse software.
2. No need to worry about backup and restore as manual snapshots and
restore can also be done.
3. Backed up data (snapshot) is automatically stored in S3. No need to
worry about storing data in tape or any outside system.
4. Redshift has an excellent feature of loading data through COPY
command that too in the parallel mode where all nodes/slices can
participate together to make the performance faster.
5. Redshift performs automatic column level compression, and it suggests
compression mechanisms on all table columns (command is ANALYZE
COMPRESSION).
6. Due to the VPC feature in AWS, Redshift security is too tight and well
controlled.

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Features supported only by Teradata, not
Redshift
1. Teradata supports various features including Procedures, Triggers, etc.
2. Teradata has a column sequencing feature while Redshift doesn't.
3. Teradata provides various load and unload utilities i.e. TPT, FastLoad,
FastExport, Multiload, TPump, and BTEQ. You can use them depending
upon data volume, business logic, and leverage it in your ETL logic.
4. Teradata has a few visual utilities which Redshift should have such as
Teradata Visual Explain. In Redshift, you need to hit query to view Explain
plan.
5. Teradata supports MULTISET and SET tables while Redshift doesn't.
6. Teradata supports Macros but Redshift doesn't. Macros are a set of
predefined SQL statements logically stored in Database. Macros also
reduce LAN traffic.
Example:
CREATE​ ​MACRO​ ​Get_Sales​ ​AS​ ​(
SELECT​ ​SalesId, StoreId, StoreName, StoreAddress​ ​FROM ​Stores ​ORDER BY
StoreId;
);
Exec Get_Sales;
→ ​This macro execute command will retrieve all rows from Stores table.

15
Redshift Vs Teradata In A Nutshell
Items Redshift Teradata
Cloud perspective
Backup and restore
strategy
Data Load and
Unload
Table Storage
Fully managed Data Warehouse
over cloud.
Backups are automatically taken
care of through the snapshot
feature. Snapshots are stored
internally stored in S3, which is
highly durable.
Redshift leverages data load
through COPY command and
unload through UNLOAD
command. Using COPY
command, data is loaded
automatically so that all nodes
can participate equally for faster
performance.
Redshift follows columnar
storage format. If the query is hit
based on a specific set of the
columns or only on specific
column then it provides an
impressive performance. Hence,
aggregates are very fast in
Redshift as it leverages column
level hit.
Core Data Warehouse is not
over the cloud. Initial setup is
required by DBAs/Export.
Teradata can be scaled to run
over the cloud (AWS/Azure)
with pay-as-you-go model.
Teradata backup and restore
can be manual or automated
(using BAR) but data is stored
in an outside system.
In Teradata, we have separate
utilities to handle load/unload.
Teradata provides TPT,
FastExport, FastLoad, etc. They
can be leveraged accordingly
for your ETL/ELT.
Teradata follows row level
storage. Teradata requires a
proper indexing on columns so
that data can be stored
properly in AMPs. If indexes
are not proper or table hit is
done on non-indexed column
then it could cause
performance issue.

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Internal Storage
In Redshift, data is stored over
chunks of 1 MB blocks of each
column. Each block follows zone
mapping. Using zone mapping,
blocks stores minimum and
maximum value of that column.
In Teradata, the data storage is
managed by AMPs under
vDisks and data is distributed
based on hash algorithm (i.e.
based on index defined etc)
and data is retrieved
accordingly.
Referential
Integrity Model
Redshift tables do have Primary
Keys and Foreign Keys but it
does not follow enforcement.
You need to apply your logic
such that referential integrity
model is applied on Redshift
tables.
Teradata tables have Primary
Keys and Foreign Keys and it
follows enforcement.
Hence, it has an additional
overhead of doing reference
checks while processing.
Sequence Support
Triggers, Stored
Procedures
Visual Features
There is no concept of column
sequencing. If you want to create
a sequence on any column you
need to handle it
programmatically.
In Redshift, there is no concept
of Triggers or Stored Procedures.
Redshift is a part of AWS,
an integrated service. Entire
Redshift performance can be
monitored through AWS
console, Cloudwatch, and
automatic alerts.
You can define Sequence on a
column.
You can create Triggers or
Stored Procedures in Teradata.
It has few visual tools like
Teradata Visual Explain but
they are cluttered.
Max Concurrency
Maximum 15 concurrent queries. Runs more than 15 concurrent

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By default its concurrency is 5.
queries.
Macros Support No concept of Macros. Supports Macros.
NoSQL to Redshift
Feature
Although, Redshift cannot load
NoSQL data from other vendors
but it can load data from
DynamoDB.
No such feature supported yet.
Maximum Storage
Capacity
2 PB
(16*128 DS2.8xlarge ~ 2 PB)
Storage capacity of much more
than 2 PB of data.
Column
Compression
In Redshift, when the table is
created it automatically creates
default compression on all
columns. It also provides a
command called ANALYSE
COMPRESSION to help on
column compression.
In Teradata, you need to
specify column compress on
individual columns. You can
compress
up to 128 unique values per
column in a table.
Maximum Columns
Per Table
Maximum 1600 columns per
table.
Maximum 258 columns per
row.
Maximum Joins No limit as such. 64 joins per query block.
Data Warehouse
Maintenance/
Updates
Table Indexes
Redshift applies regular patches
and does automatic maintenance
inside maintenance window.
It does not have table index
concept but its performance is
In Teradata, DBAs need to take
care of all these activities
manually or through some tool.
Teradata does provide various
types of index i.e. Primary

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unaffected due to zone mapping
and sort key features.
Index, Secondary Index, etc.
Table partitioning
Redshift Spectrum has but
Redshift doesn’t.
Tables can be partitioned.
Fault Tolerance
Redshift is Fault Tolerant. In
case, there is any node failure,
Redshift will automatically
replace the failed node with the
replacement node. Although,
multi-AZ is not supported in
Redshift.
Teradata is also fault tolerant.
In case, there is a failover in
AMP, fallback AMP will take
over automatically.

19
Pricing and Effort Comparison
Redshift leads Teradata in effort and in-house pricing. Redshift is cheaper
and easier than Teradata. For Redshift, you only need to turn on the
cluster, set up security settings, few other options (maintenance window
period, snapshot enabling option, etc), and you are ready to go. This way
DBAs efforts get reduced.
However, in terms of storage, Teradata has upper hand because Redshift
cluster has limitations. However, in Redshift, we can still handle that
through S3 as it does not have any space limitation.
Remember, both Teradata and Redshift Data Warehouses are designed
to solve different purposes.
You can refer to ​Redshift​ and ​Teradata​ to know about pricing.

20
When and How to Migrate data from Teradata to
Redshift
There are various considerations that need to be made on whether to
migrate from Teradata to AWS/cloud.
1) How stable is your Teradata Warehouse?
2) How much is your Teradata data volume?
3) How complex is your Teradata data model?
4) How much is your current Teradata data latency?
5) How good is your Teradata RDBMS performance?
6) How many BI tools are you using on your Teradata
tables/views/cubes?
7) Are you using plenty of unsupported features of Redshift in
Teradata?
8) Will migrating your data warehouse from Teradata to Redshift
break your system?
9) Your budget of maintaining the Redshift and other key AWS
services post-migration.
If all conditions are satisfied, you easily migrate your data from Teradata
to Redshift. AWS provides a useful service called Data Migration Service
(DMS) and Schema Conversion Tool (SCT). Although, this pretty handy
service is not fully automated as some minor manual efforts are required.
Please refer to AWS documentation for migrating data from ​Teradata to
Redshift​.

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Summary
Choosing between Redshift and Teradata is a tough question to answer
as both are solving different purposes. Redshift performs analytics and
reporting extremely well. Since Redshift is a columnar base data
warehouse, its performance is really good when it comes to hitting the
table/view based columns and aggregate functions (sum, avg, count(*),
etc). As Redshift is a part of AWS service, it is integrated with all vital
AWS services. Hence you don't need to store millions of data in Redshift
alone as you can archive old data in S3. If required, you can leverage
Redshift Spectrum to build your analytics and reports on top of it. Stored
procedures can be handled through AWS Lambda Service. In terms of
age, Redshift is a comparatively newer data warehouse. Redshift is still
developing features which other key data warehouses offer.
On the other hand, Teradata is pretty matured and old. Teradata as an
RDBMS may not provide similar performance as Redshift unless it has a
properly designed data model, fully leveraged features (FastLoad,
Multiload, TPT, BTEQ, etc), and table/views are properly tuned. Although,
some established customers might be reluctant to migrate from Teradata
to Redshift. They can also look for the hybrid model option.
In conclusion, it is still an ongoing debate, both Redshift and Teradata
have its pros and cons.

22
ETL Challenges While Working With Amazon Redshift
Data loading is one of the biggest challenges of Redshift. To perform ETL
to Redshift, you would need to invest precious engineering resources to
extract, clean, enrich, and build data pipelines. However, writing complex
scripts to automate all of this is not easy. It gets harder if you want to
stream your data real-time. Data loss becomes an everyday phenomenon
due to issues that crop up with changing sources, unstructured & unclean
data, incorrect data mapping at the warehouse, and more.
Using a data integration platform like Hevo can solve all your Redshift
ETL problems. With Hevo you can move any data into Redshift in minutes
in a hassle-free fashion. Hevo integrates with a variety of data sources
ranging from SQL, NoSQL, SaaS, File Storage Base, Webhooks, etc. with
the click of a button.
Sign up for a ​free trial​ here or view ​a quick video​ on how Hevo can help.
About Author:
Ankur Shrivastava is a AWS Solution Designer with hands-on experience
on Data Warehousing, ETL, and Data Analytics. He is an AWS Certified
Solution Architect Associate. In his free time, he enjoys all outdoor sports
and practices.

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