Data migration between two different database systems always involves the conversion of data types [ SQL Server to Teradata ]. In this post, I will introduce the concept of data migration and show how to convert data types from Microsoft SQL Server to Teradata database types.
Table of Contents
Microsoft SQL Server is a relational database management system (RDBMS) that is used to store and manage data. It is designed to support a wide variety of data types and is commonly used for data storage, data analysis, and data management applications.
Some key features of SQL Server include:
- Support for structured, semi-structured, and unstructured data: SQL Server supports a wide range of data types, including traditional data types such as numbers and strings, as well as more complex data types such as spatial data, JSON, and XML.
- Scalability and performance: SQL Server can scale up to handle large amounts of data and high levels of concurrency, and includes features such as in-memory OLTP and columnstore indexes to improve performance.
- Security: SQL Server includes a range of security features such as data encryption, authentication, and access controls to protect data and ensure compliance.
- Integration with other Microsoft products: SQL Server can be used in conjunction with other Microsoft products such as Azure, Power BI, and Visual Studio, which makes it easier to integrate data with other applications and tools.
Teradata is a relational database management system (RDBMS) that is designed to handle large volumes of data and support high levels of concurrency. It is commonly used in data warehousing and business intelligence applications, and is known for its scalability and performance.
Some key features of Teradata include:
- Massively parallel processing (MPP): Teradata uses a distributed architecture that allows it to scale horizontally by adding more nodes to a cluster. This makes it well-suited for handling large volumes of data and high levels of concurrency.
- Columnar storage: Teradata stores data in columns rather than rows, which can improve query performance for certain types of queries.
- Data warehousing and business intelligence capabilities: Teradata includes features such as data modeling, ETL, and reporting tools that are useful for data warehousing and business intelligence applications.
- Integration with other tools: Teradata provides connectors and integrations with a range of tools and platforms, including Hadoop, Spark, and Azure.
SQL Server Vs Teradata
- Architecture: SQL Server uses a client-server architecture, where the database engine runs on a central server and clients connect to it to access data. Teradata uses a massively parallel processing (MPP) architecture, where the database is distributed across multiple nodes and can scale horizontally by adding more nodes to the cluster.
- Data storage: SQL Server stores data in rows, while Teradata stores data in columns. This can affect how data is stored and retrieved, and can impact query performance.
- Performance: Both SQL Server and Teradata are designed to handle large volumes of data and support high levels of concurrency. However, Teradata’s MPP architecture and columnar storage may make it better suited for certain types of workloads, such as data warehousing and business intelligence applications.
- Integration with other tools: Both SQL Server and Teradata offer a range of connectors and integrations with other tools and platforms. However, SQL Server has closer integration with other Microsoft products such as Azure and Power BI.
Ultimately, the choice between SQL Server and Teradata will depend on your specific needs and requirements. It is important to carefully evaluate the features and capabilities of each system to determine which one is the best fit for your use case.
SQL Server To Teradata Migration Approaches
Data migration is one-time transfer of data from one IT system, database to another repository. Usually it is a one-way trip, involves many departments of the company and entails the termination of the existing database system.
Migrations of data sets to new databases are always the result of requirements for current IT systems. The change is to ensure better query performance, easier system scalability, and more efficient management of data sets.
There are a few different approaches you can take to migrate data from SQL Server to Teradata. Here are a few options:
- Use a third-party tool: There are several commercial tools available that can help you migrate data from SQL Server to Teradata. These tools typically provide a user-friendly interface for setting up and configuring the migration, and may also offer additional features such as data transformation, scheduling, and monitoring.
- Use SQL scripts: You can write SQL scripts to extract data from SQL Server and load it into Teradata. This can be a good option if you have a small amount of data to migrate, or if you want to have more control over the migration process. To do this, you can use SQL commands such as
INSERT INTO, or
CREATE TABLE AS SELECTto extract data from SQL Server and load it into Teradata.
- Use the Teradata FastLoad utility: FastLoad is a Teradata utility that can be used to load large amounts of data into Teradata tables quickly. To use FastLoad, you will need to create a data file containing the data you want to load, and then use FastLoad to load the data into a Teradata table.
Teradata Data Types
Each table attribute is associated with a specific data type that determines what values will be stored in it.
The table below presents the most common data types in the Teradata database along with their size and range of values:
|Data types||Size (byte)||Range values|
|BYTEINT||1||-128 – +127|
|SMALLINT||2||-32768 – +32767|
|INTEGER||4||-2 147 483 648 – +2 147 483 647|
|BIGINT||8||-9 233 372 036 854 775 808 – +9 233 372 036 854 775 807|
|DECIMAL (m,n)||1 – 16|
|NUMERIC||1 – 16|
|CHAR (x)||variable||1 – 64 000|
|VARCHAR (x)||variable||1 – 64 000|
|TIME||6 or 8||hh:mm:ss[.ssssss]|
|TIMESTAMP||10 or 12||yyyy-mm-dd hh:mm:ss|
Data types in IT systems practically are always differ – some are more detailed allowing for more precise specification of the attribute as MONEY, and others more general as DECIMAL. The most important thing is to choose the right data type with the appropriate size.
When converting data types between databases, always remember:
- For alphanumeric data, always check the maximum field length and try to choose the length taking into account the data that will appear in the future
- For numeric data, always check the smallest and largest attribute value
- If the precision (n) for the DECIMAL attribute is 0 consider using BYTEINT in Teradata, SMALLINT, INTEGER or BIGINT depending on the maximum values of the attribute
- Always use the smallest possible data type for flags of type 0 or 1 – BYTEINT
- When specifying the data type for the same attribute in different tables, always use the same data type and the same precision (especially if the attribute is used in table joins)
The following table shows the conversion of data types for the most common SQL Server database types:
|SQL Server data types||Teradata data types||Comments|
|TINYINT||SMALLINT||if all values are below 127, use BYTEINT|
|DECIMAL||DECIMAL||if the precision is 0, eg DECIMAL (3,0) consider BYTEINT to BIGINT|
|CHAR (x)||CHAR (x)|
|VARCHAR (x)||VARCHAR (x)|
That’s all about: SQL Server to Teradata: Data migration and conversion of data types from MS!
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