During the past few years, major strides in managing voluminous amounts of data have been made by Teradata. In the present times, the volume, velocity, and veracity of data analytics have witnessed a steep rise. As such, data analytics is now carried out with the aid of tools in the cloud environment. By drawing a line of demarcation between the storage and the computational systems, Snowflake has not only ended the Teradata debate but has enabled the creation of a nouveau model in the data warehouse system. This warehouse analyzes, captures, and creates data in a way that was not possible with existing parameters in the previous years. So, Teradata to Snowflake migration became a gradual consequence.
Motivation to migrate
There are many reasons why one needs to migrate to Snowflake. First things first, Snowflake provides a comprehensive platform for data migration which the traditional data technology platforms fail to provide. This migration also includes the on-demand delivery of services with minimum management specifications ever possible. New data sources are created, and workload migration becomes a possible consequence. One more motivation for Teradata to Snowflake migration is that the latter provides such scalability which is impossible in the former.
Advantages and quality
Snowflake has all the latest technology and qualitative environment of a data warehouse which is needed in the cloud ecosystem. Snowflake brings down the complexities and boosts performance, leaving no room for the infrastructure to collapse. Snowflake also meets all your data needs and creates a singular resource that carries out critical analytics from raw and unstructured data. It provides access to an unlimited number of users without affecting performance. This platform uses the customized payments model, which means that you need to pay only for the services which you use. Other features of Snowflake include the lift and shift model and various tools which are at the core of the business solutions today.
Lift and shift model compared to the stages model
One of the most common dilemmas that companies are confronted with is whether to use the stages data approach or carry out a single bulk operation of data transfer using lift and shift model. There are separate factors which need to be considered for the lift and shift model. Lift and shift model is preferred when we have integrated data in the existing warehouse. This model is also ideal when we are previously using a well-structured and designed data using ANSI SQL. The stages model is ideal when our platform has many independent data applications which need to be moved separately. In addition to this, critical and sensitive data processes are also executed with this type of a model.
Distinguishing features
Snowflake completely does away with the need of primary indexes. In the architecture of snowflake, data is not distributed across nodes. So, the process of computation becomes efficient. Other distinguishing features of Snowflake environment include workload management, statistics collection, capacity building and an isolated environment for testing. Precision of disaster recovery is also a distinguishing feature of snowflake which puts it apart from Teradata.
Footnotes
All these features suggest that the migration from Teradata to Snowflake is now inevitable!
