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Data integration is the act of bringing together data from several sources to create a unified picture of data with useful information and insights. ETL/ELT is a technique for moving data into a data warehouse. A common technique for moving data from a source database to a destination database is known as Extract, Transform, and Load (ETL). As we all know, data integration entails a number of different roles.
Traditional and Snowflake ETL solutions
ETL has typically been the data processing approach. For data warehousing and analytics, ELT is increasingly becoming the primary processing option. With technologies like Snowflake, companies are striving to update their data systems in order to give real-time insights. In addition to such initiatives, they should think about implementing a contemporary data integration strategy.
Snowflake is Software as a Service (SaaS) service that delivers an analytic Data Warehouse on cloud platforms like Amazon Web Services (AWS), Microsoft Azure, or metro 2 compliance. Snowflake is unique in that it features real decoupled storage and computing. The same database can be accessed by many, independent computational resources. This implies you may run an almost infinite number of concurrent workloads on the same single copy of data without interfering with other users’ performance.
Snowflake has a consumption-based pricing approach, with separate payments for computation and storage. Separate departments can conduct different size computations and bill the appropriate amount to each group effortlessly. Snowflake’s storage resources can be grown independently of its processing capabilities, allowing users to load and unload data without having to worry about ongoing queries or workloads.
Though both ETL and ELT procedures may be used to perform data integration (data preparation, migration, or movement) in Snowflake, ELT is the preferred method inside the Snowflake Architecture.
What should you consider?
Because of its flexibility and time-saving qualities, the ELT process is quickly becoming a popular approach. ELT provides an immediacy and accessibility that ETL cannot match, but the new cutting-edge performance implies that enterprises will experience growing pains as they reengineer their data operations. The teething pains will quickly subside as the ELT strategy becomes more of a norm for developers and organizations. Although you do not need to design elaborate procedures as you would for ETL, data dependability may be compromised.
Scalability of performance
With the help of Snowflake Remote Support from Big Companies like Ducima Analytics, you may have a variety of warehouses of various sizes. Cost is linked to computation performance and use. You may start and stop the warehouses as required to keep expenses under control. A built-in auto-suspend mechanism helps to keep costs down right away. Consider the following scenario: if your morning data load is taking too long, you can grow up to a larger warehouse. You may stop the warehouse sooner and experience enhanced data load performance for low or minimum expenditures since the data will load quicker. Improving peak data load performance in a historical system would be more costly and time-consuming.
Furthermore, if all of your users run their data analysis reports and dashboards on Monday mornings, the warehouse dedicated to that use case may be programmed to auto-scale. When the peak demand comes, this scales out more compute capacity, and then eliminates those additional resources as the load decreases.
