How Data Build Tool (DBT) Speeds Up Queries
SQL forms the significant foundation of analytics. Data Analysts use SQL to manipulate, understand, access, read, and analyze the data to derive constructive business insights and strategies. However, organizations with large data often face situations where the SQL queries take a long time to execute, which affects the performance of the system. Although optimizations are an effective way to reduce the query runtime, instances when multiple users are running complex queries at the same time the performance gets impacted even if the queries are optimized. A great way to cope up with such scenarios is using data transformations powered by a data build tool.
What are Data Transformations?
Data transformation involves refining raw source data from a warehouse through SQL operations such as cleaning, joining, aggregating, and applying business logic to generate essential datasets. These transformed datasets can be structured as views or models OR tables within the warehouse. Charts and dashboards can then be developed using these datasets or they can also be used by development teams to create data applications. Implementing these transformations effectively requires robust tools like the DBT (Data Build Tool), which focuses solely on enhancing the speed and simplicity of these processes.
There are many tools to help your organization transform data and make it accessible for business users. One such tool is the DBT (Data Build Tool)—focuses solely on making the process of transforming data simpler and faster.
What is a DBT(Data Build Tool)?
DBT (Data Build Tool) is a framework that enables data transformation using simple SQL statements, making it more efficient and reliable. DBT simplifies the process of transforming data within data warehouses by allowing data analysts and engineers to leverage SQL select statements.
DBT provides an effective method to incrementally materialize the transformation models as tables in the warehouse, enhancing the query performance and overall efficiency, giving analysts the power to access the full spectrum of data engineering capabilities, enabling them to create and manage end-to-end analytics workflows effectively.
A comprehensive guide of why and when you can use DBT can be found here:
https://datazip.io/blog/why-organizations-should-be-using-dbt-a-comprehensive-guide/
What are Transformation Models?
Transformation Models are views OR tables which use simple SQL to transform the raw data and materialize it in the form of views OR tables in the warehouse. Let us assume that we have to calculate a metric which can be arrived at using multiple large tables joined together.
You can understand more about Dimensional Data modeling here
https://datazip.io/blog/guided-approach-to-dimensional-data-modeling/
Use Case: Considering that we have to calculate a metric which shows Customer region wise & ship mode wise MOM Revenue. For calculating the above metrics we need the daily order data and the region data of the customers who have placed their orders.
Assuming the above data is available in two different tables: 1. Lineorders 2. Customers
The lineorders table has around 60 million rows and 17 columns whereas the customers table has approx 9 million rows. In order to derive the above metric we will have to perform a join on the lineorders table and the customer table. Since both the tables are quite large a normal SQL query would lead to scanning the entire tables leading to more RAM usage and hence more time in executing the query.
SQL Query without Transformations:
This query takes about 30 sec to execute the results.
An Efficient way to solve the problem of reducing the query time is to create a transformed table including only the required data columns from the lineorders & customers table and creating a single transformed materialized table using a data build tool.
Instead of querying two separate tables and performing a join, the materialized transformed table can be used and suitable aggregations can be done on it to achieve the desired metrics.
This is how we would create the transformed table using a data build tool:
When we query on the new transformed table the query fetches the same result in just 12 secs reducing the query time by almost 40%.
This example shows how transformation models can make queries run faster, especially when you use a data build tool.
You can use the same approach in more complex situations to improve query speeds, boost system performance, and save on RAM. If you're interested in learning about different methods of speeding up queries in Clickhouse and solve memory errors, read more on this link:
https://datazip.io/blog/solve-memory-limit-errors-and-speed-up-queries-in-clickhouse/
About Datazip
Datazip is an AI-powered Data platform as a service, giving the entire data infrastructure from ELT (Ingestion and transformation framework), storage/warehouse to BI in a scalable and reliable manner, making data engineering and analytics teams 2-3x more productive.
Ready to revolutionize your data warehousing strategy? Contact us at [email protected] to learn more about how Datazip can benefit your organization.
