Introduction
I'm Chen from the Lakehouse Department of the GLB Division. Based on a report by Mr. Nagasato, who is participating in the Data + AI SUMMIT 2023 (DAIS) held in San Francisco, "dbt Labs | Leveling Up SQL Transformations in the Lakehouse with dbt" Here's an overview of the session "Level Up Your SQL Transformations".
Hosted by dbt Labs, this session showcased the benefits of data analysis and transformation using Databricks and dbt and explained how to improve productivity and collaboration between teams. If you are interested in data analysis and transformation, use Databricks or dbt, or are involved in data analysis and transformation in a team, you will find this very interesting.
Familiar environment provided by Databricks
Databricks provides a familiar environment for those who are familiar with SQL and can improve productivity and collaboration between teams. This talk introduced the benefits of data analysis and transformation using Databricks and DBT.
Features of Databricks
- Easy to use for people familiar with SQL
- Improve productivity and cooperation between teams
- Provides powerful tools for data analysis and transformation
High quality data pipeline with dbt
dbt provides a high quality data pipeline without infrastructure overhead and offers a variety of options for data transformation. This allows data analysis teams to work with data more efficiently.
Features of dbt
- Provides data pipelines without infrastructure overhead
- Diverse options for data conversion
- Improve the efficiency of your data analysis team
Powering Lakehouse with Databricks and dbt integration
Combining Databricks and dbt allows data analytics teams to take their SQL transformations to the next level within Lakehouse. This improves the quality of your data and the accuracy of your analysis, enabling better decision making.
Leveling Up SQL Transformation at Lakehouse
- Improve data quality by linking Databricks and DBT
- Improved accuracy of analysis
- Support for better decision making
Through this session, the benefits of data analysis and transformation with the combination of Databricks and dbt were revealed. You can expect to improve not only productivity and collaboration between teams, but also the quality of your data and the accuracy of your analysis. I would like to continue to pay attention to the evolution of data analysis using Databricks and dbt.
New Possibilities for MKDX Benchmarking and Data Analysis
In addition to the above, we will introduce the benefits of data analysis and transformation leveraging Databricks and dbt, and explore ways to improve productivity and collaboration between teams. A new data analysis project called the MKDX Benchmark was highlighted as an example.
What is MKDX Benchmark?
MKDX Benchmark is a new benchmark for analyzing statistical data of the popular game "Mario Kart 8 Deluxe". This project utilizes dbt's SQL data warehouse to leverage its modularity and transformation capabilities to work with data collected through web scraping.
Leveraging dbt and Databricks
dbt is an open source tool for data transformation using SQL on data warehouses. Databricks is a data analytics platform that integrates big data processing and machine learning. Combining these two technologies can streamline the data analysis process and promote collaboration between teams.
Streamline the data analysis process
The MKDX benchmark project has realized the following streamlining of the data analysis process.
- Data collection by web scraping
- Data conversion and modularization using dbt
- Data analysis and visualization using Databricks
This allows data analysis teams to process data quickly and efficiently and share their findings.
Promote collaboration between teams
The combination of dbt and Databricks encourages collaboration between teams. Specifically, the following effects can be expected.
- Standardized data transformation makes it easier to work with data across teams
- Modularization increases reusable components and improves development efficiency
- Leverage Databricks sharing features to easily share analysis results
These effects enable data analysis teams to collaborate more smoothly and increase productivity.
Summary
In this session, the benefits of data analysis and transformation utilizing Databricks and dbt were introduced. Successful use of these tools has been shown to streamline the data analysis process, promote collaboration among teams, and achieve higher productivity. It is expected that the use of Databricks and DBT will spread in the field of data analysis. Let's keep an eye on the evolution of such technology in the future!
Conclusion
This content based on reports from members on site participating in DAIS sessions. During the DAIS period, articles related to the sessions will be posted on the special site below, so please take a look.
Translated by Johann
Thank you for your continued support!
