Dlink dbt 120

Welcome to the dbt Developer HubYour home base for learning dbt, connecting with the community and contributing to the craft of analytics engineering Popular resourcesWhat is dbt?dbt enables data practitioners to adopt software engineering best practices and deploy modular, reliable analytics code.Getting started guideLearn how to set up dbt and build your first models. You will also test and document your project, and schedule a job.DocsDiscover everything dbt has to offer from the basics to advanced concepts.Supported data platformsdbt connects to most major databases, data warehouses, data lakes, or query engines.Community spotlightThe Original dbt-athena MaintainersThe original dbt-athena Maintainers is a group of 5 people—Jérémy Guiselin, Mattia, Jesse Dobbelaere, Serhii Dimchenko, and Nicola...Read moreThe latest from the Developer BlogGetting Started with git Branching Strategies and dbtMarch 10, 2025 · 31 minute readHow to configure dbt Cloud with common git strategiesRead moreParser, Better, Faster, Stronger: A peek at the new dbt engineFebruary 19, 2025 · 4 minute readRemember how dbt felt when you had a small project? You pressed enter and stuff just happened immediately? We're bringing that back.Read moreThe key technologies behind SQL ComprehensionJanuary 24, 2025 · 15 minute readThe technologies that power the three levels of SQL comprehension. Read moreThe Three Levels of SQL Comprehension: What they are and why you need to know about themJanuary 23, 2025 · 8 minute readParsers, compilers, executors, oh my! What it means when we talk about 'understanding SQL'.Read moreWhy I wish I had a control plane for my renovationJanuary 21, 2025 · 4 minute readWhen I think back to my renovation, I realize how much smoother it would've been if I’d had a control plane for the entire process.Read moreTest smarter not harder: Where should tests go in your pipeline?December 9, 2024 · 8 minute readTesting your data should drive action, not accumulate alerts. We take our testing framework developed in our last post and make...Read moreFrom the dbt CommunityJoin the communityConnect with data practitioners from around the world.Become a contributorHelp build the resources the community uses to solve hard problems.Open source dbt PackagesTake your dbt project to the next level with community built packages.Use dbt like a proBest practicesLearn battle tested strategies for analytics engineering best practices.Community forumGet help and swap knowledge in the async forum.Online coursesStructured video courses to give you a deep dive into analytics engineering topics.

Integrate data from dbt Cloud to Snowflake using Matillion Our dbt Cloud to Snowflake connector transfers your data to Snowflake efficiently, keeping it up-to-date without requiring manual coding or managing complex ETL scripts. Overview & Benefits What is Snowflake? dbt Cloud Data Analytics Similar Connectors Using the Connector Get Started What is dbt Cloud? dbt Cloud is a managed service that builds on dbt (data build tool), which is a popular open-source framework for transforming raw data into analytics-ready datasets using SQL. The primary purpose of dbt Cloud is to simplify and streamline the orchestration, development, and monitoring of data transformation workflows. PurposeOrchestration: Automates the execution of transformation workflows, ensuring data is consistently prepared across different environments. Development: Provides an integrated development environment (IDE) tailored for writing and testing dbt models, making it easier for teams to collaborate on SQL-based transformations. Monitoring: Offers built-in logging, alerting, and documentation capabilities to keep track of the transformation process and ensure data quality.BenefitsEase of Use: Simplifies setup and ongoing management of dbt projects with a user-friendly, web-based interface. Collaboration: Enhances team collaboration with features like version control, code reviews, and environment management. Scalability: Handles large-scale data transformation workloads efficiently and reliably. Integrated Development Environment: Provides a dedicated IDE that streamlines the workflow for developing dbt models and macros. Job Scheduling and Automation: Automates repetitive tasks with customizable scheduling and rapid deployment. Comprehensive Monitoring: Offers robust monitoring tools, including alerting and logging, to ensure data pipeline health and facilitate troubleshooting. Documentation and Lineage: Automatically generates documentation and visualizes data lineage to improve data governance and understanding.In summary, dbt Cloud enhances the capabilities of dbt by providing a managed, scalable, and collaborative environment for developing and orchestrating data transformation workflows, benefiting data teams focused on accelerating analytics and maintaining data quality. What is Snowflake? Snowflake

Organization.Monitoring: DBT provides a way to monitor and track the state of data models, ensuring they are up to date.Version Control: DBT supports version control of data models, making it easier to manage changes and revert to previous versions if necessary.Pros:Improved Data Quality: DBT helps to improve the quality and consistency of data analysis.Efficient SQL Code: DBT provides a way to write, manage, and run efficient SQL code for data analysis and data integration.Scalable and Maintainable: DBT provides a scalable and maintainable solution for data modeling.Version Control: DBT supports version control, making it easier to manage changes and revert to previous versions if necessary.Comprehensive Suite of Tools: DBT provides a comprehensive suite of tools to ensure data quality and consistency across the organization.Cons:Steep Learning Curve: DBT can have a steep learning curve, especially for those unfamiliar with SQL and data modeling.Complex Setup: Setting up and configuring DBT can be complex and may require a strong understanding of SQL and data modeling concepts.Dependent on SQL: DBT is heavily dependent on SQL, which may not be ideal for those who prefer a more graphical or visual approach to data modeling.Resource-Intensive: DBT can be resource-intensive, especially for large and complex data models, requiring significant computing resources.12. Apache HiveApache Hive is an open-source data warehousing and analytics framework built on top of Hadoop. It provides a SQL-like interface for querying and manipulating large data sets stored in Hadoop Distributed File System (HDFS) or other storage systems. Hive is designed for batch processing and allows

Compete Registration. You should now be redirected to your dbt Cloud account, complete with a connection to your Snowflake account, a deployment and a development environment, and even a sample job.To help you version control your dbt project, we have connected it to a managed repository, which means that dbt Labs will be hosting your repository for you. This will give you access to a git workflow without you having to create and host the repository yourself. You will not need to know git for this workshop; dbt Cloud will help guide you through the workflow. In the future, when you're developing your own project, feel free to use your own repository. This will allow you to play with features like Slim CI builds after this workshop. Now let's set up our dbt project. Click on the hamburger menu on the top left side and click on Develop. This will spin up your IDE (Integrated Development Environment) where you will be developing your dbt Project.After the IDE loads, click Initialize your project to set up your dbt project. Once you click on it, dbt will generate our starter project with the core files and folders. You should see new files and folders created in your file tree. These are all the files and folders you will need for a dbt project. Take a look around, especially at the dbt_project.yml and what's in the models directory.Click Commit. Enter a commit message, and then click Commit again to commit your work to your master branch.Commit messages should always be indicative of the work you are saving. This helps you create a reference point for the future in case of auditing and debugging. By committing, you are saving to a remote branch in Github. This will also be the only time you will save straight to your master branch (which is the main branch). We always want to create a degree of separation from development work and your production branch.Click create new branch to check out a new git branch to start developing. Name the branch "dbt_snowflake_workshop" and click Submit. UI walkthrough (SA will walk through during the workshop). Post workshop, users can look at this video for the walkthrough.Now let's validate that everything was initialized correctly by running the sample models that come with the starter dbt project.Type in dbt run on the command line at the bottom, and click Enter. The command line is where you will be entering in dbt commands to execute dbt actions.If you want to see the actual code being executed, you can go into the ‘Details' tab next to Summary and look through the logs. Here you can see that dbt is writing the DDL for you, allowing you to focus on just writing the SQL select statement.The output should look like below, confirming dbt was able to connect to Snowflake and successfully execute the sample models in the models folder:If you want to see what is executed against Snowflake, click into one of the model

Use macros from a package and see how those macros help us write SQL quickly.Now we need to apply another operational macro. This time, this macro will add a query tag for every dbt run in the Snowflake history. To do this, we'll create a file in the "macros" folder called query_tag.sql.Copy and paste the following code. This provides the ability to add an additional level of transparency by automatically setting Snowflake query_tag to the name of the model it is associated with.{% macro set_query_tag() -%} {% set new_query_tag = model.name %} {# always use model name #} {% if new_query_tag %} {% set original_query_tag = get_current_query_tag() %} {{ log("Setting query_tag to '" ~ new_query_tag ~ "'. Will reset to '" ~ original_query_tag ~ "' after materialization.") }} {% do run_query("alter session set query_tag = '{}'".format(new_query_tag)) %} {{ return(original_query_tag)}} {% endif %} {{ return(none)}}{% endmacro %}Click "Save".Now, do a dbt run again on the command line.To see where the query tags are applied, go to the Snowflake UI, click the "History" icon on top. You are going to see all the SQL queries you've run on your Snowflake account (successful, failed, running etc.). and clearly see which dbt model a particular query is related to.Update the Filter to the user is PC_DBT_USER and the status is Succeeded to see the same view as the screenshot above, you can also remove extra columns by clicking on the arrow after hovering over a column, hovering over "Columns" and unchecking any columns you don't want.Now we are going to install a dbt package. A dbt package is essentially a dbt project that you can install onto your own dbt project to gain access to the code and use it as your own. Many of our packages are hosted on the dbt Packages Hub. In our lab, we are going to demonstrate how to use some useful macros in the dbt_utils package to write some complex SQL. To install it, create a file called packages.yml at the same level as your dbt_project.yml file.Copy and paste the following code into file packages.yml.packages: - package: dbt-labs/dbt_utils version: 0.8.0Click Save.Now we are going to install the package. By running dbt deps, you tell dbt to install the packages. With a successful run, you can check out the dbt_modules folder to see what packages you have installed and the code that is now available to you. Last thing to do before we save our work. Remove the example subdirectory configuration in the dbt_project.yml file and delete the example folder with the models in it.Click Commit to commit your work! Now we start to get into the fun stuff. In the next few sections, we are going to build our dbt pipelines. This will include transformations that define these these areas of interest:Stock trading historyCurrency exchange ratesTrading booksProfit & Loss calculation Setting up our SourcesNow let's head back to the dbt Cloud IDE. We are going to start building our pipelines by declaring our dbt sources. Create a knoema_sources.yml