Dbt notebook

Dbt notebook DEFAULT

Oh, my dbt (data build tool)

All my life, I was working with data. Somehow it sounds dramatic when I put it like that. Basically, I've done some analysis and basic work with SQL as a Business analyst, but nothing where I’d need templating. So-called BI career I started in 2013. Being a consultant and working mostly with MSSQL on multiple similar projects, it would have been a blessing to have something like dbt (or at least to know about Jinja at that time…); let’s write it off as a lack of experience.

Funny that I tried dbt only now. If I’m honest with you — I’ve been using it for ~month, so keep in mind that I’m not a pro, just spreading the knowledge and sharing what I’ve found. You can find many other medium articles on some specifics or go straight to the source of dbt.

First of all, for this to work, you’d need Docker. If you’re not familiar with docker, I will promote my older blog post I wrote some time ago about it. When working in docker-created environments, I prefer to use VSCode with its dev container option, where it basically creates an isolated environment with all my configs, mounts, etc. If you make any changes to the existing docker image, you can choose the option rebuild image, and it will compose it and open it for you with all changes. Super handy if you’re developing things so that you can skip manually doing docker-compose.

In my docker image, I’ve created a specific docker-compose file with two components — simple postgres:13-alpine and python 3.8. Choosing python 3.8.11 over 3.9 — had some issues trying to install dbt because of compatibility issues. I’m also using the mount option in my docker-compose file to pass the proper profiles.yml file for this specific project.

Postgres Dockerfile:

FROM postgres:13-alpine
ENV POSTGRES_PASSWORD=nopswd
ENV POSTGRES_DB db
COPY init.sql /docker-entrypoint-initdb.d/

In the init.sql file, I just created a database named db.

Python Dockerfile:

FROM python:3.8
COPY requirements.txt requirements.txt
RUN pip install -r requirements.txt

Nothing fancy in requirements, just the dbt library.

If you have already a production environment with dbt and you’re setting up a local one — always use the same dbt version as you have in production. Had trouble on dbt run, but my colleagues didn’t. Root cause — everyone was using 0.19.0, and I installed the latest at that time 0.19.2 and some compatibility issues occurred for dbt deps we had in the packages.yml file.

Docker-compose, as I mentioned, has some more things, but nothing fancy:

You might be wondering why I’m opening the 8001 port — it’s needed for some dbt feature you’ll see later on.

Ok, what is this dbt, you might be wondering. Basically, it’s an amazing tool to ease your transformation part in your ELT flow give you data lineage, documentation, and full control on data refreshes if some underlying data changes in one of the models somewhere in the middle. I really don’t want (and usually don’t like) to go to product details since I’m a more technical person, not a product one.

Ok, so there are a couple of important files in dbt.

  • profiles.yml — file where you set up all connections and how you’re going to use them
  • dbt-project.yml — specific configuration for specific dbt project you have this file in.

Let’s go over the profiles.yml file:

We have to have a default profile; this will be where everything is run if nothing else is specified. Different profiles will allow you to easily test pipelines on different environments (i.e., test and prod):

# Running on default:
dbt run# Running on prod:
dbt run --profile prod# Running on default with specified profile:
dbt run --profile default

After playing around in VSCode opening my folder in the development container, it’s interesting to see if all works as intended.

As you see, we have one error on dbt_project.yml. Let’s fix it.

For simplicity and keeping the original dbt structure, we can initialize it. To do this, let’s run this command:

dbt init MY_DBT_PROJECT_NAME

Now we can see what’s the structure dbt expects us and how it works with:

Let’s check if everything else is working from this folder and properly created profiles.yml

Great success! Our environment is fully functional and ready for us to check all things out.

Let’s try dbt run on the default profile:

We see that we have two models (which correspond to two files named my_first_dbt_model.sql and my_second_dbt_model.sql), but what are these tests? Where do they come from? Let’s dig deeper into the model's folder.

We can see we have schema.yml file with contents

We can see that we have two columns described plus tests — a column has to be unique and not null.

I found that my colleagues are creating a yml file per each model. In my opinion, this is a better option:

  • visually looks more clear
  • no merge conflicts because, most likely, there will be one developer per one model!

If we’d look at the queries their straightforward. Creates a table with 1 and null, creates a view out of the first table where id = 1. But wait — our tests didn’t say that we failed. We have a null value! That’s because it doesn’t have any data to test upon. So after we run our model, we need to test it.

To run tests:

dbt test --model example

Output in the console will look like this:

Clearly, we can see that there are some issues on our end and, we need to fix them.

The fix is easy. Let’s switch from null to some number and test again. We'd still see the same state if we’d run directly “dbt test” after the fix. We didn’t run the model, so underlying data didn’t change. We need to run and test it.

Hooray, we just fixed and ran our models successfully!

If we’d run dbt run on both dev/default and prod, we’d see in DB all of this

Target folder

After our dbt run, we had this folder created. Its contents:

For me, the interesting files are in the compiled/run directory. If we go down the rabbit hole, we can find our SQL queries parsed.

We also could compile our files by executing:

dbt compile

Run would create or update files in compiled and run folders. You’ll have tests SQL compiled as well, so you can understand what was being run in your specified tests.

Logs

If any issues occur and it’s not quite clear what it is - check logs/dbt.log. i.e., At work, I got “Database Error: permission denied for database X.” I have no clue what permissions I was lacking. I got a link to debugging page of dbt, and my colleague said to check the logs. From there, I found what permissions I was missing.

Incremental model

Let’s imagine we have a situation where our data residing in DB is big, and we want to add incremental load. Generically we’d do one script if a table exists — create it from scratch, else — insert and (or) update it. So basically, we have repetitive parts of code, and we have to maintain it in two places. It doesn’t comply with DRY (Don’t Repeat Yourself). Luckily dbt has an amazing feature like an incremental load. For this, we’re going to create an additional source table using Mockaroo. I’ve executed 01_mock_users_data.sql on my local Postgres database. I also made a small change and converted the created_at column to be a timestamp column instead of a date.

Created a simple model to use is_incremental macro:

If we’d run it now and check target/run:

create table "db"."dbt_dev"."mock_users"
as (
select * from "db"."operational_db"."mock_users_data"
);

Let’s run 02_more_mock_users_data.sql and do dbt run again. In target/run, we can see different outputs!

select * from "db"."operational_db"."mock_users_data"
-- this filter will only be applied on an incremental run
where created_at >= (select max(created_at) from "db"."dbt_dev"."mock_users")

Though nuance here that it will run exactly by filters you specified. The first run will be for ALL history; the next run will be for only new rows. The initial query might not even finish or encounter some other issues along the way (timeout, some hard limits on query run time, etc.). So you could go around and create an upper bound filter where you’d take only a couple of days/weeks/month and easily refresh it like this in several batches. Though it’s tedious, and you’d have to run it manually to catch it up.

Macros + insert_by_period

Disclaimer: insert_by_period works with Redshift only, dbt-vault created vault_insert_by_period works on Snowflake. So basically, I’m just explaining my journey what I tried and checked along the way.

I mentioned in incremental load “Macros,” you might wonder what it is? It’s some custom code, which is executed to add some missing functionality or more complex logic. I.e., mentioned before a tedious incremental load. In our case is a simple conditional insert that would load our initial data in multiple batches. You can check it out in the original discussion about this macro here. All in all, it’s bundled in the dbt-utils package. We can import by specifying it in the packages.yml file. Version 0.7.0 wasn’t compatible with my dbt version of 0.19.2 (asked for 0.20, which is only a release candidate at the moment this blog post was being written), so I used 0.6.4.

and we can install dependencies with

dbt deps

If we’d follow all the information for the version for our Postgres use case, it won’t work, since as it’s written in the comments - it’s suited for redshift only! After this, I went into the rabbit hole, checking dbt-vault, making some adjustments, and creating my own macro using comments in GitHub. But I guess I’m too new to macros, an advanced topic, and I couldn’t make it work. I will have to dig deep on this later.

Snapshot model

The name of it doesn’t really explain what it does. At least to me, a snapshot means the current state of the data. Though in the dbt case, if we create a snapshot model (they suggest putting it in the “snapshots” folder), we will have SCD type 2 (by the way, I wrote an article on SCD2 on spark some time ago, which covers what’s an SCD).

So let’s use the same mocked users data for this example. Let’s add the updated_at column and make it match to created_at column (03_update_at.sql). Let’s follow the basic example from dbt docs and run dbt snapshot. We can see how the snapshot looks like (only interested in newly added columns):

We can see that we have dbt_scd_id and dbt_valid_from and dbt_valid_to, corresponding to the changes. Let’s execute 04_change_some_names.sql and run dbt snapshot.

Ok, so basically, we just set up what’s unique, and dbt took care of the rest. Now that would have been handy many times for me. Looking in the target/run/snapshots folder, we can see our snapshot code was generated for us too!

So basically, we can see that it created a temporary table and then made all comparisons for us!

Generate docs

Data lineage and documentation. If you specified all relevant metadata in your yml files and used references to models and sources, you can generate documentation!

dbt docs generate

This method will generate a catalog.json file in your target directory. To check how it looks on the web:

dbt docs serve --port 8001 // or any other port you prefer

If we’d click on the greenish icon bottom right, we’d see lineage!

Keep in mind that here I show basics. Tons of things are on the official dbt page (dbt-docs page)!

So we covered most of the basic things (I found out an area of interest -> macros). Strongly suggest to anyone who’s working with the ELT approach to try out dbt. This will allow you to leverage it fully: full refreshes, downstream re-runs, documentation, and data lineage.

You can find my code in my GitHub repo.

Sours: https://towardsdatascience.com/oh-my-dbt-data-build-tool-ba43e67d2531

Women's Dialectical Behavior Therapy

The Women's Partial Hospital Program at Butler Hospital helps women manage depression, anxiety, substance abuse, problematic behaviors and other psychiatric disorders. In the program, you will learn dialectical behavior therapy (DBT), which helps manage suicidal thoughts, self-injurious behavior, and feelings that are overwhelming to you. The program runs Monday through Friday starting at 9 a.m. and ending at 3:00 p.m. 

With a focus on practical problem-solving, the Women's Dialectical Behavior Therapy (DBT) Program teaches the skills needed to cope and effectively manage challenging relationships, situations, thoughts, and feelings. In an effort to change a very painful state of mind as a result of these challenges, you may be engaging in a variety of behaviors, such as self-injury, binge eating, reckless driving, compulsive cleaning, etc. A second focus of DBT is on validation, a powerful tool whereby you, with the help of your treatment team, work on accepting uncomfortable thoughts, feelings, and behaviors rather than struggling with them. Through this process the idea of change no longer appears impossible.

Sours: https://www.butler.org/services/partial/womens-dialectical-behavior-therapy
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Dialectical Behavior Therapy


Dialectical Behavior Therapy (DBT) is a fairly new type of psychotherapy or “talk therapy.” Developed by Marsha Linehan, Ph.D., DBT was first introduced about 20 years ago as a treatment for Borderline Personality Disorder (BPD). People with BPD (for more info on BPD see "Problems addressed" page). experience intense emotions which lead to actions such as self-injury, anger outbursts or abrupt ending of important relationships. Although these actions temporarily reduce emotional pain they often wind up causing independent problems that can make life even more difficult. Historically, BPD has been thought of as one of the most difficult problems to address effectively.  Several research studies, often referred to as clinical trials, have demonstrated that DBT is an effective treatment for adults with BPD. Both the American Psychological Association and the American Psychiatric Association currently consider DBT to be a first-line treatment for BPD (click on either Association for a link to their respective website).   

As a result of DBT’s success in treating adults with BPD, it has been adapted for adolescents struggling with severe emotional turmoil and intensely problematic ways of dealing with their distress. DBT has also been modified so that it can be used with other difficulties such as eating disorders, substance use, and anger management.   

So what exactly is DBT? In a nutshell, DBT is a compassionate type of behavioral therapy that is intended to help people move toward having a life that feels even more meaningful and worth living. Distress, emotional pain, interpersonal difficulties, and behavioral problems such as over-eating, not eating, using substances, self-injuring, losing control, withdrawing, and using-up relationships can make it incredibly difficult to function normally and lead a life that feels meaningful and worthwhile. DBT targets the issues that cause distress and teaches skills to deal with them without having to resort to self-defeating behaviors. It does so in a framework, though, that helps us understand that we are doing the best we can even though we need to learn ways that work better.   

Now that we have a quick idea about what DBT is at its heart, how does it work? For DBT to be successful, the treatment has to do two things effectively: (1) Teach skills that people need in order to move closer toward their life goals and; (2) Help people cultivate an ability to work these skills into their daily lives.  The teaching skills part happens through our 16-week DBT Skills Group. When people sign-up for the DBT Skills Group we ask that they make a commitment to the entire 16-week course.  The Skills Group is run very much like a class or a seminar. It meets one time per week for 90-minutes.  Participants are provided with notebooks that go along with the skills being taught in group.  Homework that corresponds with the skills topics is routinely assigned and reviewed.   

Individual DBT psychotherapy and DBT group therapy are two ways of developing and sharpening the ability to apply skills taught in skills group to real life. DBT clients usually meet individually with their therapist one to two times per week for 45-minute sessions.  DBT group therapy consists of 5-8 group members once a week for 90 minutes. While individual DBT therapy is more personalized, DBT group therapy offers the participant more social support as well the opportunity to see how others incorporate DBT skills into their lives. Some people choose to do both, while others enroll in one or the other. A beginning discussion of how to decide how much and what to sign-up for occurs just below. As is the case with the Skills Group, when people enroll in either individual DBT psychotherapy or DBT group therapy we ask that they make a 16-week commitment.   

At our Center, we offer DBT Skills Groups, DBT group therapy, and DBT individual psychotherapy. The skills and therapy groups are held at a wide range of times during the week, including early evenings and mornings. Each group occurs for 90-minutes and participants typically attend 1-2 groups per week. DBT individual psychotherapy can be scheduled at a wide range of times, including evenings and Saturday’s.   

Decisions regarding which parts of DBT to enroll in usually depend upon individual goals and whether an individual already has an individual therapist they wish to continue with and who understands and supports the goals of Skills Group training. Our Center’s therapists are available to assist in making these choices. With this said, enrolling in a Skills Group is an essential part of DBT for anyone who has not already successfully completed Skills Group training in another setting. To get to this level, we have found that the majority of people need to complete the equivalent of two cycles of our Center’s 16-week skills group.   

Either DBT group therapy or DBT individual psychotherapy is also typically included in most people’s initial plans. These two parts of DBT are the main ways we have of helping people develop and sharpen their ability to apply what is learned in the Skills Group to their lives. To get an even more intensive experience, some people choose to sign-up for both.   

Several people come to our Center already involved in psychotherapy with a therapist in the community.  In these instances, we usually recommend that people enroll in both a skills and therapy group.  At other times, people may come to our Center already involved in individual DBT psychotherapy with a therapist in the community. In these cases, many people choose to limit their enrollment to a skills group. Also, whether part of our Center or not, a pre-requisite of our DBT program is that clients have an individual therapist, who may or may not be the prescribing physician.   

Medication is a useful adjunct to many clients in DBT. In these cases clients need to have a prescribing physician familiar with DBT. We do not provide medication at the Center. If medication alone has successfully treated the problematic symptoms, though, there is no need for DBT. If not, it is important to understand that in undertaking DBT, DBT becomes the primary treatment. If medication side-effects interfere with effective participation in DBT, it is usually a good idea to postpone enrolling in DBT or to discuss with the prescribing physicians the pros and cons of continuing the medications at their current dose.

Sours: https://cdcbt.com/dbt
How does dbt actually compile queries?

© Westside DBT, 2020 All rights reserved

In 2007, Marsha Linehan, Ph.D., Alec Miller, Psy. D. and Jill Rathus, Ph.D. published their research in Treating Suicidal Adolescents Using Dialectical Behavior Therapy. (Guildford Press) Their comprehensive protocol for treating adolescents is the model by which Westside DBT clinicians apply DBT for this population.

How this works at Westside DBT
Adolescents in our DBT program will attend individual therapy sessions weekly and are asked to actively attend weekly group skills training. Group leaders will teach the 5 modules of DBT in skills training group, one at a time: Mindfulness, Interpersonal Effectiveness, Emotion Regulation, Distress Tolerance and "Walking the Middle Path".

DBT groups are similar to taking a class, there are two skills "trainers"/therapists and often have 4-6 clients in the room, each adolescent is provided with a notebook and participation in listening, taking notes and here and now skill based discussion is modeled and expected.

Skills training is provided in both individual and group sessions. Group is designed to teach clients the skills they need to decrease impulsive behaviors and increase emotion regulation in a safe non-judmental environment, and individual therapy sessions are designed to help the clients further apply these skills in their day-to-day life, as well as become aware of what is getting in their way of managing their emotions, relationships and overall quality of life. "Coaching calls" to their individual and group therapist in between sessions is encouraged as a way to generalize skills while they are at home and or school.

The involvement of parents and guardians when working with adolescents is very important. When parents learn the skills their children/teens are learning, parents can potentially model these skills at home, in the day to day life experiences and also use the skills to facilitate their own coping. Research shows family involvement can also be an important aspect of treatment compliance. Ideally in a DBT adolescent program and here at WESTSIDE DBT, therapists will offer skills training groups for family members, either in conjunction with the adolescent’s skills training, separately or some combination of both. In addition, individual family training can be implemented as needed, as can between-session phone coaching for the parents as well as the adolescent. At the very least, support from family members is crucial to DBT’s effectiveness with adolescents.

Sours: https://westsidedbt.com/

Notebook dbt

DBT File Import

The phrase file import refers to the act of taking a non-native DBT file and bringing it into DBT for editing and producing braille. There are many different file types that DBT can import. Of these, the most common is Microsoft Word.

If you want to import files into DBT for languages other than English, there are few alternatives beyond Microsoft Word and Open Office files. This topic is organized so that all the information about languages other than English is at the end.

Image of "Select Document File" dialog

The picture above is the File, Open dialogue. The pull-down menu near the bottom that shows All Files (*.*) allows you to select different file types. The first line of this pull-down menu says DBT Files (*.dxp, *.dxb). DBTopens your Duxbury print files (*.dxp) and your Duxbury braille files (*.dxb). Since this is a discussion about importing files from non-DBT sources, it skips any discussion of opening existing DBT files.

DBT File Import: Word Documents (*.doc, *docx)

Note: This section deals with files written in the English language.

Importing Word 97, 2003, 2007, 2010, 2013, and 2016 Files

Duxbury DBT has supported Word 97/Word 2003 files for a very long time. DBT now supports importing Microsoft Word 2007/2010/2013/2016 files.

Importing Open Office Files

Open Office is an open source alternative to using Microsoft Office. Open Office uses a similar file format to Word 2007/2010/2013/2016. If you have Duxbury DBT 11.1 or newer, you can also import Open Office files.

Importing Microsoft Word Documents using the BANA Template

For those preparing files for use in the United States, a template for MS Word comes with DBT to ease the work of braille production. Click here for details on the latest BANA Template.

Importing MathType Files

MathType is a relatively low-cost program which, among its many capabilities, enables mathematical equations to be inserted into Microsoft Word documents. It is produced by Wiris. Word documents containing MathType equations can be opened in DBT, and translated into mathematical braille. See below about Scientific Notebook, another popular program for producing math equations for use with DBT.

RTF Files: May Require a Work-Around

DBT does not import MS Word RTF files. However, many third party programs produce RTF files but call them Word Documents. Some copies of Acrobat offer to export to Word files. Some OCR software offers to produce Word documents. In both cases, the result may actually be an RTF file. See below on how to convert RTF files into true Word files.

If you do attempt to import an RTF format file into DBT, DBT posts an error message, identifying that this is an RTF file.

Importing WordPerfect Files (*.wp, *.wpd, *.wp5, *.wp6)

While WordPerfect is not a leading product anymore, it was once the premier word processor. DBT retains the ability to import files from WordPerfect 3.X, 5.X, or 6.X.

DBT File Import: All Files (*.*)

Importing DAISY Files

DAISY files (Digital Accessible Information SYstem) are XML files designed for access by the disabled. While useful for braille production, DAISY files often lack all of the encoding needed for quality braille. While DBT can import these files, some of them are extremely large, making direct import difficult.

NIMAS files are specialized DAISY files that meet national instructional materials standards. These files are often massive, and they can require careful treatment to prepare them for braille. Duxbury Systems has created a program called NimPro, which greatly facilitates the import of NIMAS files to DBT. Please, click here for more information.

Importing MegaDots Files

Getting quality braille files from MegaDots to Duxbury DBT has become quite easy. MegaDots 2.5 can export a file from MegaDots into the MegaDots export file type MS Word/BANA Template. This file can be imported into DBT using the DBT template English - American - BANA. Just about all the subtle formatting placed in the MegaDots file is carried over into the DBT file, so it is worthwhile to follow the recipe exactly.

Importing Text Files

DBT imports many different standard text (.txt) files.

Modern text files mostly use the "Unicode" character set, but older files may use one of many "Code Pages" to represent their text. If you find yourself facing unreadable text, selecting the correct Code Page may help. See DBT Code Pages for Text Files.

Scientific Notebook Files

Scientific Notebook, is software which produces LaTeX files. LaTeX files can be imported into DBT both for the text and the mathematical equations. (See above about MathType, another popular program for producing math equations for use with DBT). Duxbury Systems supports Scientific Notebook 5.5, which is still available as a download. The new version, Scientific Notebook 6.0, is not compatible with DBT at this time.

Importing HTML (Web) Files

DBT has the ability to import simple HTML files from the web. If the direct file import is not successful, import the HTML file into Microsoft Word, then export (Save As) as a native Word file. NOTE: Word offers as a default choice to export the file back into a Web Page. You need to deliberately select Word file as the file type to import this file into DBT.

Importing RTF Files

To import an RTF file into DBT, first import the file into Microsoft Word, then export (Save As) as a native Word file. NOTE: Word offers as a default choice to export the file back into a Rich Text Format (RTF) File. You need to deliberately select Word file as a file type. Then you can import this (actual) Word format file into DBT.

Importing Braille Formatted Files

Braille Formatted Files have many nuances. Click here for a detailed discussion on importing braille formatted files.

Languages Other than English (DBT File Import: Word Documents)

Unicode Font Support

DBT supports Unicode fonts. Microsoft Word works with both Unicode and non-Unicode fonts. In the past, languages like Lao, Tibetan, or Oriya were impossible to support without specialized add-ons to Microsoft Word. It is now possible to buy a computer, obtain a word processor (Open Office or Microsoft Word), and immediately set to work writing in many alternate script languages without first having to obtain a special font. As an added bonus, this means that screen readers and other accessibility software have more opportunity to handle your language. At this point, DBT imports or handles about 75 ranges of Unicode characters.

Supporting non-Unicode Fonts

For some nations, their isolation from mainstream computer users and the cost barriers to better solutions caused widespread use of non-Unicode fonts. Thus to offer a braille translator that is practical, Duxbury Systems had to support the dominant font system in use. We have some support for the Akadem font for Cyrillic text, the Saysettha font for Lao text, and the SutonnyMJ font used in Bangladesh for Bengali text.

This work is exacting and quite complex, so Duxbury supports non-Unicode fonts only when there are no other alternatives.

Han or Chinese Characters

DBT does not directly display Chinese characters. Instead DBT displays an appropriate substitute based on the current braille translation language:

LanguageDBT Characters
Mandarin (Mainland)Pinyin romanization with accent
marks for the tones
Mandarin (Taiwan)Zhuyin romanization
CantoneseJyutping romanization with superscript
numbers for the tones
JapaneseUnicode U+30xx characters
KoreanUnicode U+11xx characters

In the Global Settings, Word Import Menu, you can set the choice of how Chinese characters are imported.

Arabic and Hebrew Characters

Both Arabic and Hebrew inkprint are written from right to left. Arabic and Hebrew braille are written from left to right. While DBT displays all inkprint text from right to left, do not attempt to edit the text in DBT. Instead, clipboard the entire line into Word, edit it in Word, and then paste the whole line back into DBT. See also the note below about Script and Font Issues.

Hangul or Korean Characters

Hangul script composes 2 or 3 characters into a single symbol. When DBT imports a file, the process is reversed. DBT breaks down a single Hangul character into its component parts. In technical terms, all Hangul characters from U+AC00 through U+D7AF are redirected into Hangul Jamo characters U+11xx. The result can be difficult to read for those who are used to reading the conventional inkprint. For these languages, it is best to do all the editing in Microsoft Word, then to import into DBT and use DBT as the translation engine and for output.

A Word About Windows: Script and Font Issues

Many scripts (Mandarin, Cantonese, Ethiopic, Khmer, Lao, Oriya, Sinhala, and Tibetan) are best viewed on a Vista, Windows 7, Windows 8, or Windows 10 machine.

Myanmar (Burmese) script support requires Windows 8 or Windows 10.


Copyright Duxbury Systems, Inc. Saturday, April 25, 2020

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Use dbt Seeds to work with static CSV data

dbt Core integration with Azure Databricks

  • 22 minutes to read

dbt (data build tool) is a development environment that enables data analysts and data engineers to transform data by simply writing select statements. dbt handles turning these select statements into tables and views. dbt compiles your code into raw SQL and then runs that code on the specified database in Azure Databricks. dbt supports collaborative coding patterns and best practices such as version control, documentation, modularity, and more. For more information, see What, exactly, is dbt? and Analytics Engineering for Everyone: Databricks in dbt Cloud on the dbt website.

dbt does not extract or load data. dbt focuses on the transformation step only, using a “transform after load” architecture. dbt assumes that you already have a copy of your data in your database.

This article focuses on using dbt Core. dbt Core enables you to write dbt code in the text editor or IDE of your choice on your local development machine and then run dbt from the command line. dbt Core includes the dbt Command Line Interface (CLI). The dbt CLI is free to use and open source.

A hosted version of dbt called dbt Cloud is also available. dbt Cloud comes equipped with turnkey support for scheduling jobs, CI/CD, serving documentation, monitoring and alerting, and an integrated development environment (IDE). For more information, see dbt Cloud integration with Azure Databricks. The dbt Cloud Developer plan provides one free developer seat; Team and Enterprise paid plans are also available. For more information, see dbt Pricing on the dbt website.

Because dbt Core and dbt Cloud can use hosted git repositories (for example, on GitHub, GitLab or BitBucket), you can use dbt Core to create a dbt project and then make it available to your dbt Cloud users. For more information, see Creating a dbt project and Using an existing project on the dbt website.

Requirements

Before you install dbt Core, you must install the following on your local development machine:

Step 1: Create and activate a Python virtual environment

In this step, you use to create a Python virtual environment. We recommend using a Python virtual environment as it isolates package versions and code dependencies to that specific environment, regardless of the package versions and code dependencies within other environments. This helps reduce unexpected package version mismatches and code dependency collisions.

  1. From your terminal, switch to an empty directory, creating that directory first if necessary. This procedure creates an empty directory named in the root of your user home directory.

    Unix, linux, macos

    Windows

  2. In this empty directory, create a file named with the following content. This Pipfile instructs to use Python version 3.8.6. If you use a different version, replace with your version number.

  3. Create a Python virtual environment in this directory by running and specifying the Python version to use. This command specifies Python version 3.8.6. If you use a different version, replace with your version number:

  4. Activate this virtual environment by running . To confirm the activation, the terminal displays before the prompt. The virtual environment begins using the specified version of Python and isolates all package versions and code dependencies within this new environment.

    Note

    To deactivate this virtual environment, run . disappears from before the prompt. If you run or with this virtual environment deactivated, you might see a different version of Python, a different list of available packages or package versions, or both.

  5. Confirm that your virtual environment is running the expected version of Python by running with the option.

    If an unexpected version of Python displays, make sure you have activated your virtual environment by running .

Step 2: Install required software into your virtual environment

In this step, you install the module, dbt, and the dbt Spark ODBC plugin into your Python virtual environment.

  1. With the virtual environment activated, install the module by running .

  2. Install dbt by running with the name of the dbt package from the Python Package Index (PyPI), which is .

    Important

    If your local development machine uses any of the following operating systems, you must complete additional steps before installing dbt: Ubuntu, Debian, CentOS, and Windows.

  3. Install the dbt Spark ODBC plugin by running with the name of the dbt Spark ODBC plugin package from PyPI, which is .

    You must specify , including the version number if needed, within quotes.

  4. Confirm that your virtual environment is running the expected versions of dbt and the dbt Spark ODBC plugin by running with the option.

    If an unexpected version of dbt or the dbt Spark ODBC plugin displays, make sure you have activated your virtual environment by running . If an unexpected version still displays, try installing dbt or the dbt Spark ODBC plugin again after you activate your virtual environment.

Step 3: Create a dbt project and specify and test connection settings

In this step, you create a dbt project, which is a collection of related directories and files that are required to use dbt. You then configure your connection profiles, which contain connection settings to an Azure Databricks cluster, a SQL endpoint, or both. To increase security, dbt projects and profiles are stored in separate locations by default.

Tip

You can connect to an existing cluster or SQL endpoint, or you can create a new one.

  • An existing cluster or SQL endpoint can be efficient for multiple dbt projects, for using dbt in a team, or for development use cases.
  • A new cluster or SQL endpoint allows you to run a single dbt project in isolation for production use cases, as well as leverage automatic termination to save costs when that dbt project is not running.

Use Azure Databricks to create a new cluster or SQL endpoint, and then reference the newly-created or existing cluster or SQL endpoint from your dbt profile.

  1. With the virtual environment still activated, run the dbt init command with a name for your project, specifying the adapter. This procedure creates a project named .

    Tip

    Carefully read the output of the command, as it provides additional helpful usage guidance.

  2. From within the dbt configuration folder, open the file. The location of this file is listed in the output of the command.

    Tip

    If you forget where the dbt configuration folder is located, you can list it by running the command.

  3. Modify the contents of the file, depending on what you want to connect to.

    Cluster

    Replace:

    • with the path to where the Databricks ODBC driver is installed on your local development machine. To get the path, for Unix, Linux, or macOS, run . For Windows, use the Registry Editor to browse to the key.

    • with the Server Hostname value from the Advanced Options, JDBC/ODBC tab for your Azure Databricks cluster.

    • with the value between and any other query string separators in the URL to your Azure Databricks workspace.

      Important

      The organization ID value must be within quotes.

    • with the value of your personal access token.

    • with the ID of your cluster. You can get this ID from the HTTP Path value from the Advanced Options, JDBC/ODBC tab for your Azure Databricks cluster. The ID is the string of characters following the final forward slash character. For example, if the HTTP Path value is , the cluster ID is .

    • with the Port value from the Advanced Options, JDBC/ODBC tab for your Azure Databricks cluster.

    For more information, see Connecting to Databricks ODBC on the dbt website and Configuring your profile in the dbt-labs/dbt-spark repository in GitHub.

    Sql endpoint

    Replace:

    • with the path to where the Databricks ODBC driver is installed on your local development machine. To get the path, for Unix, Linux, or macOS, run . For Windows, use the Registry Editor to browse to the key.

    • with the Server Hostname value from the Connection Details tab for your SQL endpoint.

    • with the value between and the first dot () in the Server Hostname value from the Connection Details tab for your SQL endpoint.

      Important

      This value must be within quotes.

    • with the value of your personal access token.

    • with the ID of your SQL endpoint. You can get this ID from the HTTP Path value from the Connection Details tab for your SQL endpoint. The ID is the string of characters following the final forward slash character. For example, if the HTTP Path value is , the SQL endpoint ID is .

    • with the Port value from the Connection Details tab for your SQL endpoint.

    For more information, see Connecting to Databricks ODBC on the dbt website and Configuring your profile in the dbt-labs/dbt-spark repository in GitHub.

    Tip

    You do not have to use the connection profile name provided in the example (such as ). You can use whatever connection profile names you want. To allow dbt to switch connections, you can add a separate profile entry for each connection, giving each profile entry a unique name, for example:

  4. In your project’s file, change the value of the setting to match the name of your connection profile in the file. This procedure uses a connection profile named .

  5. Confirm that the connection details are correct by running the command.

Step 4: Create and run models

In this step, you use your favorite text editor to create models, which are statements that create either a new view (the default) or a new table in a database, based on existing data in that same database. This procedure creates a model based on the sample table from the Azure Databricks datasets, as described in the Create a table section of _. This procedure assumes this table has already been created in your workspace’s database.

  1. In the project’s directory, create a file named with the following SQL statement. This statement selects only the carat, cut, color, and clarity details for each diamond from the table. The block instructs dbt to create a table in the database based on this statement.

    Tip

    For additional options such as using the Delta file format and the incremental strategy, see Apache Spark configurations on the dbt website and the “Model Configuration” and “Incremental Models” sections of the Usage Notes in the dbt-labs/dbt-spark repository in GitHub.

  2. In the project’s directory, create a second file named with the following SQL statement. This statement selects unique values from the column in the table, sorting the results in alphabetical order first to last. Because there is no block, this model instructs dbt to create a view in the database based on this statement.

  3. In the project’s directory, create a third file named with the following SQL statement. This statement averages diamond prices by color, sorting the results by average price from highest to lowest. This model instructs dbt to create a view in the database based on this statement.

  4. With the virtual environment activated, run the command with the paths to the three preceding files. In the database (as specified in the file), dbt creates one table named and two views named and . dbt gets these view and table names from their related file names.

  5. Run the following SQL code to list information about the new views and to select all rows from the table and views.

    If you are connecting to a cluster, you can run this SQL code from a notebook that is connected to the cluster, specifying SQL as the default language for the notebook. If you are connecting to a SQL endpoint, you can run this SQL code from a query.

Step 5: Create and run more complex models

In this step, you create more complex models for a set of related data tables. These data tables contain information about a fictional sports league of three teams playing a season of six games. This procedure creates the data tables, creates the models, and runs the models.

  1. Run the following SQL code to create the necessary data tables.

    If you are connecting to a cluster, you can run this SQL code from a notebook that is connected to the cluster, specifying SQL as the default language for the notebook. If you are connecting to a SQL endpoint, you can run this SQL code from a query.

    The tables and views in this step start with to help identify them as part of this example. You do not need to follow this pattern for your own tables and views.

  2. In the project’s directory, create a file named with the following SQL statement. This statement creates a table that provides the details of each game, such as team names and scores. The block instructs dbt to create a table in the database based on this statement.

  3. In the project’s directory, create a file named with the following SQL statement. This statement creates a view that lists team win-loss records for the season.

  4. With the virtual environment activated, run the command with the paths to the two preceding files. In the database (as specified in the file), dbt creates one table named and one view named . dbt gets these view and table names from their related file names.

  5. Run the following SQL code to list information about the new view and to select all rows from the table and view.

    If you are connecting to a cluster, you can run this SQL code from a notebook that is connected to the cluster, specifying SQL as the default language for the notebook. If you are connecting to a SQL endpoint, you can run this SQL code from a query.

Step 6: Create and run tests

In this step, you create tests, which are assertions you make about your models. When you run these tests, dbt will tell you if each test in your project passes or fails.

There are two type of tests. Schema tests, applied in YAML, return the number of records that do not pass an assertion. When this number is zero, all records pass, therefore the tests pass. Data tests are specific queries that must return zero records to pass.

  1. In the project’s directory, create a file named with the following content. This file includes schema tests that determine whether the specified columns have unique values, are not null, have only the specified values, or a combination.

  2. In the project’s directory, create a file named with the following SQL statement. This file includes a data test to determine whether any games happened outside of the regular season.

  3. In the project’s directory, create a file named with the following SQL statement. This file includes a data test to determine whether any scores were negative or any games were tied.

  4. In the project’s directory, create a file named with the following SQL statement. This file includes a data test to determine whether any teams had negative win or loss records, had more win or loss records than games played, or played more games than were allowed.

  5. With the virtual environment activated, run the command with the option and names of the two models in the file to run the tests that are specified for those models.

  6. Run the command with the option to run the tests in the project’s directory.

Step 7: Clean up

You can delete the tables and views you created for this example by running the following SQL code.

If you are connecting to a cluster, you can run this SQL code from a notebook that is connected to the cluster, specifying SQL as the default language for the notebook. If you are connecting to a SQL endpoint, you can run this SQL code from a query.

Next steps

  • Learn more about dbt models.
  • Learn more about how to test your dbt projects.
  • Learn how to use Jinja, a templating language, for programming SQL in your dbt projects.
  • Learn about dbt best practices.
  • Learn about dbt Cloud, a hosted version of dbt.

Troubleshooting

This section addresses common issues when using dbt Core with Azure Databricks.

issues

For issues related to the use of , see Troubleshooting.

General troubleshooting

See Getting help on the dbt Labs website.

Additional resources

Feedback

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Sours: https://docs.microsoft.com/en-us/azure/databricks/dev-tools/dbt

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