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How one can Outline Analytics Initiatives Utilizing Code: CI/CD & Integration


The “as code” method has proliferated to nearly each facet of the fashionable tech firm. Why ought to knowledge analytics be any completely different? A contemporary analytics venture may be no much less complicated than, let’s say, an Infrastructure as Code setup, and it could actually additionally profit from versioning, automation, and collaborative coding instruments.

On this article, you’ll discover ways to outline an analytics resolution as code, find out how to arrange CI/CD pipelines for such an answer, and find out how to combine it together with your infrastructure.

If you happen to’d quite examine the advantages of analytics as code in comparison with conventional options, right here is an article discussing simply that!

GoodData’s Tackle Analytics as Code

Analytics as code isn’t new to GoodData. We’ve had our Declarative API for some time now. Its predominant objective being to model analytics initiatives, copy them between completely different cases of GoodData and permit the manipulation of the metadata in a pipeline.

Python SDK is constructed on high of the Declarative API and took it to a complete different stage. A venture may be outlined utterly programmatically, or loaded from the Declarative API and manipulated with Python scripts.

GoodData for VS Code is our newest addition to the toolset and the subject of this text. Our aim with this software is to introduce analytics engineers to software program growth finest practices like coding inside an IDE, contributing to Github, opening Pull Requests, and using CI/CD pipelines for deployment.

GoodData for VS Code

GoodData for VS Code consists of two complementary instruments: a VS Code Extension and a CLI utility. It additionally defines the way you describe analytics objects in code — a language syntax. Let’s undergo this stuff subsequent.

Language Syntax

The GoodData for VS Code language syntax is predicated on YAML, which we selected for its brevity and ease.

Our Python SDK additionally makes use of YAML to retailer declarative definitions in recordsdata for versioning. Nevertheless, these are two completely different codecs. GoodData for VS Code’s format focuses on being human-friendly and transient, whereas the format of Python SDK strictly follows the REST API schema from our server. These instruments have completely different use circumstances in thoughts and, as they evolve additional, we are going to resolve if we wish to ultimately merge them or allow them to occupy their very own niches.

Metric definition in YAML
Metric definition in YAML

At present, GoodData for VS Code allows you to outline datasets and metrics. Collectively, these objects describe the semantic layer; the inspiration of an analytics venture.

We’re planning to help visualizations and dashboards with GoodData for VS Code within the following releases, thus permitting you to outline a whole analytics venture as code.

VS Code Extension

VS Code has respectable help for YAML file enhancing out of the field, particularly when you connect the correct JSON schema. Nevertheless, it could nonetheless be missing the context wanted to run semantic validation or recommend the correct autocomplete possibility. It is for that cause we created our extension for VS Code. Listed below are some options that the GoodData extension packs:

  • In contrast to the built-in syntax spotlight, our extension additionally highlights ids and references between objects, making it simpler to navigate the doc.

  • We’ve put a variety of effort into analytics venture validation. You get a typical, to be anticipated, schema validation and semantic validation for each file. However we went even additional and added contextual validation. Your venture recordsdata aren’t solely cross-validated throughout the venture but additionally validated towards your database to make sure you’re referencing solely current tables and columns. This additionally opens some prospects for future integration with different instruments in your stack, like dbt or Meltano.

  • Autocomplete does what you anticipate — it suggests legitimate choices for a given property as you sort.

    Metric preview
    Metric preview
  • The preview characteristic is a big productiveness booster and means that you can preview your datasets and metrics proper from VS Code with out the necessity to change to the browser to test the outcomes.

GoodData’s extension for VS Code is out there now on {the marketplace}. You too can set up it proper from the extensions tab in your VS Code — simply seek for “GoodData”.

CLI Utility

GoodData CLI is a command line app that’s meant for use as a companion to the VS Code extension or individually in CI/CD pipelines. It’s written in JavaScript, thus requires NodeJS, and may be put in immediately from NPM (npm i -g @gooddata/code-cli).

GoodData CLI
GoodData CLI

GoodData CLI supplies 4 instructions. Some are extra fascinating when utilized in mixture with the VS Code extension (init and clone), whereas others have been constructed with CI/CD pipelines in thoughts (validate and deploy).

The Workflow

Irrespective of how good your instruments are and the way environment friendly you might be in creating code, you will not get far with no robust workflow. A workflow to stop human errors, but be versatile sufficient to not get in the way in which if you’re on a task. Let’s see what the setup and CI/CD pipelines might seem like for an analytics venture.

The Setup

GoodData for VS Code setup
GoodData for VS Code setup

To begin with, each analytics engineer must have the “handle” permission on the group stage on GoodData Cloud. Ideally, you’ll wish to have two organizations: one for growth, the place all analytics engineers get full entry, and one other for manufacturing, the place solely CI/CD pipelines can push modifications to.

Subsequent, every analytics engineer ought to ideally have their very own sandbox workspace throughout the growth group. That’s as a result of we have to deploy the modifications with a purpose to run previews for datasets and metrics. If a number of individuals would share the identical dev workspace, there could be a threat of overriding one another’s work and ending up with unreliable previews.

With such a setup, each analytics engineer in your crew will have the ability to work independently in their very own sandbox, with none threat of inadvertently affecting manufacturing. All modifications to the manufacturing atmosphere are achieved by means of CI/CD pipelines after correct gating: code assessment and automatic assessments.

CI/CD Pipelines

If all you want is to propagate the work that analytics engineers are doing to the manufacturing server, the CI/CD setup may be very simple. Right here is an instance for GitHub Actions.

First, you’ll have to gate any new code that’s being merged to the primary department — GoodData CLI can validate the venture and guarantee there aren’t any apparent errors. The next pipelines will execute validation on each Pull Request to the predominant department. If you happen to additionally forbid direct pushes to the department and make the checks necessary for Pull Requests in your repo settings, you may ensure that no invalid code will ever be merged there.

title: GoodData Analytics Gating

on:
  pull_request:
    branches:
      - 'predominant'

jobs:
  gate:
    runs-on: ubuntu-latest
    env:
      # Outline your token in GitHub secrets and techniques
      GOODDATA_API_TOKEN: ${{secrets and techniques.GOODDATA_API_TOKEN}}

    steps:
      - title: Checkout code
        makes use of: actions/checkout@v3
      - title: Arrange NodeJS
        makes use of: actions/setup-node@v3
      - title: Set up GoodData CLI
        run: npm i -g @gooddata/code-cli
      - title: Validate agains staging atmosphere
        run: gd validate --profile staging

Subsequent, you’ll wish to deploy the brand new model of analytics after the merge. If your organization is embracing Steady Supply, this might be your manufacturing deployment. If not, you may set it for a staging atmosphere and produce other pipelines for manufacturing, maybe triggered manually.

title: GoodData Analytics Deployment

on:
  push:
    branches:
      - 'predominant'

jobs:
  gate:
    runs-on: ubuntu-latest
    env:
      # Outline your token in GitHub secrets and techniques
      GOODDATA_API_TOKEN: ${{secrets and techniques.GOODDATA_API_TOKEN}}

    steps:
      - title: Checkout code
        makes use of: actions/checkout@v3
      - title: Arrange NodeJS
        makes use of: actions/setup-node@v3
      - title: Set up GoodData CLI
        run: npm i -g @gooddata/code-cli
      - title: Validate agains manufacturing atmosphere
        run: gd validate --profile manufacturing
      - title: Deploy to manufacturing
        run: gd deploy --profile manufacturing --no-validate

Notice, that within the instance above we’ve separated the validation and deployment steps. That’s achieved purely for our comfort when studying the pipeline outcomes. Technically, each deploy command first runs validation, except you cross the --no-validate possibility.

There’s a catch to this setup, although. GoodData for VS Code solely covers the semantic layer (and shortly will cowl the analytics layer) of your venture. However there’s a lot extra to a typical venture: knowledge supply definitions, knowledge filters, workspace hierarchies, person administration, and permissions, and so on. Moreover, you would possibly wish to have a number of workspaces with completely different semantic layers in a single group. How do you orchestrate a whole deployment? Effectively, that’s the place the older brothers of GoodData for VS Code are available: Declarative API and Python SDK. I’ve made a demo venture on what a whole setup would possibly seem like — with analytics outlined by means of GoodData for VS Code and the remaining is finished with a Python script. Be at liberty to fork it on GitHub.

What’s Subsequent?

GoodData for VS Code is presently obtainable as a public beta, and we’re dedicated to creating it additional right into a secure launch. Listed below are a couple of matters we’re wanting into:

  • Including help for visualization and dashboard definitions in code.
  • Integration with different “as code” instruments, each up the information pipeline (e.g. ELT instruments like dbt or Meltano) and down the pipeline (like our personal React SDK).
  • Check automation for knowledge analytics.

What characteristic would you prefer to see applied subsequent? If you wish to be a part of the story, attain out to us on our neighborhood Slack channel with suggestions and solutions.

Need to attempt GoodData for VS Code your self? Right here is an effective start line To make use of it, you’ll want a GoodData account. One of the best ways to acquire it’s to register for a free trial.

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