Purview Atlas JSON Flow: ISS-02 Review & Examples

by Viktoria Ivanova 50 views

Hey guys! Today, we're diving deep into the ISS-02 Review, where we're going to dissect how the Atlas JSON flows into Purview. Our main goal here is to really understand how these JSON structures are generated and how they're used to feed information into Purview. We’ll be focusing on the examples/ folder to get a handle on the entire process.

Objective

Our primary objective is to analyze the examples/ folder. We aim to identify and understand how the JSON structures are generated and formatted before being sent to Purview. This involves tracing the flow of data, understanding the transformation logic, and documenting the expected schema.

Criteria

To achieve our objective, we’ve set out a few key criteria:

  1. Locate Complete Examples: We need to find comprehensive examples, specifically those related to Tag DB and SSIS. These examples will serve as our primary case studies for understanding the JSON flow. We’ll be looking for examples that illustrate the full lifecycle of data, from its origin to how it's represented in Purview.
  2. Document Inputs, Outputs, and Parameters: For each example, we’ll meticulously document the inputs, outputs, and parameters involved. This documentation will be crucial in understanding the data transformation process. We will note these details in our notes/diario_poc.md file, ensuring we have a clear and accessible record of our findings.
  3. Create a Mapping Table: We will create a table that maps files to their purpose and expected output. This table will serve as a quick reference guide, helping us and others quickly understand the role of each file and what to expect from it.

Diving into the Examples Folder

Okay, let's get our hands dirty and dive into the examples/ folder! We're on the hunt for those juicy examples that will illuminate the JSON flow process. Remember, we're looking for complete examples, particularly those dealing with Tag DB and SSIS. These are gold mines for understanding how data is structured and transferred into Purview.

Tag DB Examples

When we talk about Tag DB examples, we're essentially looking at how database tags and metadata are represented in JSON format for Purview. This is super important because tags are the bread and butter of data governance. They help us categorize, classify, and manage data assets effectively. So, what do we need to look for?

  • Schema Definitions: How are database schemas, tables, and columns represented? We need to understand the JSON structure that describes these database elements. Are there specific attributes or properties used to define schemas, and how are these linked together?
  • Tag Application: How are tags applied to database objects? This is crucial because tags provide context and meaning to the data. We need to see how tags are associated with tables, columns, and other entities within the database.
  • Metadata Enrichment: How is additional metadata included? Besides the basic schema information, what other metadata is included in the JSON? This could include descriptions, data types, constraints, and other relevant details.
  • Relationships: How are relationships between different database objects represented? Databases often have complex relationships between tables and views. Understanding how these are represented in JSON is vital for maintaining data integrity and lineage in Purview.

We'll be scrutinizing the JSON structures to see how these components are organized and represented. Keep an eye out for patterns, consistency, and any areas that might need further clarification. Think of it like being a detective, piecing together the clues to solve the mystery of data representation!

SSIS Examples

Now, let's shift our focus to the SSIS examples. SSIS, or SQL Server Integration Services, is a powerful tool for building data integration and ETL (Extract, Transform, Load) solutions. Understanding how SSIS metadata and workflows are represented in JSON is crucial for tracking data lineage and transformations in Purview. When looking at SSIS examples, here’s what we'll be focusing on:

  • Package Structure: How are SSIS packages represented in JSON? SSIS packages are the fundamental units of work, containing tasks, control flows, and data flows. We need to see how the overall structure of a package is captured in JSON.
  • Task and Component Details: How are individual tasks and components within a package defined? This includes tasks like data flow tasks, execute SQL tasks, and more. We need to understand how each task is represented, including its properties and configurations.
  • Data Flow Transformations: How are data flow transformations represented? Data flows involve complex transformations like aggregations, lookups, and joins. Understanding how these transformations are described in JSON is key to tracking data lineage.
  • Parameters and Variables: How are parameters and variables within SSIS packages represented? Parameters and variables are used to make packages dynamic and configurable. We need to see how these are captured in the JSON structure.
  • Execution Flow: How is the execution flow of the SSIS package represented? This involves understanding the order in which tasks are executed and how control flows dictate the execution path.

By examining these SSIS examples, we’ll gain insights into how ETL processes are described in JSON, which is crucial for data governance and compliance. Think of it as mapping out the blueprint of a data pipeline, ensuring we know exactly how data flows and is transformed along the way.

Documenting Our Findings

Okay guys, as we dig through these examples, it's super important that we document everything. We don't want to lose track of the cool stuff we're discovering! Our go-to spot for jotting down all the details is the notes/diario_poc.md file. Think of it as our treasure map, guiding us through the ins and outs of the JSON structures.

What to Document

So, what exactly are we documenting? Well, we're focusing on the inputs, outputs, and parameters for each example. This is like the holy trinity of data flow understanding. Let’s break it down:

  • Inputs: What data is going into the process? This could be anything from database schemas to SSIS package configurations. We need to understand the source and structure of the input data. What format is the input data in? Is it a CSV file, a database table, or an XML document? Are there specific fields or elements that are crucial for the process?
  • Outputs: What's the result of the process? In our case, it's the JSON structure that gets sent to Purview. We need to meticulously describe the format, schema, and content of this JSON. What does the output JSON look like? What are the main elements and attributes? How is the data structured hierarchically? Are there any specific naming conventions or data types used?
  • Parameters: What configurations or settings influence the process? These are the knobs and dials that control how the JSON is generated. Understanding parameters helps us see how the process can be customized and adapted. What parameters are used in the process? How do these parameters affect the output JSON? Are there default values for the parameters? Can the parameters be overridden?

How to Document

Now that we know what to document, let's talk about how to document it. The notes/diario_poc.md file is our digital notebook, so let's make it clear, concise, and easy to read. Here are a few tips:

  • Use Markdown: Markdown is our friend! It lets us format text easily with headings, lists, and code blocks. This will keep our notes organized and readable. Use headings to structure the notes for each example. Use lists to enumerate inputs, outputs, and parameters. Use code blocks to include snippets of JSON or code.
  • Be Detailed: Don't skimp on the details! The more info we capture, the better we'll understand the process. Describe the purpose of each input, output, and parameter. Explain any assumptions or dependencies. Note any challenges or questions that arise during the analysis.
  • Use Examples: Whenever possible, include examples of the input data, output JSON, and parameter values. This will make it much easier to understand the concepts. Include snippets of JSON to illustrate the structure and content. Show examples of input data and how it is transformed into the output JSON. Provide examples of parameter values and how they affect the process.

By diligently documenting these details, we’re not just helping ourselves—we’re creating a valuable resource for the entire team. It’s like building a knowledge base, one note at a time!

Creating a Mapping Table

Alright, let’s talk about the final piece of the puzzle: creating a mapping table. This table is going to be our cheat sheet, our quick-reference guide to understanding the purpose and expected output of each file in the examples/ folder. Think of it as a roadmap, helping us navigate the JSON landscape.

What to Include in the Table

So, what goes into this magical mapping table? We need three key columns:

  • File: The name of the file we're looking at. This is the starting point, the identifier for each example.
  • Purpose: What's the main goal or function of this file? Is it generating JSON for a Tag DB, an SSIS package, or something else? This column gives us context, helping us understand why the file exists.
  • Expected Output: What kind of JSON structure should we expect to see? What entities or metadata will it contain? This is the destination, the result we're aiming to understand.

Why This Table Matters

Why are we even bothering with this table? Great question! Here’s why:

  • Quick Reference: It gives us a super-fast way to look up a file and understand its purpose. No more digging through code or documentation to figure out what a file does. Just glance at the table, and boom—you know!
  • Consistency: It helps us maintain consistency in our understanding. By explicitly stating the purpose and expected output, we ensure everyone is on the same page.
  • Knowledge Sharing: It's a fantastic tool for sharing knowledge with the team. Newcomers can quickly get up to speed, and even seasoned pros can use it as a refresher.

Example Table Structure

To give you a clearer picture, here’s what our mapping table might look like:

File Purpose Expected Output
tag_db_schema.json Generates JSON for a database schema. JSON representing tables, columns, data types, and relationships.
ssis_package_metadata.py Extracts metadata from an SSIS package and generates JSON. JSON describing the package structure, tasks, data flows, transformations, parameters, and variables.
tag_application.json Generates JSON to apply tags to database objects. JSON associating tags with tables, columns, and other database entities.
data_lineage.py Generates JSON representing data lineage for a specific data flow. JSON describing the source, transformations, and destination of data, including the steps and processes involved.

By creating this table, we’re not just documenting—we’re building a powerful tool for understanding and navigating the JSON landscape in our project.

Conclusion

Alright guys, that wraps up our deep dive into the ISS-02 Review and understanding the Atlas JSON flow in Purview! We’ve covered a lot of ground, from locating examples and documenting findings to creating a mapping table. Remember, the key takeaway here is to really grasp how JSON structures are generated and used to feed information into Purview.

By meticulously analyzing the examples/ folder, documenting inputs, outputs, and parameters, and creating a clear mapping table, we’re setting ourselves up for success. This isn't just about understanding the technical details; it's about building a solid foundation for data governance and compliance.

So, let’s keep exploring, keep documenting, and keep sharing our knowledge. Happy coding, and see you in the next review!