Introduction

As a Databricks partner, Infinitive empowers clients to unlock value from data dispersed across various systems. Implementation of these strategies typically involves building data pipelines. Data pipelines automate the collection and processing of data, reduce manual errors and ensure high quality data, so that business decisions can be made with confidence. Infinitive has streamlined this process of creating data pipelines with our metadata-driven framework.

Metadata-driven ETL represents a transformative approach to data integration that enables data-driven organizations to achieve operational efficiency and drive innovation.

Infinitive’s metadata-driven framework for automatically generating Databricks data pipelines is implemented through a setup where structured information, or “metadata,” is used to define the components and steps in a data pipeline. Metadata is essentially a set of instructions that details the “who,” “what,” “where” and “how” of your data pipeline. This metadata describes the specifics of the data sources, transformations, and destinations, allowing for the automated creation of the pipeline without manually coding each element. Our method saves time, reduces costs, and eliminates errors. Here’s how this approach works:

Defining Metadata as a Blueprint

In a metadata-driven framework, metadata typically includes:

• Source information: Where the data is coming from, such as databases, file locations, or APIs.
• Transformation rules: Any data processing steps, like filtering, joining, aggregating, or cleansing the data.
• Destination details: Where the processed data should go, such as data warehouses, data lakes, or tables within Databricks.

This metadata is often stored in a structured format like JSON objects, XML trees, etc.. Think of it as a “blueprint” that describes the pipeline without building it.

Framework Components for Interpreting Metadata

A metadata interpreter in Databricks reads the metadata and translates it into specific actions or configurations. For example, the Infinitive framework:

• reads the metadata and sees that data needs to be pulled from a specified database.
• recognizes specific transformation rules, like dropping null values or merging columns.
• understands where to store the final dataset based on destination metadata.
• executes source extraction, data transformation, and data loading.
• applies data quality rules, quarantine the records that fail, and alert Data Stewards for remediation efforts.

Each component dynamically adjusts based on what the metadata dictates, meaning the pipeline logic is modular and reusable.

Automatic Pipeline Generation in Databricks

When the metadata is fed into the interpreter, it automatically generates the Databricks pipeline by:

• connecting to sources specified in the metadata (e.g., connecting to a MySQL database to pull raw data)
• running data quality checks by translating the rules in the metadata into Spark SQL or Python commands, quarantining records that fail
• executing transformations by translating the transformation rules in the metadata into Spark SQL or Python commands
• loading data into the target locations by creating or updating tables in Databricks, based on the destination details in the metadata.

This automation reduces the need to manually create new code for each pipeline since the same interpreter code dynamically adjusts to different pipeline specifications.

Parameterization and Flexibility

One of the most powerful aspects of the Infinitive metadata-driven framework is its parameterization. For example, if you add a new data source or change a transformation rule, you can simply update the metadata file without altering the core interpreter code.

Parameters such as file paths, table names, and transformation logic are defined in the metadata. If you need to build similar pipelines for multiple data sources, the same framework can read different metadata configurations to generate a new pipeline for each source.

Error Handling and Logging

The Infinitive framework includes metadata for error handling, specifying actions if an error occurs, such as logging it in a separate table or triggering an alert.

Logging is integrated to track each pipeline’s progress, and these logs are stored in Databricks tables. By checking logs, teams can monitor pipeline statuses and performance without needing to dig into the code.

Simplifying Maintenance and Scalability

Since pipelines are metadata-driven, they’re easier to maintain. Changes to data sources or transformation logic don’t require extensive code rewrites, only updates to the metadata files.

Using metadata-driven pipelines allows for the framework to more easily scale. This allows for code reusability, modularity, and abstraction. When new data pipelines are needed, you simply add a new metadata configuration rather than building an entirely new pipeline from scratch, making it highly efficient for enterprises with a variety of data workflows.

This framework helps organizations achieve code reusability, modularity, and abstraction while improving pipeline maintenance and scalability by:

• Simplifying Architecture
  Metadata-driven ETL helps decouple technical implementation from business logic, which allows for faster decision-making around centralized ETL rules. Metadata-driven ETL also allows for diversification of ownership, which reduces the owner’s dependence on decision-making.
• Enhanced Maintenance
  Metadata-driven ETL helps standardize development and deployment of data infrastructure, which helps minimize error risks while also allowing developers to quickly adapt to requirement changes.
• Improved Scalability
  Metadata-driven ETL allows developers to quickly spin up new pipelines, get immediate feedback, and identify successful transformation patterns, which in turn helps with both vertical and horizontal scaling.
• Better Governance
  Metadata-driven ETL also enables comprehensive audit trails, which ensures consistency and helps with version control of both the code and business logic, holding all parties accountable.

Example: Metadata-Driven Pipeline Flow

Here’s a simple example:

Metadata Entry:

• Source: “Database A, Table B”
• Transformation: “Filter records where ‘status = active,’ Join with ‘Table C’ on ‘ID’”
• Destination: “Databricks Delta Table D”
• Framework Execution:
  • The interpreter reads the metadata, connects to the database, fetches the data, applies data quality rules and quarantines failed records, performs filtering and joining based on the rules, and then saves it in the Delta table.
  • Outcome: The pipeline runs, processing data exactly as specified in the metadata, without custom code for each step.

The Infinitive metadata-driven framework empowers data teams to use Databricks more flexibly, with automated, reusable, and standardized pipelines, boosting productivity while reducing manual work. This allows them to harness the full potential of Databricks. By automating repetitive tasks and standardizing data pipelines, we significantly boost productivity and reduce manual effort.

Let’s discuss how we can help you optimize your Databricks environment and achieve your data goals.