Delta Live Tables Decoded: What Every Data Engineer Needs to Know

In the ever-evolving landscape of data engineering, Databricks Delta Live Tables (DLT) has emerged as a powerful framework for building reliable, maintainable, and scalable data pipelines. As organizations increasingly adopt the data lakehouse architecture, DLT promises to streamline the process of creating and managing data workflows. However, like any technology, it comes with its own set of strengths and limitations. This deep dive aims to decode Delta Live Tables, offering a comprehensive look at its features and constraints. Whether you’re considering implementing DLT in your data lakehouse or seeking to optimize your current setup, this analysis provides the critical insights every data engineer needs to know to make informed decisions and leverage DLT effectively.

Key Features

Developer-Friendly: The Power of Declarative Programming

One of Delta Live Tables’ standout features is its developer-friendly approach, particularly its use of a declarative programming model. This model represents a significant shift from traditional imperative programming, offering several key benefits:

• Simplified Code Structure: With DLT, developers define ‘what’ they want to achieve rather than specifying ‘how’ to do it. This approach results in cleaner, more concise code that’s easier to read and maintain.
• Reduced Boilerplate: The declarative framework eliminates much of the boilerplate code typically required in data pipeline development. Developers can focus on data transformations and business logic rather than infrastructure concerns.
• Flexibility in Language Choice: DLT supports both Python and SQL for defining tables and transformations. This flexibility allows developers to use the language they’re most comfortable with or that best suits the task at hand.
• Enhanced Collaboration: The declarative approach makes it easier for team members to understand and contribute to each other’s work. It bridges the gap between data engineers, data scientists and data analysts, fostering better collaboration.
• Scalability and Maintainability: As data volumes grow, the declarative model scales more gracefully than imperative approaches. Changes to data schemas or business rules often require less code modification, improving maintainability.

Data Quality: Ensuring Integrity at Scale

Delta Live Tables places a strong emphasis on data quality, offering robust mechanisms to enforce and monitor data integrity throughout the pipeline. This feature is crucial for maintaining reliable and trustworthy data in your lakehouse. DLT introduces the EXPECT clause, a powerful tool for defining data quality rules. These rules are declarative assertions about the data that should hold true. It can handle different types of expectations, e.g. non-null values, value ranges, data types.

-- Example Data Quality Rules in DLT
CONSTRAINT valid_customers_name EXPECT (first_name IS NOT NULL and last_name IS NOT NULL) ON VIOLATION DROP ROW,
CONSTRAINT valid_price EXPECT (price > 0)

DLT offers multiple ways to handle records that violate expectations:

1. Warn (Default): Flags violating records but allows them to pass through.
   Useful for monitoring without disrupting the pipeline.
2. Drop: Automatically removes records that fail to meet expectations. Ensures downstream processes only work with clean data.
3. Fail: Halts the pipeline if any violations are detected.
   Crucial for critical data quality issues that require immediate attention.
4. Quarantine (Custom Action): Allows for custom handling of violating records. Can be used to move problematic data to a separate table for further investigation.

DLT pipeline offers comprehensive monitoring and reporting capabilities:

• Built-in Statistics: DLT automatically generates statistics on data quality rule violations. This provides insights into percentage of violated rows, the frequency and nature of data quality issues.
• Visual Diagrams: It offers visual representations of data quality metrics and helps in quickly identifying trends or recurring issues.
• Detailed Results: Provides detailed logs of which records violated which expectations. Facilitates easier debugging and data cleansing efforts.

Metadata Management: Automated Propagation of Column-level Comments

• Inheritance Mechanism: DLT automatically propagates column-level comments (not includes tags currently) from source tables to target materialized views and streaming tables. This ensures that valuable metadata is preserved throughout the data pipeline.
• Enhanced Data Understanding: Provides context and descriptions for data columns without manual intervention. Aids data scientists and analysts in quickly understanding the meaning of each field.
• Time-saving: Eliminates the need for manual re-documentation of column descriptions in derived tables. Reduces the risk of outdated or inconsistent metadata.

Handle Change Data Capture (CDC) with APPLY CHANGES API

Delta Live Tables provides robust support for Change Data Capture (CDC) through its powerful APPLY CHANGES API, offering a streamlined approach to handling data changes and maintaining historical records. This feature is particularly crucial for implementing Slowly Changing Dimensions (SCD) Type 1 (overwrite) and Type 2 (historical tracking), common requirements in data engineering.
The APPLY CHANGES API in DLT simplifies the process of applying incremental changes to target tables. It automatically handles the complexity of merging updates, inserts, and deletes from source systems into the target tables, reducing the need for custom ETL logic.

Limitations in Practice

Architectural Constraints: The Single Schema Dilemma in DLT Pipelines

One of the significant limitations of Delta Live Tables (DLT) lies in its rigid schema destination structure. Each DLT pipeline is confined to writing data to only one Unity Catalog (UC) schema destination. This constraint necessitates careful planning and configuration of the destination schema before the pipeline creation process begins. While DLT offers flexibility in reading data — allowing tables to be sourced from various catalogs, schemas, or storage locations and joined across them — its output is restricted to a single schema. This limitation can pose significant challenges, particularly for organizations with complex data architectures.

Impact on Medallion Data Architectures
The single-schema constraint doesn’t seamlessly align with “multi-hop” data architectures, such as the commonly used bronze-silver-gold model. In these architectures, data typically flows through different layers, each represented by a separate schema:

• Bronze: Raw, unprocessed data
• Silver: Cleaned, refined & transformed data
• Gold: Aggregated, analysis-ready data

With DLT’s current limitation, implementing this architecture within a single pipeline becomes impossible. Each tier would require its own DLT pipeline, potentially leading to:

• Increased complexity in pipeline management
• Additional overhead in data movement between pipelines
• Potential inconsistencies or delays between different data layers

Ecosystem Constraints: Restricted Third-Party Library Landscape

DTL notebooks currently do not support the use of third-party libraries. This limitation restricts data engineers to using only the built-in functions and libraries provided by the DLT environment. For complex data transformations or specialized processing and data ingestion that relies on external libraries, workarounds or alternative solutions must be implemented. This constraint can potentially impact the flexibility and efficiency of data pipelines, especially for teams accustomed to leveraging a wide range of Python libraries in their data processing workflows.

Security Constraints: Absence of Column-Level & Row-Level Security

Delta Live Tables does not currently support row-level security or column masking features. This limitation can be significant for organizations dealing with sensitive data or requiring fine-grained access control. Without these security features, implementing data privacy and compliance measures becomes more challenging within DLT pipelines. Organizations may need to implement these security controls at the data source or destination level, or create additional processes outside of DLT to ensure data protection. This gap in security features may limit DLT’s applicability in scenarios where strict data access controls are mandatory.

Git Constraints: No Direct Support for Remote Git Repositories

Unlike Databricks Workflows, DLT currently lacks native support for sourcing code directly from remote Git repositories. This limitation can complicate version control and collaborative development processes. Data engineering teams accustomed to working with centralized code repositories may find it challenging to integrate DLT into their existing development workflows. This constraint necessitates additional steps to manage and deploy code changes.

Programming Constraints: Language Segregation in Notebooks

One of the limitations of DLT is the inability to mix SQL and Python within the same notebook. Unlike standard Databricks notebooks that support magic commands (%) to switch between languages, DLT notebooks are language-specific. This means that data engineers must choose either SQL or Python for an entire DLT notebook in each DLT pipline, limiting the ability to leverage the strengths of both languages within a single pipeline. This constraint can lead to less flexible and potentially more complex pipeline designs, especially when dealing with tasks that would benefit from the combined power of SQL and Python.

Data Management Constraints: Limited Target Format Options in DLT

One of the notable constraints in DLT is its exclusive support for materialized views and streaming tables as target objects, rather than allowing direct creation of Delta tables. This limitation impacts how data is stored and accessed within DLT pipelines:

• Limited Control: Users have less direct control over the underlying storage locations, physical properties and optimization of the underlying Delta tables.
• Lack of Time Travel and Historical Features: Unlike standard Delta tables, Materialized Views in DLT do not support time travel capabilities or table history. This means DLT users have no ability to query data as it existed at a previous point in time. It also results in loss of historical change tracking and inability to access operation metrics for the view.
• Restricted Query Access: Materialized Views in DLT can only be queried by shared access clusters or SQL Warehouse. This restriction has several implications including limited flexibility in data access patterns, potential increased costs if additional shared clusters are needed and complications in integrating with existing data access workflows that may rely on different cluster configurations.

Conclusion: Navigating the DLT Landscape

Delta Live Tables represents a significant leap forward in simplifying and streamlining data pipeline development. Its declarative approach, automated optimizations, and built-in data quality controls offer substantial benefits to data engineering teams. However, as we’ve explored throughout this blog, DLT also comes with its share of limitations and constraints.

Factors to Consider When Adopting DLT

Before fully embracing Delta Live Tables, organizations should carefully evaluate the following factors:

• Additional Computational Costs: DLT’s automated management can lead to increased cluster usage and higher computational costs, especially for frequently updating pipelines.
• Cluster Requirements: The need for shared access clusters for querying Materialized Views may necessitate additional cluster provisioning and management.
• Pipeline Proliferation: The limitations in schema destinations and language mixing might result in a higher number of pipelines, increasing management overhead and costs.
• Integration Challenges: Assess how well DLT can integrate with existing data architectures, tools, and workflows in your organization.
  Scalability Considerations: Evaluate DLT’s performance and cost implications as data volumes and pipeline complexities grow.

Workaround Approaches

To address some of DLT’s limitations, consider the following workarounds:

1. Multi-Pipeline Strategy: Use multiple DLT pipelines to overcome the single-schema limitation, creating separate pipelines for bronze, silver, and gold data tiers.
2. Hybrid Approach: Combine DLT with traditional Databricks jobs for tasks requiring third-party libraries or mixed language use. Data Engineers can make good use of DLT’s data quality checking and Change Data Capture in the pipeline.
3. External Processing: Implement pre- or post-processing steps outside of DLT for operations not supported within DLT notebooks.
4. Custom Security Layers: Develop additional security measures at the data source or consumption level to compensate for the lack of row-level security and column masking in DLT.
5. Orchestration Tools: Utilize external orchestration tools like Airflow and dagster to manage complex workflows across multiple DLT pipelines and other data processes.

Final Thoughts

Delta Live Tables offers a powerful new approach to building and managing data pipelines, with the potential to significantly enhance productivity and data quality. However, it’s not without trade-offs.

Data Engineers must weigh DLT’s simplicity and automation against its limitations and potential additional costs. The optimal solution may lie in a hybrid approach — leveraging DLT where it shines while complementing it with traditional methods where more flexibility is needed.

As DLT evolves, some limitations may be addressed in future updates. A thoughtful, measured approach to DLT adoption can help organizations harness its benefits while navigating its current constraints, aligning the tool with specific use cases, architectural requirements, and long-term data strategy.