Streamlining Databricks Solutions with BimlFlex Data Automation

Managing data pipelines and integrating diverse data sources into a unified platform remains one of the biggest challenges for data teams today. Many organizations struggle to efficiently ingest, stage, and prepare data for analytics without creating unnecessary complexity or incurring high operational costs.

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The need to balance automation with flexibility, especially in cloud environments like Databricks, calls for solutions that simplify setup while providing control over data flows and compute resources.

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Data professionals working with Databricks often face issues such as connecting multiple data sources, managing persistent data lakes, optimizing compute usage, and supporting different development environments. Handling these tasks manually or with fragmented tools can slow down delivery and increase the risk of errors.

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This article addresses these common pain points by outlining a practical approach to Databricks persistent staging solutions using automation frameworks like BimlFlex. It explains how to set up connections, orchestrate pipelines, and manage data efficiently in Databricks, with a focus on automation that integrates seamlessly with Azure Data Factory and GitHub repositories.

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The goal is to provide a clear, actionable guide that helps data teams reduce complexity, accelerate workflows, and maintain control over their data environments.

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Challenges in Data Integration and Staging for Databricks

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Data integration projects often involve multiple source systems, each with different formats and protocols. Common sources include SQL Server databases, flat files (CSV, JSON), REST APIs, and FTP or SFTP servers. On top of that, parquet files are frequently used for efficient storage and querying in cloud environments.

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One of the critical challenges is setting up a reliable landing zone where raw data can be stored persistently before transformation. Without a persistent landing area, data pipelines risk overwriting or losing original files, which complicates auditing and troubleshooting.

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Another pain point is managing compute clusters efficiently.

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Running Databricks clusters continuously can be expensive, so it’s important to activate compute resources only when necessary, such as when new data becomes available.

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In addition, supporting multiple environments such as development, testing, and production requires flexible configuration management to avoid errors and streamline deployment.

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Setting Up Connections and Landing Zones

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The first step in building a Databricks persistent staging solution is to establish connections to all your data sources. These connections include:

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• Source connections: Access to SQL Server data, flat files, REST APIs, FTP/SFTP systems, and parquet files.
• Landing connection: A data lake location where all imported data files are stored initially.
• Persistent landing connection: An optional but recommended area in the data lake to preserve original files for auditing and reprocessing.
• Compute connection: Enables access to Databricks compute clusters, allowing for scalable processing. This can support multiple clusters for different workloads.
• ODBC connection: Used as a placeholder for integration stages and metadata import from Databricks, useful for managing pipeline orchestration.

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By configuring these connections at the start, you create a solid foundation that supports both data ingestion and processing workflows while ensuring data integrity.

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Data Extraction and Staging Workflow

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Once connections are in place, you define how data will flow through the system. A typical configuration includes:
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1. Extracting data from source systems. This involves querying or pulling data from SQL Server, APIs, or file systems.
2. Landing the data. Saving extracted data as parquet files in the data lake, preserving the original data files for future use.
3. Persisting data. Archiving the original files to a persistent landing zone to prevent loss or accidental modification.
4. Staging the data. Using Databricks compute clusters to process and prepare the data for downstream analytics or loading.

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This workflow supports both full data loads and incremental (delta) loads. For initial loads, a full load is preferred, ingesting all historical data without comparisons. For ongoing loads, delta processing identifies only new or changed records, optimizing performance and reducing compute costs.

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Automation with Azure Data Factory and Databricks Notebooks

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Automation is key to managing data pipelines efficiently. This solution leverages Azure Data Factory (ADF) to orchestrate data extraction and loading activities. Once the project is configured and built, all necessary ADF pipelines and Databricks notebooks are generated automatically.

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Key features of the automation include:

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• High Watermark Lookup: ADF pipelines use high watermark values to extract only the most recent data, avoiding redundant data processing.
• Conditional Copy Activities: Pipelines check if data exists before triggering copy operations, preventing unnecessary resource usage.
• Databricks Cluster Optimization: Databricks clusters are started only if new data was extracted, reducing compute costs.
• Databolt Integration: Support for Databolt automates staging processes for both source and persistent staging layers, running dedicated notebooks if enabled.
• File Archiving Options: Users can toggle settings to archive extracted files, enabling persistent storage and cleanup of staging areas.
• Global Parameters: ADF global parameters allow environment-specific configurations, making it easy to switch between development and production setups without changing the core pipelines.

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Managing Databricks Artifacts and Scripts

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All generated artifacts, including ADF pipelines and Databricks notebooks, are stored in a GitHub repository. This setup supports continuous integration and deployment (CI/CD) workflows, allowing teams to maintain version control and streamline releases.

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Within the Databricks workspace, the repository is organized clearly:

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• Databricks folder: Contains all Databricks-related artifacts.
• Tables folder: Holds scripts for creating and managing tables in the staging environment.
• Python scripts: Designed to deploy table DDL notebooks and manage data loading processes.

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The Python scripts are parameterized to support different catalogs or environments, providing flexibility to customize deployments. For example, a Python script controls whether a full data load or incremental delta load should be executed, based on project requirements.

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SQL scripts handle the actual data loading, using native Databricks code optimized for performance and scalability.

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This modular approach allows teams to adapt the solution to their specific needs without rewriting core components.

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Benefits of a Persistent Staging Solution in Databricks

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Implementing a persistent staging solution provides several advantages:

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• Data Integrity: Original data files are preserved in a persistent landing zone, enabling auditability and reprocessing if needed.
• Cost Efficiency: Compute clusters run only when necessary, minimizing cloud expenses.
• Automation: Seamless integration with Azure Data Factory and Databricks notebooks automates complex workflows.
• Flexibility: Supports multiple data sources, various load types (full and delta), and environment-specific configurations.
• Version Control: All scripts and pipelines are stored in GitHub, supporting CI/CD practices and collaboration.

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This approach helps data teams focus on delivering insights rather than managing infrastructure or manual processes.

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It also reduces the risk of errors and accelerates time-to-value by automating repetitive tasks and providing a clear, maintainable framework.

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Conclusion

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Handling diverse data sources and managing data pipelines in Databricks can be complex and resource-intensive. A well-designed persistent staging solution that leverages automation frameworks like BimlFlex can simplify these challenges. By setting up robust connections, automating data extraction with Azure Data Factory, and organizing Databricks notebooks and scripts efficiently, data teams can reduce operational overhead and improve pipeline reliability.

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This solution supports both initial full loads and ongoing delta processing, preserves original data files for auditing, and optimizes compute cluster usage. Additionally, managing all artifacts in GitHub enables continuous integration and deployment, making it easier to maintain and evolve the data platform over time.

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For organizations looking to make the most of their Databricks investment while maintaining control and flexibility, adopting an automated persistent staging approach offers a practical path forward.

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Schedule a BimlFlex demo today and start automating your Databricks solutions tomorrow.