Introduction
Databricks, as we know it today, comes with a range of powerful features, one of which is the workflow engine. Databricks’ native workflow management system lets you create jobs that help orchestrate data movement and manage data estates. While it offers tools for managing and governing workflows, engineers often seek ways to enhance their experience when working with dependency management and library configuration within Databricks Jobs.
Additionally, Databricks Jobs development involves several important steps—from code writing and debugging to performance tuning and configuring job and task-level parameters. Managing and integrating data sources requires expertise and attention to detail, which can impact development timelines for teams working with complex data pipelines.
In today’s fast-paced data environment, there’s a growing opportunity for complementary developer-first data engineering tools — ones that build upon and extend Databricks’ capabilities to further simplify and streamline workflow management.
Meet a smarter workflow companion: DataNimbus Designer.
DataNimbus Designer: A Smarter Workflow Companion for Databricks
DataNimbus Designer is a powerful data engineering tool that offers enhanced ETL and Workflow management capabilities on top of Databricks. It enables users to build data pipelines, perform data quality checks, and orchestrate large-scale workflows within Databricks through a comprehensive No-Code platform with simple drag-and-drop functionality.
Building upon native Databricks Workflows, DataNimbus Designer enhances capabilities by providing visual design, management, and monitoring of workflows through intuitive flowcharts, eliminating the need to manage underlying data operations or dependencies. The platform supports deploying multiple workflows simultaneously, offers real-time job run monitoring with detailed task-level logs, and provides readily available run-level insights- enabling efficient debugging and faster resolution of errors throughout the ETL process.
With its extensive suite of in-built features and reusable blocks, DataNimbus Designer significantly accelerates workflow development in Databricks. Users can easily customize workflow parameters to meet specific business logic requirements and design custom ETL workflows tailored to unique data needs. Unlike native Databricks Workflows, DataNimbus Designer provides built-in connectors to various data platforms, benefiting from seamless connectivity to a variety of data sources, support for real-time Spark transformations, and flexible data storage options.
DataNimbus Designer intelligently handles Spark configurations, dependency injection, library management, workflow optimization, and parameter tuning behind the scenes. Developers gain access to fast and optimized Spark code that’s been pre-tested and ready to implement in their ETL workflows, eliminating the need to write underlying code from scratch. One of DataNimbus Designer’s most compelling feature is how it extends Spark’s native lazy evaluation capabilities to new heights, further optimizing performance and resource utilization.
The result is a plug-and-play experience specifically designed to enhance productivity, reduce engineering overhead, and accelerate development cycles while reducing overall costs.
How DataNimbus Designer Enhances Databricks Data Workflows?
Optimizing Workflow Execution with “Prod Mode”
In native Databricks Workflows, data must be written to a central staging layer (like Unity Catalog) to move from one task to another. DataNimbus Designer eliminates this dependency by leveraging Apache Spark’s lazy evaluation through a feature called “Prod Mode.”
When a workflow is published in Prod Mode, DataNimbus Designer compacts all tasks into a single Databricks Workflow task. This allows direct data flow between blocks without intermediate writes, significantly reducing execution overhead. The system preserves transformations in Spark’s optimizer until an action is performed—typically at the final write stage of the ETL process. This approach fully utilizes Spark’s lazy evaluation capabilities.
Key Benefits of Prod Mode:
- Removes the need for staging layers
- Fully leverages Spark’s lazy evaluation
- Reduces workflow runtime by up to 50%
Here’s a visualization of the native Databricks workflow and the DataNimbus Designer Prod Mode published on Databricks
Native Databricks Workflow:
DataNimbus Designer Prod Mode Workflow:
Connector Marketplace
DataNimbus Designer comes with an extensive library of built-in connectors to extract and load data from various data sources. These connectors feature Auto-Discovery capability where you simply point to the data sources and DataNimbus Designer intelligently detects all the underlying databases and tables and loads them into the desired destination tables of your choice.
Some of the connectors offered by DataNimbus Designer are:
- MongoDB
- Cassandra DB
- AWS Keyspaces
- AWS RDS
- AWS S3
- Apache Solr
- Azure Blob Storage
- PostgreSQL
- MySQL
Here’s a snapshot of some of the in-built connectors offered by DataNimbus Designer:
Reusability of Components Across Pipelines
DataNimbus Designer is built for reusability. It comes with a rich library of pre-built Spark connectors and transformation blocks—tested, optimized, and ready to use across multiple pipelines. These blocks can be reused with different parameters, eliminating repetitive coding and reducing development time significantly compared to native Databricks workflows. Each block comes pre-configured with Spark optimization and parameter tuning for optimal performance.
Developers can also incorporate custom code for tasks not covered by default. Once added, these custom blocks become reusable components across workflows—promoting consistency and flexibility to build a tailored ETL workload for specific needs.
Custom blocks offer a high degree of flexibility within a workflow, enabling users to plug in their own code to meet specific business needs. Unlike predefined tasks, custom blocks empower users to define completely custom behavior. Whether it’s sending an email, logging a message to a database, or performing more complex operations, users can bring in their own Python code and execute it seamlessly as part of the workflow.
Some common use cases for Custom blocks include:
- Logging a message to a queue
- Sending notifications or alerts via email
- Starting a compute cluster
- Running custom jobs
- Triggering external APIs
Essentially, if it can be scripted in Python, it can be done inside a custom block, making them an extremely powerful tool for handling dynamic or specialized requirements in data workflows.
Because the logic inside a custom block is written in Python, users benefit from Python’s vast ecosystem of libraries and frameworks. From database connectors and web request handlers to ML tools and system utilities, virtually anything achievable in Python can be embedded directly into the workflow. This makes custom blocks a vital tool for teams looking to operationalize complex logic, integrate legacy systems, or simply inject flexibility into otherwise rigid automation pipelines.
Ultimately, custom blocks act as a powerful extensibility mechanism that gives users complete control, allowing them to handle edge cases, perform advanced transformations, or build deeply integrated data workflows without being constrained by the limitations of standard task types.
Taking it a step further, DataNimbus Designer allows grouping multiple blocks into custom composite blocks. These can then be reused for common patterns, further accelerating pipeline development and promoting standardization.
Here’s a snapshot of custom blocks:
Native Integration with Databricks Notebooks and Jobs
A core strength of DataNimbus Designer is its native integration with Databricks Notebooks and Jobs. Every task built in DataNimbus Designer is automatically converted into a dedicated notebook, enabling a modular and scalable workflow design.
These notebooks—whether prebuilt connectors, transformation blocks, or custom code—are deployed as part of a Databricks Job. This seamless deployment ensures that ETL workflows built in DataNimbus Designer run natively on Databricks, with full access to real-time execution, monitoring, and autoscaling.
Databricks Jobs then efficiently orchestrate and schedule these notebook tasks, providing teams a unified method to manage even the most complex pipelines—without losing the benefits of Spark or the flexibility of Databricks.
This integration creates a powerful ecosystem where organizations can leverage the full potential of both platforms—DataNimbus Designer’s visual development and optimization capabilities alongside Databricks’ scalability and performance—ensuring efficient management of data-driven workflows.
Key Advantages of DataNimbus Designer for Databricks Workflows
DataNimbus Designer provides significant enhancements to the Databricks ecosystem through these core capabilities:
- Faster ETL Development: Unlike native Databricks workflows, DataNimbus Designer automates the creation of optimized Spark code, significantly reducing manual coding and debugging time. This accelerates the ETL development process, enabling developers to build workflows faster and more efficiently. Developers can build the same ETL workflow up to 60% faster compared to native Databricks Workflows.
- Real-time Monitoring and Debugging: DataNimbus Designer provides real-time dashboards and detailed logs for each task in the workflow, making it easier to monitor and debug issues throughout the ETL process.
- Seamless Integration with Multiple Data Platforms: DataNimbus Designer offers a wide range of pre-built connectors for various data platforms, which are not directly available in native Databricks workflows. This eliminates the need to write custom code for integrating diverse data sources, making DataNimbus Designer a more versatile solution for connecting to various data platforms.
- Optimized Spark Code for ETL: DataNimbus Designer provides Spark-tested and pre-optimized code that can be directly incorporated into ETL workflows. This removed the need for developers to manually write complex Spark code, which is entirely required in native Databricks workflows, saving time and reducing the potential for errors.
- Advanced Use of Spark’s Lazy Evaluation: DataNimbus Designer enhances Spark’s lazy evaluation, allowing for transformations without intermediate writes to staging layers. Native Databricks workflows require data to be written to a central location, such as Unity Catalog, between tasks, which adds unnecessary complexity and storage overhead. DataNimbus Designer eliminates this issue by passing data between tasks without requiring staging writes, improving overall workflow efficiency.
- Improved Workflow Performance: By utilizing DataNimbus Designer’s Prod Mode, the entire ETL workflow can run up to 50% faster than native Databricks workflows. This performance boost is due to the efficient execution model in DataNimbus Designer, which leverages Spark’s optimizer to minimize redundant steps and unnecessary data writes.
- High Reusability and Customization: DataNimbus Designer excels in reusability, offering pre-built Spark connectors and transformation blocks that can be used across multiple pipelines. Unlike Databricks, where you often need to build and optimize these blocks manually, DataNimbus Designer lets you reuse components, reducing development time. Additionally, DataNimbus Designer allows developers to create and reuse custom blocks tailored to specific needs, enhancing flexibility and scalability far beyond what Databricks alone offers.
DataNimbus Designer fundamentally transforms how teams work with Databricks by addressing the most time-consuming aspects of data engineering. By automating code generation, optimizing performance, enhancing monitoring capabilities, and simplifying integration with diverse data sources, DataNimbus Designer allows data teams to focus on business outcomes rather than technical implementation details.
