Is Delta Live Tables Key Ingredient in Your Modern Data Stack?

In the past few years, we have gained insights into the advantages and disadvantages of each approach, leading us to discover a new framework that combines the strengths of both approaches. By introducing Delta Live Tables, data teams can harness the substantial processing capabilities offered by Databricks, all the while ensuring the user-friendly nature inherent in the Modern Data Stack is preserved. Additionally, we will discuss the implications of this development for the future of data engineering and outline the subsequent steps that your organization should contemplate.

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Analyze your data with Apache Spark  

Data engineers have harnessed the potential of distributed computing through Apache Spark, a framework that empowers efficient transformation and aggregation operations on extensive and intricate datasets. This is achieved by distributing the data across multiple nodes in a cluster. Apache Spark also brings versatility to the table by supporting both streaming and batch data processing, offering flexibility to accommodate various data processing requirements.  

As a result of its scalability, Spark has been the leader in big data engineering in recent years. Its compatibility with the lakehouse architecture enables the use of SQL at the end of pipelines as well.  

While Spark displays remarkable performance and aptness for handling big data tasks, it has often been perceived as excessively intricate. The behaviour of Spark dataframes differs from that of conventional dataframes due to the distributed nature of the framework. Engineers must exercise additional caution regarding data partitioning across the cluster to optimize shuffling during transformations. Furthermore, incorporating machine learning models into this partitioned data introduces further complexity to ensure their smooth execution.  

Moreover, before an engineer can commence their work with these tools, they are required to undergo a complex infrastructure setup procedure, which involves configuring the necessary software and hardware components for the environment.  

Simplify Data discovery with SQL-Centric tools  

On the contrary, organizations have embraced simplicity over processing power by adopting SQL-centric tools, many of which are categorized under the collective term "Modern Data Stack." This ecosystem has flourished alongside the rapid proliferation of the data build tool (dbt), enabling analysts to construct data pipelines solely using SQL Select statements that reference one another. The tool delegates the execution of this SQL code to popular cloud data warehouses like Amazon Redshift, Snowflake, GCP (Google Cloud Platform) BigQuery, and Azure Synapse. As a result, a new role has emerged, known as the analytics engineer, which combines the data analyst's proficiency in asking pertinent questions with the data engineering expertise required for building reusable pipelines.  

Interestingly, by also building in a data testing framework and providing the ability to document data during development, dbt often leads to better DataOps processes than traditional tooling used by data teams. However, dbt has limitations around the types of processing allowed (batch-only, SQL-only) that can be confining, and many of its advantages are tied to it being used end-to-end.  

Delta Live Tables brings together and consolidates these disparate approaches 

Databricks has recently introduced Delta Live Tables (DLT), a unified and scalable Spark platform that effectively bridges the divide between these two approaches. DLT provides a native implementation of the Modern Data Stack within the Databricks ecosystem. By combining the efficiency and resilience of the Spark framework with the user-friendly nature and software best practices of the Modern Data Stack, DLT empowers data engineers to construct dependable and well-managed data pipelines.  

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Introducing Delta Live Tables: A Comprehensive Overview  

In Delta Live Tables (DLT), the primary unit of execution is a pipeline. However, unlike traditional extract, transform, and load (ETL) pipelines that are defined as a sequence of Spark operations, DLT pipelines are composed of queries. By specifying the data source and target schema, users can leverage queries, while DLT takes care of orchestrating the intermediate data transformations. This approach simplifies the development process by abstracting away the complexity of managing the data flow.

DLT provides the capability to enforce data quality by utilizing expectations. These expectations enable users to incorporate sanity checks at various stages of the pipeline and define error-handling mechanisms for cases where these checks fail. Additionally, DLT offers several other advantages, such as supporting both streaming and batch data processing through a unified API, enabling pipeline monitoring via the DLT user interface, ensuring chain dependency of dataframes to automatically propagate updates downstream, and allowing users to seamlessly switch to PySpark for more intricate operations when needed.
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To further illustrate the differences, here is a feature comparison between DLT and dbt:  

DLT:  

• Unified and scalable Spark platform  
• Pipeline execution using queries  
• Automatic orchestration of data transformations  

• Data quality enforcement through expectations  
• Support for streaming and batch data processing with a single API  
• Monitoring of pipelines through the DLT user interface  
• Chain dependency of dataframes for propagating updates  
• Seamless integration with PySpark for advanced operations  

dbt:  

• SQL-centric data build tool  
• Data pipeline construction using SQL Select statements  
• Independent management of software and hardware setup  
• Integration with cloud data warehouses (Amazon Redshift, Snowflake, GCP BigQuery, Azure Synapse)  

• Role of analytics engineer combining data analyst and data engineering skills  

These features highlight the unique strengths and capabilities of both DLT and dbt in the data engineering landscape.

What does this mean for the future of Data Engineering?  

Delta Live Tables achieves a significant milestone by unifying data engineering, analytics engineering, and data science within a shared platform. This integration, combined with the comprehensive Databricks platform (including Notebooks, on-demand compute clusters, and MLFlow), facilitates end-to-end collaboration among these three roles without compromising the computational power necessary for handling complex workloads. Each role can operate within their preferred interface, allowing data engineers to write Spark code in Notebooks or their preferred integrated development environment, analytics engineers to write SQL in a native interface, and data scientists to utilize Python in integrated Notebooks. The Databricks platform seamlessly handles automated orchestration and performance optimizations by executing these operations on a consolidated platform.


Data teams benefit from the ability to collaborate seamlessly, leading to faster delivery of projects. When teams work together effectively, they can speed up moving projects into production. In numerous organizations, the timeline for successfully transitioning advanced machine learning-based data products to production can span approximately two months. Most of this time is typically consumed by the integration of various components and the deployment phase.


The shift towards a more collaborative future within data teams represents a significant advancement. However, the primary motivation behind developing these functionalities lies in the creation of data products, and their successful adoption relies on establishing trust. Consequently, an important subsequent step would involve integrating Delta Live Tables (DLT) into the broader ecosystem to facilitate the emerging field of data reliability engineering and address the challenges associated with building data trust. This integration process would entail incorporating DLT into the Databricks Unity Catalog to enhance data governance and discoverability, improving lineage and reporting integration within the MLFlow Stack, and integrating Databricks SQL with visualization tools like Mode Analytics. These changes are crucial for ensuring seamless integration, comprehensive lineage tracking, and effective data visualization to strengthen the overall data reliability and trust-building efforts.

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Next Steps  

Data teams no longer must choose between power and simplicity when implementing a modern data ecosystem. Delta Live Tables, combined with the broader Databricks platform, provide a unique opportunity to boost the speed of developing data products, while still adhering to software best practices.  

When considering the adoption of Delta Live Tables (DLT) within your organization, it is important to identify the specific teams or pipelines that could gain significant advantages from a unified approach. Pay attention to the hand-offs between team members or pipeline steps that are currently time-consuming and cause severe bottlenecks. By pinpointing these pain points, you can determine the areas where DLT can provide the most value.  

To discover how to effectively leverage DLT for your data products and overcome these challenges, we encourage you to reach out to us. Our team can provide guidance and insights tailored to your organization's unique requirements, enabling you to maximize the benefits of DLT within your data engineering workflows.