Explore Delta Live Tables
Delta Live Tables (DLT) are an integrated environment for managing and automating data pipelines, and are designed for large-scale data processing within Azure. Instead of defining your data pipelines using a series of separate Apache Spark tasks, you define streaming tables and materialized views that the system should create and keep up to date. DLT manages how your data is transformed based on queries you define for each process step.
DLT integrates with other Azure services such as Azure Data Lake Storage (ADLS), Azure Synapse Analytics, and Azure Machine Learning. This integration facilitates a unified approach to data engineering, allowing data to flow across various Azure resources. DLT abstracts the complexity of data pipeline management by automating the orchestration, monitoring, and reliability of data workflows. This managed approach allows data engineers to focus more on defining business logic and less on operational challenges.
Benefits of Delta Live Tables
Delta Live Tables provide a robust, scalable, and integrated solution for building and managing data pipelines in the cloud. It enhances productivity, improves data quality, and enables a more streamlined data infrastructure in a cloud-native environment.
Simplified development process
Pipelines in DLT are defined declaratively using Python or SQL, which simplifies the development process. By declaring what transformations should occur rather than how to execute them, you can quickly adapt to changing data requirements and business needs. DLT supports the implementation of data quality checks through expectations, which are customizable rules that the data must satisfy. If data doesn't meet the criteria specified in the expectation, DLT can automatically handle errors, either by logging issues, retrying operations, or skipping faulty records.
Built in tools
Azure Databricks provides comprehensive monitoring tools that help track the health and performance of DLT pipelines. Additionally, DLT includes automatic lineage tracking, which is crucial for debugging and understanding data transformations across complex workflows. DLT pipelines are optimized for performance in Azure, and use Delta Lake’s transactional capabilities to efficiently handle large volumes of data. These capabilities include features like scalable partition handling and optimized file management, which reduce bottlenecks and improve data processing speeds.
Dynamic scaling
DLT can dynamically scale resources based on a workload, which means that it can handle increases in data volume without manual intervention. This flexibility makes it suitable for businesses experiencing rapid growth or seasonal fluctuations in data processing needs.