Building Reliable Data Pipelines Using DataBricks’ Delta Live Tables

The enterprise data landscape has become more data-driven. It has continued to evolve as businesses adopt digital transformation technologies like IoT and mobile data. In such a scenario, the traditional extract, transform, and load (ETL) process used for preparing data, generating reports, and running analytics can be challenging to maintain because they rely on manual processes for testing, error handling, recovery, and reprocessing. Data pipeline development and management can also become complex in the traditional ETL approach. Data quality can be an issue, impacting the quality of insights. The high velocity of data generation can make implementing batch or continuous streaming data pipelines difficult. Should the need arise, data engineers should be able to change the latency flexibly without re-writing the data pipeline. Scaling up as the data volume grows can also become difficult due to manual coding. It  can lead to more time and cost spent on developing, addressing errors, cleaning up data, and resuming processing.

Automating Intelligent ETL with Data Live Tables

Given the fast-paced changes in the market environment and the need to retain competitive advantage, businesses must address the challenges, improve efficiencies, and deliver high-quality data reliably and on time. This is possible only by automating ETL processes.

The Databricks Lakehouse Platform offers Delta Live Tables (DLT), a new cloud-native managed service that facilitates the development, testing, and operationalization of data pipelines at scale, using a reliable ETL framework. DLT simplifies the development and management of ETL with:

• Declarative pipeline development
• Automatic data testing
• Monitoring and recovery with deep visibility

With Delta Live Tables, end-to-end data pipelines can be defined easily by specifying the source of the data, the logic used for transformation, and the target state of the data. It can eliminate the manual integration of siloed data processing tasks. Data engineers can also ensure data dependencies are maintained across the pipeline automatically and apply data management for reusing ETL pipelines. Incremental or complete computation for each table during batch or streaming run can be specified based on need.

Benefits of DLT

The DLT framework can help build data processing pipelines that are reliable, testable, and maintainable. Once the data engineers provide the transformation logic, DLT can orchestrate the task, manage clusters, monitor the process and data quality, and handle errors. The benefits of DLT include;

Assured Data Quality

Delta Live Tables can prevent bad data from reaching the tables by validating and checking the integrity of the data. Using predefined policies on errors such as fail, alert, drop, or quarantining data, Delta Live Tables can ensure the quality of the data to improve the outcomes of BI, machine learning, and data science. It can also provide visibility into data quality trends to understand how the data is evolving and what changes are necessary.

Improved Pipeline Visibility

DLT can monitor pipeline operations by providing tools that enable visual tracking of operational stats and data lineage. Automatic error handling and easy replay can reduce downtime and accelerate maintenance with deployment and upgrades at the click of a button.

Improve Regulatory Compliance

The event log can automatically capture information related to the table for analysis and auditing. DLT can provide visibility into the flow of data in the organization and improve regulatory compliance.

Simplify Deployment and Testing of Data Pipeline

DLT can enable data to be updated and lineage information to be captured for different copies of data using a single code base. It can also enable the same set of query definitions to be run through the development, staging, and production stages.

Simplify Operations with Unified Batch and Streaming

Build and run of batch and streaming pipelines can be centralized, and the operational complexity can be effectively minimized with controllable and automated refresh settings.

Concepts Associated with Delta Live Tables

The concepts used in DLT include:

Pipeline: A Directed Acyclic Graph that can link data sources with destination datasets

Pipeline Setting: Pipeline settings can define configurations such as;

• Notebook
• Target DB
• Running mode
• Cluster config
• Configurations (Key-Value Pairs).

Dataset: The two types of datasets DLT supports include Views and Table, which, in turn, are of two types: Live and Streaming.

Pipeline Modes: Delta Live provides two modes for development:

Development Mode: The cluster is reused to prevent restarts and disable pipeline retries for detecting and fixing errors.

Production Mode: Cluster restart for recoverable errors such as stale credentials or memory leak and execution is retried for specific errors.

Editions: DLT comes in various editions to suit the different needs of the customers such as:

• Core for streaming ingest workload
• Pro for core features + CDC, streaming ingest, and table updation based on changes to the source data
• Advanced where in addition to core and pro features, data quality constraints are also available

Delta Live Event Monitoring: Delta Live Table Pipeline event log is stored under the storage location in /system/events.

Indium for Building Reliable Data Pipelines Using DLT

Indium is a recognized data engineering company with an established practice in Databricks. We offer ibriX, an Indium Databricks AI Platform, that helps businesses become agile, improve performance, and obtain business insights efficiently and effectively.

Our team of Databricks experts works closely with customers across domains to understand their business objectives and deploy the best practices to accelerate growth and achieve the goals. With DLT, Indium can help businesses leverage data at scale to gain deeper and meaningful insights to improve decision-making.

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