From ETL to ELT to Real-Time: Modern Data Engineering with Databricks Lakehouse

The data engineering landscape has rapidly changed over the past few years, shifting from the classical ETL (Extract, Transform, and Load) model to the more modern ELT (Extract, Load, Transform) model. In the ETL approach, data was transformed before being stored, which reduced flexibility. ELT reverses this process by first loading raw data into data lakes or warehouses and then transforming it within these environments, enabling more agile, on-demand analytics. However, as data volumes and business requirements have increased, ELT has become inadequate for many real-time use cases.

Today, organizations need rapid access to insights to maintain operational agility, which has led to a growing demand for real-time data processing capabilities. Leading this shift is the Databricks Lakehouse solution, which provides a unified framework that combines the strengths of data lakes with the power of data warehouses. This fully integrated platform enables organizations to move quickly, make data-driven decisions, and maintain flexibility across diverse workloads.

Through continuous innovations like Delta Live Tables, enhanced streaming, and LakeFlow orchestration, Databricks is transforming how modern enterprises use data to gain a strategic advantage.

The Evolution: From a Classic ETL to ELT to Real-Time

Conventional practices of data integrations used to be ETL where data was extracted, and transformed into useful form for loading into data warehouse for analysis. Though this technique served quite well for structured data, it could not live up to the increasing volumes and complexities of modern datasets. As scalable storage solutions and state-of-the-art processing engines started reaching the market, the paradigm shifted towards the ELT processes in which data is first loaded somewhere and then immediately transformed on demand into a data warehouse or lake. This change has brought more flexibility, data retrieval in a way that it is fast to access; thus, it allows for a better management of varied datasets which has empowered agile and scalable data processing.

The demand for real-time data processing is now more important than ever in today's business world. With an aim at maintaining competitiveness and responsiveness, basing the analysis of data on batch processing is no longer adequate. Real-time data processing allows businesses to process data in real time and comprehend it as it is received; that is, to make immediate decisions based on relevant data, thereby enhancing responsiveness and, in turn, competitiveness. By streamlining data in real time, firms can follow real-time metrics, spot problems immediately, tweak operations on the fly, and as a result create a more flexible and well-informed decision-making environment.

Databricks Lakehouse: A Unified Platform

Databricks Lakehouse has incorporated the best features in data lakes and data warehouses, making it a unified platform used for data engineering, machine learning, and analytics. Key components include:

• Delta Lake 4.0: Provides more robustness and better performance, includes Delta Lake Uniform for interoperability between Delta and Iceberg formats, and supports the VARIANT type to better process semi-structured data. 
• Apache Spark 4.0: Delivers major improvements such as ANSI mode by default, polymorphic Python UDTFs, and structured logging, which improve overall data processing capabilities. 

Introducing Databricks LakeFlow

Announced at the Data + AI Summit 2024, Databricks LakeFlow is a unified solution designed to seamlessly ingest, transform, and orchestrate data within the Lakehouse platform. Built to simplify complex data engineering workflows, LakeFlow integrates data pipelines, scheduling, and monitoring into a single interface, enhancing productivity and streamlining operations.

• LakeFlow Connect: Eases the process of data ingestion from different platforms such as databases, enterprise systems, and cloud services. 
• LakeFlow Pipelines: Offers efficient and declarative pipelines for batch as well as real-time data processing.
• LakeFlow Jobs: Guarantees consistent orchestration of workloads and maintains a complex relationship of dependencies and conditional execution of tasks. 

Improvement in Delta Live Tables (DLT)

It is possible to say that Delta Live Tables (DLT) has become a kind of backbone for reliable and scalable data pipelines. As of 2025, Delta Live Tables have grown a lot, and they offer even more powerful capabilities to make pipeline management more intuitive and intelligent. Key advancements include:

• No-Code Approach: Allows users to define data transformations using simple SQL commands, making it accessible to a broader audience.
• Real-Time Data Quality Monitoring: Integrates data quality checks into the pipeline, ensuring accuracy and completeness.
• Integration with Unity Catalog: Enables fine-grained data governance and access control across data assets. 

Enhanced Streaming Capabilities

Major improvements in streaming capabilities of Databricks have allowed businesses to ingest and process real-time data very efficiently and on a large scale. These enhancements address the enhanced need for real-time analytics, providing access to various kinds of data sources and workflows. Through optimization of data ingestion, processing, and security, Databricks makes it easier for organizations to derive immediate insights and ignite decision-making at scale.

• Support for Apache Pulsar: Structured Streaming now supports Apache Pulsar, expanding the ecosystem of streaming sources.
• Streaming Reads from Unity Catalog Views: Allows streaming data directly from views registered with Unity Catalog, facilitating real-time analytics.
• Azure Active Directory Authentication: Enhances security by supporting AAD authentication for Kafka connectors with Azure Event Hubs. 

Embracing Generative AI and Data Intelligence

Databricks is bringing in generative AI to improve data engineering workflows by automating complicated work like pipeline generation, SQL query optimization, and document generation. Using features such as Databricks Assistant, which is driven by generative models, engineers can speed up development, cut the number of manual coding mistakes, and become more productive. These AI capabilities assist in streamlining data transformations, provide intelligent suggestions, and allow users to interact naturally, making it easier for technical and non-technical users to manage and scale data pipelines easily within the Lakehouse platform.

• Automatic Data Tagging: Utilizes AI to tag and set policies on incoming data, streamlining data governance.
• AI-Assisted Development: Provides UI assistance for coding tasks, error diagnosis, and understanding governance policies.
• Data Intelligence Platform: Aims to understand data semantics, assisting users in navigating and querying data effectively. 

Conclusion

The trend towards moving from the classic ETL processes (Extract, Transform, Load) to the ELT model (Extract, Load, Transform), and to real-time data processing, is a sign of the growing demand for agility and speed of the active business world of today. Several years ago, data would be run in a planned batch, which would sometimes result in a delay between the period the data was being collected and when data was actionable. ELT allowed organizations to load initial raw data first, then transform it directly in modern cloud data platforms, gaining higher degrees of flexibility and scalability. Databricks enables companies to utilize a wide range of data types, including level of storing, managing and scaling advanced analytics, machine learning and streaming. There are innovations—Delta Live Tables for declarative data pipelines, strengths around streaming, and so forth for Databricks to make organization possible to maximize the value of data in real time, optimize the operational efficiency, accelerate the decision making process, and facilitate truly data-driven culture in the organization.