The role of pushdown architecture: Unlock speed, security and cost savings in data pipelines

Efficient data pipelines are critical for accurate and timely insights in every data-driven environment.

A pushdown ELT (Extract, Load, Transform) approach defers transformations until data is loaded into the cloud platform. This not only accelerates data processing but also minimizes costs and optimizes resources. 

Furthermore, executing in-database transformations enhances security and reduces data movement, maintaining integrity within a unified, scalable framework.

This article will describe how a pushdown ELT architecture takes advantage of the compute power embedded within advanced cloud data platforms such as data warehouses and data lakehouses. 

It will also describe how ELT harnesses cloud environments' inherent scalability and massive parallel processing (MPP) capabilities, dramatically boosting the efficiency of data pipelines.

Enhanced Data Transformation Efficiency

In a pushdown ELT architecture, the transformation phase leverages the compute power of advanced data storage systems—a cloud data platform such as a data warehouse or a data lakehouse. 

This optimizes the efficiency of data pipelines without adding complexity. Offloading transformation tasks to the data source, whether a data warehouse or cloud storage, using the computational power inherent in these systems efficiently.

When raw data is extracted and loaded into a cloud data warehouse, the cloud’s massive and scalable compute capability immediately comes into play. All such platforms are built around the massively parallel processing (MPP) paradigm, which means parallelism without the need for specialist coding efforts. 

In contrast to on-premises servers, which may need to balance transactional handling and data transformations, cloud data warehouses focus on high-speed, parallel processing. Their columnar database structure facilitates faster index and record location operations, enhancing overall transformation speed.

In an ELT workflow, data is streamed via intermediate cloud storage upon extraction, and then loaded directly into the target cloud data warehouse. Transformations are performed by executing SQL scripts directly on the MPP database, harnessing multiple nodes to process transformations concurrently. 

All this concurrent processing accelerates transformation tasks, ultimately leading to quicker data availability and faster load times.

Cost and Resource Optimization

Pushdown ELT architecture significantly reduces costs and optimizes resources by removing the need for separate ETL infrastructure. 

Instead of extracting data, transforming it on a standalone ETL server, and then loading it into a data warehouse, ELT leverages the intrinsic power of the data warehousing platform itself to perform transformations. 

This approach eliminates the need for additional (and costly) ETL hardware and infrastructure, resulting in operational cost savings and reduced configuration overhead.

Modern cloud-based data warehousing platforms are designed to efficiently handle in-database transformations. 

Tasks that once required expensive, disconnected servers are now handled natively within the platform. This leads to enhanced workload performance, shorter development cycles, and immediate data availability for business analysis.

By keeping the data within the cloud platform for all stages of processing—from raw to cleansed to aggregated—ELT avoids the significant costs and latency involved in data transmission between disparate systems.

Cloud platforms offer scalable storage capacities where raw, cleansed, and aggregated data coexist, providing the flexibility to query any data stage as business needs evolve without incurring the costs of moving data.

Improved Security and Minimized Data Movement

Using a pushdown ELT architecture enhances security and minimizes data movement by keeping data transformations within the data warehouse environment. By eliminating the need to move data across the network or store it (even temporarily) in multiple places, this approach effectively reduces the possible attack surface.

Data always stays within the confines of the cloud data platform, using its inherent security features, including role-based access control, encryption, and fully managed backups. 

ELT further simplifies the workflow by deferring transformations until after the data has been loaded into the data warehouse. This helps find the necessary balance between security and accessibility.

This is very much in contrast to traditional ETL processes, which require data to be extracted and loaded into external staging areas for transformation. Transmitting and storing data on multiple systems increases costs and introduces latency and potential security vulnerabilities.

Lastly, the fully managed nature of cloud data warehouses means that security practices such as disaster recovery are handled by dedicated experts, eliminating the need for specialized in-house skills. 

Scalability and Performance Boost

Pushdown ELT enhances scalability and performance by leveraging the horizontal scaling capabilities of a modern cloud data platform. 

Unlike traditional ETL processes, which conduct transformations before loading data into the destination database, ELT defers transformation tasks until after the data is loaded into the cloud-based data warehouse. This pivot is crucial for managing large datasets efficiently.

Cloud data platforms provide nearly unlimited compute power. They capitalize on scalable and distributed architectures, allocating compute resources dynamically based on workload demands. This eliminates the bottleneck of pre-load transformations on limited hardware, allowing for superior performance even as data volume grows.

By offloading transformation tasks to the cloud, data processing can harness the parallel processing capabilities of these platforms. The result is faster query performance and effective utilization of resources. 

In this setup, businesses benefit from cost-efficient scaling and advanced processing power, all while keeping data pipelines simple.

Conclusion

Pushing down data transformations utilizes the computational power of advanced cloud data platforms—such as your data warehouse or data lakehouse. Data processing takes full advantage of the built-in MPP capabilities, delivering optimal efficiency.

By eliminating standalone ETL servers, pushdown ELT reduces infrastructure costs and maximizes resource utilization, with transformations executed using SQL directly on MPP databases. 

This makes pushdown ELT architecture essential for agile and scalable analytics, as it streamlines ingestion and reduces operational overhead while also offering a fortified security posture.

Matillion is the data integration platform that empowers data teams to build and manage pushdown pipelines faster for AI and analytics, solving complex problems at scale. 

Plus, Matillion also accelerates productivity and collaboration through its code-optional environment, allowing users to build ELT pipelines using a blend of pre-built components or custom code in SQL, Python, or DBT. 

Moreover, Matillion leverages the processing power of hyperscalers and integrates seamlessly with cloud data platforms, CDPs, and LLMs. 

It's intuitive UI, combined with first-class Git integration, supports asynchronous workflows, while its hybrid SaaS deployment and extensive no-code connectors democratize access to advanced data engineering and AI capabilities.