The power of remote engine execution for ETL/ELT data pipelines

Business leaders must actively implement generative AI or risk damaging their competitive advantage. But companies scaling AI face barriers to entry.. Organizations need trusted data for powerful AI models and accurate insights, but the current technology landscape presents unparalleled data quality challenges.

According to the International Data Corporation (IDC), stored data is expected to grow 250% by 2025, with data rapidly propagating across on-premises and clouds, applications, and locations, all with reduced quality. This situation exacerbates data silos, increases costs, and complicates the governance of AI and data workloads.

The explosion of data volumes in a variety of formats and locations and the pressure to scale AI are creating a daunting task for those responsible for deploying AI. Before data can be used with AI models, data from multiple sources must be combined and harmonized into a unified and consistent format. Integrated and managed data may also be used for a variety of analytical, operational and decision-making purposes. This process is known as data integration, one of the key components of a robust data fabric. End users cannot trust AI results without a proficient data integration strategy to integrate and manage the organization’s data.

Next level data integration

Data integration is essential to modern data fabric architectures, especially since an organization’s data is in hybrid, multi-cloud environments, and in a variety of formats. With data residing in a variety of different locations, data integration tools have evolved to support multiple deployment models. As adoption of cloud and AI grows, fully managed deployments for integrating data from diverse and disparate sources have become popular. For example, IBM Cloud’s fully managed deployments allow users to take a hands-off approach to serverless services and benefit from application efficiencies such as automated maintenance, updates, and installations.

Another deployment option is a self-managed approach, such as a software application deployed on-premises, giving users full control over business-critical data, lowering data privacy, security, and sovereignty risks.

that much remote execution engine This is a fantastic technological development that takes data integration to the next level. It combines the advantages of fully managed and self-managed deployment models to provide ultimate flexibility to end users.

There are several styles of data integration. The two most popular methods, extract, transform, load (ETL) and extract, load, transform (ELT), are both performant and scalable. Data engineers build data pipelines, called data integration tasks or tasks, as incremental steps to perform data operations and coordinate these data pipelines in an overall workflow. ETL/ELT tools typically have two components: design time (Designing Data Integration Tasks) and run-time (To run data integration jobs).

From a distribution perspective, they’ve been packaged together so far. Remote Engine Running is revolutionary in the following ways: Disunite It separates the data plane from the control plane, where data integration tasks are executed, through design time and runtime. The remote engine appears as a container that can run on any container management platform or natively on any cloud container service. The remote execution engine can run data integration jobs for workloads from cloud to cloud, cloud to on-premises, and on-premises to cloud. This allows you to manage your designs in a timely manner as you deploy your engines (runtimes) across all clouds, data centers, and regions, such as customer managed environments and VPCs.

This innovative flexibility keeps data integration operations closest to business data through a customer-managed runtime. This prevents fully managed design time from impacting that data, improving security and performance while maintaining the application efficiency benefits of the fully managed model.

Remote engines allow you to design your ETL/ELT jobs once and run them anywhere. Again, the remote engine’s ability to provide ultimate deployment flexibility combines the following benefits:

• Users reduce data movement by running pipelines with data.
• Users save on sending costs.
• Users minimize network latency.
• As a result, users improve pipeline performance while ensuring data security and control.

There are several business use cases where this technology is beneficial, but let’s look at three:

1. Hybrid cloud data integration

Existing data integration solutions often face latency and scalability issues when integrating data across hybrid cloud environments. Remote Engine allows users to run data pipelines from anywhere, from on-premises and cloud-based data sources, while maintaining high performance. This allows organizations to leverage the scalability and cost-effectiveness of cloud resources while keeping sensitive data on-premises for compliance or security reasons.

use SeedNow Sscript: Consider a financial institution that needs to aggregate customer transaction data across both on-premises databases and cloud-based SaaS applications. The remote runtime allows you to deploy ETL/ELT pipelines within a Virtual Private Cloud (VPC) to process sensitive data from on-premises sources while accessing and integrating data from cloud-based sources. This hybrid approach helps you comply with regulatory requirements while leveraging the scalability and agility of cloud resources.

2. Multicloud data orchestration and cost reduction

Organizations are increasingly adopting multicloud strategies to avoid vendor lock-in and leverage best-of-breed services from multiple cloud providers. However, coordinating data pipelines across multiple clouds can be complex and expensive due to ingress and egress operational expenses (OpEx). The remote runtime engine supports any type of container or Kubernetes, simplifying multicloud data orchestration by allowing users to deploy on any cloud platform with ideal cost flexibility.

Transformation styles such as TETL (Transform, Extract, Transform, Load) and SQL Pushdown also work synergistically with the remote engine runtime to further reduce costs by leveraging source/target resources and limiting data movement. Organizations with a multicloud data strategy must optimize data gravity and data locality. In TETL, transformations are first run within the source database to process much of the data locally before following traditional ETL processes. Similarly, SQL Pushdown for ELT pushes transformations to a target database, allowing data to be extracted, loaded, and then transformed within or near the target database. This approach leverages integration patterns with remote runtime engines and improves pipeline performance and optimization while providing users the flexibility to design pipelines to suit their use cases, thereby minimizing data movement, latency, and egress costs. do.

use SeedNow Sscript: Let’s say a retail company uses a combination of Amazon Web Services (AWS) to host its e-commerce platform and Google Cloud Platform (GCP) to run AI/ML workloads. Remote runtime allows you to deploy ETL/ELT pipelines on both AWS and GCP, enabling seamless data integration and orchestration across multiple clouds. This ensures flexibility and interoperability while leveraging the unique capabilities of each cloud provider.

3. Edge computing data processing

Edge computing is becoming increasingly prevalent, especially in industries such as manufacturing, healthcare, and IoT. However, traditional ETL deployments are often centralized, making it difficult to process data at the edge where it is generated. The remote execution concept unlocks the potential of edge data processing by allowing users to deploy lightweight, containerized ETL/ELT engines directly within edge devices or edge computing environments.

use SeedNow Sscript: Manufacturing companies need to analyze sensor data collected from machines on the factory floor in near real time. The remote engine allows you to deploy the runtime to edge computing devices within the factory. This allows you to preprocess and analyze data locally, reducing latency and bandwidth requirements, while maintaining centralized control and management of your data pipeline in the cloud.

Harness the power of remote engines with DataStage-aaS Anywhere

Remote Engine helps companies take their data integration strategies to the next level by providing ultimate deployment flexibility, allowing users to run data pipelines wherever their data resides. Organizations can unlock the full potential of their data while reducing risk and lowering costs. Adopting this deployment model allows developers to design their data pipelines once and run them anywhere, building a resilient and agile data architecture that drives business growth. Users benefit from a single design canvas, but can switch between different integration patterns (ETL, ELT with SQL pushdown, or TETL) without manual pipeline reconfiguration to best suit their use case.

IBM® data stage®-aaS Anywhere Benefit your customers with a remote engine that allows data engineers of any skill level to run data pipelines in any cloud or on-premises environment. In an era of increasingly siled data and rapid growth in AI technology, it is important to prioritize a secure and accessible data foundation. Start building a trusted data architecture with DataStage-aaS Anywhere, a NextGen solution built by the trusted IBM DataStage team.

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