Business leaders risk losing their competitive edge if they don’t proactively embrace generative AI (Gen AI).But companies expanding AI face barriers to entry. Organizations need reliable data for robust AI models and accurate insights, but today's technology environment presents unparalleled data quality challenges.
According to the International Data Corporation (IDC), stored data is expected to increase by 250% by 2025, and data will rapidly propagate with reduced quality across on-premises, cloud, applications, and locations. This situation exacerbates data silos, increases costs, and complicates the governance of AI and data workloads.
The explosive growth in the amount of data in various formats and locations and the pressure to scale AI pose a daunting challenge for those responsible for implementing AI. Data from multiple sources must be combined and reconciled into a unified, consistent format before being used in AI models. Integrated and managed data can also be used for a variety of analytical, operational, and decision-making purposes. This process is known as data integration and is one of the key components of a strong data fabric. End users cannot trust AI output without a skilled data integration strategy to integrate and manage an organization's data.
Next level data integration
Data integration is essential to modern data fabric architectures, especially as organizations' data resides in hybrid, multicloud environments, and in multiple formats. Because data resides in many different locations, data integration tools have evolved to support multiple deployment models. As cloud and AI adoption increases, fully managed deployments to integrate data from a variety of disparate sources have become commonplace. For example, fully managed deployments on IBM Cloud allow users to take a manual approach with serverless services and benefit from application efficiencies such as automated maintenance, updates, and installations.
Another deployment option is a self-managed approach, such as software applications deployed on-premises. This gives users complete control over their business-critical data, reducing data privacy, security, and sovereignty risks.
of remote execution engine This is a great technology development that takes data integration to the next level. It combines the best of fully managed and self-managed deployment models to provide maximum flexibility to your end users.
There are several styles of data integration. The two most common methods, extract, transform, and load (ETL) and extract, load, transform (ELT), are both performant and scalable. Data engineers build data pipelines called data integration tasks, or jobs, as incremental steps to perform data operations and orchestrate these data pipelines throughout the workflow. ETL/ELT tools typically have two components: design time (to design data integration jobs) and runtime (to run data integration jobs).
From a deployment perspective, these have traditionally been packaged together. Remote engine execution is revolutionary in the following ways: separate Distinguish between design time and runtime, and separate the control plane and data plane where data integration jobs run. Remote engines appear as containers that can run natively on any container management platform or on any cloud container service. The remote execution engine can run data integration jobs for cloud-to-cloud, cloud-to-on-premises, and on-premises-to-cloud workloads. This gives you timely control over your designs when deploying engines (runtimes) on clouds such as customer-managed environments, VPCs, data centers, geographies, and more.
This revolutionary flexibility brings your data integration jobs closest to your business data using customer-managed runtimes. This means you never touch that data during a fully managed design, improving security and performance while retaining the application efficiency benefits of a fully managed model.
With remote engines, you can design your ETL/ELT jobs once and run them anywhere. Again, the remote engine's ability to provide ultimate deployment flexibility has multiple benefits:
- Users reduce data movement by running pipelines where the data resides.
- Users reduce downstream costs.
- Users minimize network delays.
- As a result, users can improve pipeline performance while ensuring data security and control.
There are several business use cases where this technology can be advantageous, but let's explore three:
1. Hybrid cloud data integration
Traditional data integration solutions often face latency and scalability challenges when integrating data across hybrid cloud environments. Remote Engine allows users to pull data from on-premises and cloud-based data sources and run data pipelines anywhere while maintaining high performance. This allows organizations to leverage the scalability and cost efficiency of cloud resources while keeping sensitive data on-premises for compliance and security reasons.
use cheat rash sCenario: Consider a financial institution that needs to aggregate customer transaction data from both on-premises databases and cloud-based SaaS applications. Remote runtimes allow 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 ensure compliance with regulatory requirements while leveraging the scalability and agility of cloud resources.
2. Multicloud data orchestration and cost savings
To avoid vendor lock-in and use best-in-class services from different cloud providers, organizations are increasingly adopting multi-cloud strategies. However, orchestrating data pipelines across multiple clouds can be complex and expensive due to inbound and outbound operational costs (OpEx). The remote runtime engine supports any type of container or his Kubernetes, allowing users to deploy on any cloud platform, providing ideal cost flexibility and simplifying multi-cloud data orchestration. Masu.
Transformation styles such as TETL (transform, extract, transform, load) and SQL pushdown also synergize with remote engine runtimes to leverage source/target resources to limit data movement and further reduce costs. A multicloud data strategy requires organizations to optimize data importance and data locality. With TETL, transformations are first performed in the source database to process as much data as possible locally, before following traditional ETL processes. Similarly, ELT's SQL pushdown pushes transformations to the target database, allowing data to be extracted, loaded, and transformed in or near the target database. These approaches minimize data movement, latency, and egress charges by leveraging remote runtime engines and integration patterns to enhance pipeline performance and optimization while allowing users to tailor pipelines to suit their use cases. Allows for flexible line design.
use cheat rash sCenario: A retail company uses a combination of Amazon Web Services (AWS) to host its e-commerce platform and Google Cloud Platform (GCP) to run its AI/ML workloads. Remote runtimes allow you to deploy your ETL/ELT pipelines to both AWS and GCP, enabling seamless data integration and orchestration across multiple clouds. This ensures flexibility and interoperability while using each cloud provider's unique features.
3. Edge computing data processing
Edge computing is becoming increasingly popular, 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 their lightweight, containerized ETL/ELT engines directly on edge devices or within edge computing environments.
use cheat rash sCenario: 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 runtimes to edge computing devices within your factory premises. This reduces latency and bandwidth requirements by allowing you to preprocess and analyze data locally while maintaining central control and management of your data pipeline from the cloud.
Unleash the power of your remote engine with DataStage-aaS Anywhere
Remote Engine helps enterprises take their data integration strategy to the next level by providing ultimate deployment flexibility and allowing users to run data pipelines no matter where their data resides. . Organizations can leverage the full potential of their data while mitigating risk and reducing costs. This deployment model allows developers to design data pipelines once and run them anywhere, creating resilient and agile data architectures that drive business growth. Users can benefit from a single design canvas while using a variety of integration patterns (ETL, his ELT with SQL pushdown) to best suit their use case without having to manually reconfigure their pipelines. , or TETL).
IBM® data stage®-aaS Anywhere Remote Engine enables data engineers of any skill level to run data pipelines within a cloud or on-premises environment, benefiting customers. In an era of increasing data silos and rapidly growing AI technologies, it's important to prioritize a secure and accessible data foundation. Get an early start on building a reliable data architecture with DataStage-aaS Anywhere, a NextGen solution built by the trusted IBM DataStage team.
Learn more about DataStage-aas Anywhere Try IBM DataStage as a Service for free
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