Reimagining Data Integration for a Hybrid, Multi-Cloud World

Last Published: Apr 23, 2024 |
Sudipta Datta
Sudipta Datta

Product Marketing Manager

As your organization grows and modernizes, your business data will inevitably scale and become more complex. You will find that hybrid, multi-cloud environments (with data workloads distributed across multiple public and or private cloud service providers), potentially coupled with on-premises data centers offer a way to address these increasingly complex data management needs. 

In a recent survey, 89% of respondents reported having a multi-cloud strategy1.  This is not surprising, given the ability of hybrid and multi-cloud data infrastructures to enable faster, more flexible and more secure data operations at scale while also optimizing costs and resources. 

However, your organization still needs an integrated view of the data spread across hybrid, multi-cloud environments, as it may still be siloed. To give your users access to credible, business-ready insights, your data needs to be connected, transformed and flow smoothly to various upstream and downstream applications. That’s where efficient data integration can make all the difference. 

Data integration helps you move, manage and use data efficiently across multiple cloud and on-premises systems. However, it is more complex than just pulling data from various systems into your analytics engine and generating insights. 

Data integration must be reimagined and adapted to work in a hybrid, multi-cloud data landscape. Seamless data interoperability and workload portability in this context require you to carefully consider factors that could pose a risk to your data management performance, security and cost efficiencies. 

3 Data Integration Challenges You May Face in Hybrid, Multi-Cloud Environments

Challenge #1: Flexible and Versatile Data Integration
 

The data management ecosystem is designed based on a combination of factors, including the need for security, centralization and control; performance and latency demands; costs; and scale. These factors keep evolving and may vary for different functions within the same organization. Eventually, it turns into a complex hybrid, multi-cloud ecosystem made up of multiple service providers.

The first challenge with data integration in this context is a lack of standardization in data formats across different cloud service providers. This could lead to compatibility issues in data exchange and portability.  

Heterogeneous data formats, such as structured, semi-structured and unstructured, could require additional data transformation steps before undergoing analysis for business insights. 

Finally, different cloud service providers could mandate the use of their proprietary APIs and pipelines for data transfer, leading to further restrictions and compatibility issues. To keep things ticking at the speed of business, data engineers often make quick fixes, resulting in a complex patchwork of workarounds and pipelines to overcome these speed bumps.

This ad-hoc maze can be difficult to unravel and end up causing unseen complications over time, stemming from incorrectly identifying dependencies or overlooking an integration that impacts a critical downstream business process.
 

How to Address the Challenge
 

While the multi-cloud reality is inevitable, data integration complications don’t have to be. If your goal is to manage complexity in a flexible and adaptable manner without impacting efficiency and performance, consider a single, cloud-native, cloud-agnostic data integration solution

Such a solution offers an easier way to run complex integrations across a multi-cloud data environment. You will have enhanced flexibility to onboard diverse use cases and develop innovative approaches, thanks to a single unified platform that can:

  • Handle end-to-end data integration capabilities — such as ingestion, cleansing, transformation and loading — efficiently and at scale.
  • Automate pipelines to streamline data movement and seamlessly link various combinations of on-cloud and on-premises data sources and targets with thousands of universal and reusable pre-built connectors.
  • Leverage common design patterns to support different integration patterns (serverless, event-driven, batch, extract, transform, load (ETL), extract, load, transform (ELT), streaming, API, etc.).
  • Align with next-new tech easily and without friction or risk to business-as-usual data ops.
     

Challenge #2: Cost and Resource Management
 

While a core purpose of a multi-cloud or hybrid strategy is to optimize the cost of data storage and management, this does not automatically address the issue of data transfer, which can be cost, time and resource-intensive. 

If you’re not careful, you may find your cloud migration and modernization initiatives running over budget because they do not take a holistic, enterprise-wide approach to data migration. This can lead to cloud sprawl, which increases data integration complexity and costs, as you end up paying for idle or forgotten workloads.

But that is just the beginning. With data integration, there are costs involved anytime you call data via APIs to the cloud. You need to consider the cost of fees, time and skilled resources for the complex movement of data between clouds or from your on-premises data center to the cloud. It could get even more complicated and expensive if your data workloads are on geographically dispersed clouds.
 

How to Address the Challenge
 

Suppose you aim to build a scalable cloud-native data platform to support modern self-service analytics. In that case, an intelligent, cloud-native data integration solution can deliver significant cost and resource savings with:

  • Multi-tenant capabilities that allow multiple business units to share a common license.
  • Multi-cloud support frees you from vendor lock-in, enables seamless data sharing and eliminates integration costs incurred with multiple point solutions.
  • Flexible, consumption-based pricing and the ability to control compute and data transfer costs. 
  • Capabilities such as rapid access to new features and APIs and easy AI-assisted setup to facilitate self-serve and further streamline data integration costs.
  • Automated decision-making about best-fit workflows; best-fit cases for pro, low and no code; and code reuse (based on usage patterns, user roles and business priorities) to reduce skill dependency and dynamically balance workloads for cost and resource optimization.
     

Challenge #3: Effective Data Governance
 

You may also modernize your data infrastructure for better data governance and security. However, when it comes to data integration, your data still needs to flow across these distinct clouds, potentially exposing it to governance and security risks. 

The same data transfer processes that seem relatively uncomplicated within a single cloud can quickly become complicated in a hybrid or on-premises environment. Just applying patches is not enough. Even if only a tiny percentage of your data workloads reside on-premises, you still need comprehensive data governance, quality and security measures. 

Collecting and storing data across multiple clouds and data centers without a data discovery framework can quickly lead to a lack of clarity on data lineage and escalate data governance issues. In effect, you would not know what data you have, where it is stored, how to access and connect it quickly, or what sensitive data may be vulnerable to unauthorized access across the many systems and users who keep tapping into it.
 

How to Address It
 

Suppose you aim to make data portability easier and more scalable while trying new tools without breaking pipelines and disrupting workflows. In that case, you need an AI-powered data integration tool that:

  • Automates processes such as data discovery, metadata inventory, and the creation of an intelligent data catalog without impacting speed and accuracy. That way, users can spend less time curating or searching for the data they need.

- Automates documentation and standardizes best practices that can be reused for similar use cases across the organization. AI-powered data governance tools include capabilities such as data profiling, data quality and data cataloging to deliver the most trusted business data to users.

 

Choose a Data Integration Solution Made for the Multi-Cloud World

You can address data integration challenges in hybrid, multi-cloud environments with a platform-agnostic, cloud-native data integration solution that can provide a unified interface for data access, transfer and integration. 

The Informatica Intelligent Data Management Cloud (IDMC) offers a future-proof solution to help you realize the promise of the cloud and democratize data in your organization. Cloud data integration with IDMC is: 

  • Comprehensive: manage various data types, patterns, complexities or workloads across multiple locations – with just one cloud integration platform.
  • Intelligent: automate thousands of manual tasks and augment human activity, thanks to AI-powered insights and recommendations, saving costs and empowering business users.
  • High-performing: effortlessly scale your enterprise workloads with elastic and serverless processing.

Start at your own pace with CDI-Free or PayGo and upgrade to the full enterprise solution when ready. Learn more and get started by visiting informatica.com/free-paygo.

1. Flexera, State of Cloud Report, 2024.

First Published: Apr 17, 2024