Hybrid Multi-cloud Data Lake Strategy

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October 5, 2023 by Updated April 16th, 2024

Today, organizations are either building new cloud-native data warehouses or data lakes or modernizing their existing on-prem systems in the cloud to accelerate their digital transformation journey. However, just moving the data to the cloud doesn’t solve their challenges. Data management is still a huge blocker for enterprises to get maximum ROI from their cloud-native data warehouse and data lake investments.

Read this blog to maximize your cloud ROI, with a hybrid and multi-cloud data lake strategy – providing information on:

  • Exploring the need for an AI data management setup in both hybrid and multi-cloud lakes.
  • Highlights the difference between hybrid and multi-cloud setups.
  • Introduces the concept of Data Fabric architecture, for cross-cloud data management.
  • Shows you how to get better value and lower TCO.

As data continues to grow and business requirements continue to rise, organizations need to move away from their traditional handcoding and siloed data management approach and embrace an intelligent AI-powered cloud-native data management solution that can ingest, catalog, integrate, apply data quality rules, and prepare the data in a governed manner.

In today’s times, organizations are looking for a modern cloud-native architecture that can enable a long-term strategy for maximizing their data assets based on a multi-cloud platform and give them maximum ROI from their cloud data warehouse and data lake investments.

Cloud Native Data Lake

Many organizations are in the midst of a massive data transformation. They are competing in the market to out-innovate their rivals with a cloud-native approach that enables them to build modern applications.

The cloud-native approach offers the following benefits:

  • Faster time to market: Provides a flexible architecture that empowers companies to respond to market conditions quickly. 
  • Deliver better products: Enables organizations to deliver more features faster to their customers for competitive advantage. 
  • Flexibility to adopt best practices: A cloud-native architecture is self-healing, cost-efficient, and easily updated and maintained through continuous integration/continuous delivery (CI/CD). With the cloud, there are no limitations on the amount of data that can be processed. You can scale up resources when you need to match demand. This ensures that anyone who needs data processing can do so and only pay for the compute they use.
  • Increased ROI:  Faster first-time value by ensuring timely completion of the data warehouse and/or lake migration to the cloud.
  • Increase productivity: With an integrated and comprehensive data management solution that delivers intelligence and automation, resulting in cost savings.
  • Minimize risks: Helps in avoiding the challenges that come with using hand-coding and multiple-point solutions to address data management issues.
  • Gain cloud scale and agility:  With the rapid deployment of jobs, faster DevOps, DataOps, and MLOps, automatic upgrades, and fast data onboarding, cloud-native offers an integrated solution for high availability and advanced security.
  • Improve visibility: Connect and scan metadata for all types of databases, SaaS apps, on-premises apps, on-premises data warehouses, ETL tools, BI tools, and more to provide complete and detailed data lineage.
  • High-performance data integration: Successfully deploy new data warehouses and data lakes in the cloud that connect to all data and seamlessly integrate high volumes of data for any analytics workload.
  • Elasticity: Provision or de-provision resources to meet real-time demand. You can change the capacity and power of machines on the fly, leading to greater agility and flexibility.
  • Self-Service and Collaboration: Everything is API-driven. Users can choose the resources they need without requiring that someone else provision there for them.

Data Lake Cloud Migration

When moving to the cloud, organizations can take a few different approaches which can be broadly categorized as:

  • Lift and Shift: In this approach, the entire on-premise software stack is replicated on the cloud to take advantage of the shift from CapEx to OpEx. This approach is a great way to get started and experiment on the cloud without a very significant upfront investment. However, it does not take advantage of cloud features such as separation of compute and storage, autoscaling, and other cost optimizations.
  • Lift and Reshape: As organizations mature, they will be able to take a workload-driven approach to take advantage of the cloud’s elasticity. However, technologies and tools in the big data space are continuously evolving, and it becomes very cumbersome to support all users and multiple use cases as new users are onboarded.
  • Autonomous Cloud Data Platform: This approach builds on top of the lift and reshapes by adding advanced features explicitly constructed to optimize costs and cloud computing for big data operations. Using a combination of heuristics and machine learning, big data cloud automation ensures workload continuity, the best performance, and significant cost savings. Automation of lower-level tasks makes engineering teams less reactive and more focused on improving business outcomes.

Several successful companies have leveraged Qubole’s cloud-native data platform to transition to the cloud and successfully use big data to improve business outcomes.

Hybrid Multi-cloud Data Lake Differences

Although the two terms `Hybrid Cloud` and `Multi-cloud` can be safely interchanged, a major distinction can be said as :

  • Hybrid clouds always include a private cloud and are typically managed as one entity
  • Multi-clouds always include more than one public cloud service, which often performs different functions. Multi-clouds do not have to include a private cloud component, but they can, in which case they can be both multi-cloud and hybrid cloud.

Hybrid Multi-cloud Infrastructure

To understand which infrastructure is best suited for your organization correctly, organizations need to factor in the benefits that both models offer. 

Here are some benefits that each model avails –

Hybrid Cloud

  • Reliable Access: It gives access to the data to be able to support the remote workforce better. Organizations have the flexibility to support remote employees with on-demand access to data that is not tied to one central location.
  • Reduced costs: Organizations can pay only for the specific cloud resources they use.
  • Improved scalability and control: Increased automation allows for adjusting cloud settings to respond automatically to changes in demand, performance optimization, and efficiency.
  • Security and risk management:  It gives organizations control over their data and helps improve security by reducing the potential exposure of data to threats. 


  • Risk Reduction: In situations where one cloud provider has a temporary service failure, we can switch to another vendor, thus handling the failure case.
  • Finding the best-in-class multi-cloud providers at quite a competitive pricing: Compare the offerings and policies of different service providers and then select the best offer that suits your organization the most. This way, we can achieve a very high level of agility.
  • Automation & Scalability: A multi-cloud strategy helps organizations to be able to coordinate varying workloads and manage hybrid workflows concurrently.
  • Robust Security: Each cloud provider is responsible for the security of their infrastructures, so we are ensured that they have all the capabilities for protecting the data.

Benefits of Data Fabric Architecture in a Hybrid Multi-Cloud World

Data Fabric architecture has emerged as the solution to hosting a robust hybrid multi-cloud world that enables organizations to centrally monitor, manage, orchestrate, and govern data across multiple clouds, on-premise, data lakes, or data warehouses. Data Fabric facilitates improved data discovery, cataloging, integration, and sharing of data across hybrid multi-cloud environments.

It acts as an intelligent connecting tissue that joins together disparate data repositories, data pipelines, and data-consuming applications through a separate neutral layer that every cloud in an enterprise can interact with.

 The key benefits of data fabric in hybrid multi-cloud include:

  • Enabling common data services across environments from distributed on-premise, hybrid, and multi-cloud.
  • Delivering high-quality data consistently in the right form for a wide range of analytical, operational, transactional, governance, and self-service use cases.
  • Leveraging the value of data by assessing, combining, and transforming both in-motion and at-rest data from diverse data landscapes using metadata, data models, and pipelines.
  • Delivering flexibility, scalability, and data optimization while moving databases from one cloud to another.
  • Robust data control and a 360-degree view of data to realize the power of hybrid multi-cloud.

Why Organizations choose Qubole

According to a Statista survey, the global big data market is projected to reach USD 103 billion by 2027, more than double its projected demand in 2018. For all the promise big data holds, companies must stay focused and continue digital transformations by adopting a  cloud-agnostic platform to improve the way they work with data, so they do not fall behind. Here’s why organizations must choose to meet these business objectives: 

  1. 3x faster time to value: Qubole delivers faster innovation with self-service access to big data, enabling use cases that can be deployed in days — not weeks or months.
  2. Single platform: Our platform offers a shared infrastructure for all users with the ability to leverage multiple best-of-breed engines. Qubole is massively scalable on any cloud, thereby preventing vendor lock-in.
  3. 10x more users and data per administrator: Qubole’s self-service platform enables administrators to input policy controls and controls user/group access privileges. The platform’s automation capabilities ensure that all users and workloads are provisioned.
  4. 50% lower TCO: Several cost optimization features allow users to leverage lower and cheaper compute.
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