Your data team feels confident that they are "doing a good job." However, stakeholders have concerns around how well you’re measuring against these metrics.
- Currently, there are discrepancies between the model used in the existing system and the model in use outside of the existing system
- The amount of time it takes for a data service in an existing system to be delivered does not reflect the time it would take for that same service to be delivered in the hybrid system.
- Each additional integration added to an existing integration will create the potential for errors.
As a result, this hidden cost has significant implications on the effectiveness of hybrid cloud infrastructures.
Investor-Ready Infrastructure in 90 Days
Inside a 90-day sprint that took a flagged round to a $28M close.
As VP or Head of a Data Team, this guide will demonstrate the impact of complexity on the management of hybrid cloud data infrastructures and how to develop strategies to effectively manage your data regardless of whether it is located on-premises or stored in the cloud, as well as provide guidance for designing a system that provides greater reliability, observability, and performance.
Hybrid computing is a given; the question today is; how will your organization implement hybrid architecture as the starting point?
Reasons Data Teams Fail to Implement Successful Hybrid Cloud Infrastructure
Most organizations do not set out to create a hybrid infrastructure; they simply evolve into having one through their operations:
Failure Patterns:
- Build On-Premise Databases First
- Build Cloud-Based Databases Next.
- Add Additional Integrations on Top of These Existing Integrations.
Failure Result -
- Unconnected Data Pipelines
- Unpredictable Operational Performance
- Increased Operational Support.
Why It Will be Harder to do than it is Now by 2026
The Current Hybrid Cloud Data Infrastructure (HCDI) contains 3 Main Attributes:
- Multiple Cloud Platforms
- Multiple Legacy Platforms. (on-premises)
- Both Real & Batch workloads.
When a New Feature is added to the HCDI, it introduces New Latency Challenges, New Security Issues, and New Data Movement Requirements.
A Common Example of Difficult-to-Manage Data
An example of a company that stores financial data and is subject to compliance requirements would be a
- Delays in data synchronization
- Repetitive burdens caused by debugging
What does success look like?
A hybrid environment that is a mature hybrid environment.
Key requirements for a mature hybrid environment:
- The ability to seamlessly move data between environments
- The ability to consistently maintain data quality
- The ability to provide unified visibility
Key insight
Unified architecture/gov does not allow hybrid systems to fail because the technology is bad
Prerequisites: What you need to have in place before starting.
Before you work to optimize hybrid cloud solutions ensures you have the fundamentals.
1. Clear Ownership Model
Define:
- Who owns on-prem systems
- Who owns cloud pipelines
- Who owns integrations
Without clarity your:
- Issues will fall through the cracks
2. Baseline Infrastructure
At this point, you should have:
- Existing functional data pipelines
- Existing central storage
- Existing orchestration systems
3. Data Contracts
Ensure:
- Schemas remain consistent across environments
- Managed schema changes
This will reduce:
- Integration failure
- Data inconsistency
4. Stakeholder Alignment
Hybrid systems will impact:
- Engineering
- Security
- Compliance
- Business teams
Therefore, you all need to agree on:
- Data accessibility
- Performance expectations
- Risk acceptance
5. Defined Success Metrics
Track:
- Data latency
- Data pipeline reliability
- Data consistency
Key Insight
Hybrid systems are amplifying existing problems. Identify and address the fundamentals before continuing through Phase 1
Phase 1: Assess Your Current State
Before designing the new version of your hybrid cloud environment, it is important to evaluate your current system.
Step 1: Audit Your Infrastructure
Document:
- On-premise systems
- Cloud platforms
- Data pipelines
- Integration points
In addition to auditing, you should collect:
- Ownership
- Service Level Agreements
- Dependency relationships
Step 2: Identify Key Gaps
The majority of teams experience the following:
- Blockages of data movement
Slow and/or unreliable data transfer
- dditionalVisibility Gaps
There should be one source for all of the systems you reside in.
- Inconsistent Data Models
Different schemas across your environments.
Step 3: Mapping Data Flows
Create a simple flow that includes;
On-Premise → Cloud → Analytics.
By doing this;
You can identify;
- Latency Issues
- Dependencies
Step 4: Prioritizing Improvement Initiatives
You will want to divide improvement initiatives into two categories;
Quick Win Initiatives: Monitoring Enhancements, Synchronization Improvements.
Strategic Initiatives: Re-Design the Architecture.
Your Output Will Be:
A Clear and Accessible;
- Immediate Fix Roadmap.
- Long-Term Change Roadmap.
Key Insight
Understanding your system is the first step towards being able to control it.
Phase Two: Hybrid Architecture Design
At this point, you will have a clear description of what your hybrid architecture is to be designed to do.
1. Defining Design Principles for Your Cloud Based Infrastructure.
You should have a;
- Unified Infrastructure
- Observable Infrastructure
- Scalable Infrastructure
- Secure Infrastructure
2. You Will Also Want to Deliberately Evaluate Your Components.
What tools you will use to move data?
What tools you will use to store data?
What tools you will use to process data?
You should also Avoid:
- Tool Sprawl
- Redundant Tools
3. Build Observability into your Design as a First Class Citizen.
You will build in Observability that Includes
- Cross Environment Monitoring
- Data Freshness
- Error Detection
Teams That Have Good Observability Have Over 50% Less Time Resolving Incidents.
4. Establish a Plan for How Data Will Move.
You should identify the Characteristics of Data Movement you will establish including;
- Batch and Real-Time Transfers between Environments
- Data Replication Strategies from Source to Destination or Between Environments.
- Optimizing Latency/Batch Times.
5. Create and Document your Design Assumptions.
You will want to document your initial assumptions regarding;
- How you will make Architectural Decisions
- Trade-offs made in the Design
- Constraints on your Architecture
You should also set and identify a time frame to review your Assumptions
Key Insight
Hybrid Architectures Must Be Designed with Intent, Not by Accident!
Phase Three: Develop, Test and Deploy Incrementally.
You Should Avoid Large Scale Migrations.
1. Start your Testing and Development Process with One Domain.
Choose;
- Analytic Data
- Customer Data
Then Test Your Approach!
2. Establish a Parallel Development Environment.
To Maintain Your Existing ETL Pipelines.
And To Create and Test Your New Hybrid Pipelines.
3. Create and Automate your Testing Procedures.
Your Test Procedures Should Include;
- Data Validation
- Schema Validation
All Aspects Must Be Measured
Measurable metrics include:
- Latency Rates
- Rate of Errors
- Consistency of Data in all Domains
Scale-Up at an Appropriate Tempo
Once it has been validated:
- Expand to more than one domain
- Establish/Standardize processes to be the same as other domains
Important information
When using incremental rollout, it decreases overall risk when there is a complex hybrid-type system.
Successful Measurement of Success-Finalization and Iteration
It is crucial to have continuous monitoring in hybrid systems.
1. Identify SLO's (Service Level Objectives).
Examples of SLO's might include:
- Thresholds/jerks of data delivery
- Up-time of our pipelines
- The metric of consistency in data
2. Create and implement a single dashboard.
Components for inclusion:
- The health of the pipeline across different environments
- Timeliness of data linked to the pipeline
- Data being identified with the incident report
Regular reviews
- Both causes of failure
- Evaluation/bottleneck areas
- Identifying locations for improvement.
3. What Impact it Has on the Business
Results can be measured:
- Through an accelerated decision-making process
- With increased downtime
- Can increased or decreased summary reports for the business positive?
4. Use Intelligent Platforms
Logiciel offers a unified and cross-referenced view of all:
- Automation
- Automated pipelines
- Integrated View of Everything
Important information
The measurement will help to ensure that hybrid systems are in alignment with the ongoing requirements of the business.
In Summary
Using hybrid-cloud based solutions is not a passing trend, but rather a long-term solution.
Three Key Elements
- The hybrid and complex nature of hybrid systems requires that the design be performed with intent.
- Governance and observability are a must.
- An incremental implementation of hybrid systems is recommended as it reduces risk.
Building hybrid systems is arduous; however, when performed correctly will allow an organization to experience:
- Full and seamless data flow
- Reliability
- Scalable Infrastructure
Board Approval for Infrastructure Modernization
Inside a financial-frame business case that turned a 14-month stall into a 45-minute board approval.
Call to Action
If you manage hybrid systems, before doing anything else, consider completing the following:
Check out both of the sites listed below.
- Cloud Infrastructure Management: How to Stop Blinded by the Flying Elephant in the Room
- 4 Reasons Why Your Data Infrastructure Continuously Fails - The Root Causes And Solutions To Resolve It
If after completing the above two items you are still interested in moving forward:
👉 Read more on how to request an audit of your hybrid systems
Logiciel Solutions helps AI-first, hybrid data systems deliver consistently, rapidly, and with reliable insight through a seamless integration of all data sources within a single environment.
Frequently Asked Questions
What does hybrid cloud-managed data infrastructure mean?
Hybrid cloud-maintained data infrastructure is the combining of on-premises and cloud-based systems that manage and store data across multiple locations and/or different types of platforms.
Why do companies choose to use hybrid infrastructure?
Many companies select to use hybrid infrastructure for compliance, cost savings, and/or versatility in terms of capability and/or location.
What are the key challenges to be aware of in the hybrid space?
Data movement Latency Visibility Security
How can teams be successful in managing hybrid systems and data?
Hybrid systems must establish visibility, create common contracts, and establish a common architectural format.
Is hybrid better than full-scale cloud?
It is dependent upon the requirements to be met. Hybrid provides greater versatility; however, hybrid systems tend to be more complex as a whole than full-scale cloud systems.
