Enterprise Data Storage: Understanding Your Options and the Trade-offs

No matter what type of enterprise you're leading or managing, every data leader will encounter this issue.

The data stack that began as a clean and efficient system (the data stack) is now cracking due to the pressure placed upon it. The costs to store that data are now rising unpredictably. The performances of the queries against that data are degrading. The amount of duplicate data that is being created by teams doesn't allow them to complete their tasks effectively, or in some cases at all.

At first glance, you may see this as a tooling issue.
But it is not.

The problem lies in the architecture of the enterprise data storage system as it was originally designed and implemented.

This moment is important for vp & of data leaders. The outcome of this decision will determine whether your enterprise scales smoothly or if your enterprise becomes a bottleneck to its analytics and ai initiatives, as well as hinders its ability to grow.

This guide will provide:

• An overview of what enterprise data storage is, and how it has changed
• An overview of the core types of storage systems available
• An overview of the trade-offs associated with enterprise data storage that leaders are expected to understand
• Things that all growing teams are consistently doing wrong
• The process to define the best strategy and implementation of enterprise data storage

The Definition of Enterprise Data Storage

So let's start by answering the question, "What is enterprise data storage?" and in order to do that, we need to define what we mean by "enterprise data storage" as it relates to the systems and infrastructure required to store, manage, and retrieve large-scale amounts of data to support an organization, in a high reliable, high performant, and secure manner.

However, when you think about enterprise data storage, the definition has changed.

You want to develop:

Overview of Cloud Technology's Role in Enterprise Data Storage

The Cloud is the primary technology that most companies use for their Enterprise Data Storage. This is due in part to advantages given by Cloud Platforms, such as:

• Scalability, Elasticity
• On-demand Separation of Compute and Storage
• Built-in Redundancy and Availability

Cloud Technologies Have Changed the Way We Think About Storage Architecture.

Main Components of Enterprise Storage Systems

Current Enterprise Data Storage Systems have four key components:

• Data Lake for storing raw data
• Data Warehouse for structured data (analytics)
• Object-Based Storage to achieve scalability
• Lakehouse to provide an architecture that combines the functionality of both a Data Lake and Data Warehouse

Key Point: The Next Generation of Enterprise Data Storage will primarily be housed within a Distributed Model vs a Centralized Model

What Types of Data Storage Are There – What Are the Available Solutions?

When selecting a solution for your Enterprise Data Storage, you must be familiar with all available features/options for each type/solution available.

A Warehouse is typically used to store Structured Data (Analytics) to support Business Intelligence (BI) Dashboards, Reporting, and SQL-Type Workloads.

A Lake is typically used to store Raw and Semi-Structured Data and provides a real-time option for Machine Training and High-volume Data Ingestion.

A Lake/Digital Warehouse provides a unified approach to Analytics and Machine Learning.

Object Storage (i.e., S3, Azure Blob, etc.) provides a flexible form of storage.

Best Applications

• Storage that scales well.
• Backup and archiving.

Benefits

• Low cost.
• High durability.

Drawbacks

• It needs orchestration layers to operate.
• Data is not directly queryable.

Key Information

• There is no one-size-fits-all solution for an 'ideal' enterprise data storage system.
• Most enterprises have a multi-layer architecture.
• When choosing the ideal architecture, choose multiple storage types available for your use case strategically.

Storage Strategy Trade-offs

Every choice made regarding enterprise storage has certain trade-offs.
Failure to recognize these trade-offs may result in delays and inefficiencies over the long-term.

Cost vs Performance

This is possibly the most significant trade-off.
High-performance systems will generally increase computing costs, while lower-cost systems will compromise on speed and responsiveness.

Example:
Warehouses allow for fast processing but come at a high cost.
Lakes are less expensive to process than a warehouse, but the data must be processed optimally to gain similar performance levels.

Flexibility vs Governance

Lakes provide flexibility.
Warehouses enforce governance structures.

Without proper governance, lakes can become unwieldy.

Scalability vs Complexity

Cloud storage can be easily scaled.
Managing the pipelines and ensuring access to data can be challenging.

Real-time vs Batch Processing

Real-time processing requires low latency.
Batch processing can have a substantially lower cost to process.

Example Scenario

A rapidly growing company builds out its entire infrastructure utilizing a data warehouse.

Initially:

• Fast queries and easy set up.

Eventually:

• Costs will spike
• Machine Learning workloads will not perform well
• There will be increased data duplication

Fixing this will take a significant amount of engineering work and will necessitate changes in the architecture to a hybrid model.

Key Concept: Trade-offs are intentional and should not be made by accident.

Growing Organizations - What They Get Wrong

Most enterprise-level organizations will make the following two mistakes:

Storage is a one time decision

Storage is always evolving as your company grows.
What works for 10TB will not work for 500TB.

Over-Optimizing for cost at the wrong time

Searching for the most affordable option leads to:

• Decreased performance
• Unnecessarily high engineer overheads

Ignoring Data Access Patterns

The following patterns of how to store data – based on both:

• How often it is queried
• How it is being used

are important considerations.

Secondly, not having any integration with analytics and/or AI will cause problems with:

• BI Tools
• ML Pipelines
• Real-Time Systems

Disaster Recovery Strategy

If you don't have a disaster recovery strategy, it is critical to understand:

• How to implement a solid plan for recovering enterprise data from disaster strikes or downtime.

Failure to implement a disaster recovery plan will negatively impact a business by increasing its risk of losing critical data and will incur higher costs for not being operational.

Governance Considerations

If you fail to consider and/or underestimate governance as part of your architecture, you will:

• Create inconsistencies in your data
• Increase the chances of non-compliance with regulations

Most Technical Failures are Due to Poor Architectural Decisions Made Early Without Long Term Considerations

Key Takeaway: If Scale is an Issue for Your Organization, Poorly Designed Storage Solutions Will Not Work.

The Best Way to Choose an Enterprise Data Storage Solution

To find the best storage solution for your organization requires a structured approach.

Define Use Cases

Determine whether or not you are optimizing for analytics only, ML only or both.
Determine whether or not you require real-time capabilities.

Evaluate Data Growth

• What is the current size of your data?
• What is expected growth?

Determine Performance Requirements

• What is your required query latency?
• How much concurrency will you require?

Align with Cloud Strategy

Most organizations will place enterprise data storage into the cloud.

Evaluate Integration Requirements

Is the storage solution compatible with:

• ETL Pipelines
• BI Tools
• ML Frameworks

Compare Storage Providers

Which Provider Has The Best Solution When Selecting an Enterprise Data Storage Provider?
AWS, Google Cloud, and Azure have the leading enterprise data storage solutions based on specific architectural requirements (i.e., each one's strength).

Prepare for Scaling an Improved Data Infrastructure - Cloud Storage for Enterprises

Objective: Design Cloud Storage Solutions with the Future's Complexity in Mind

Cloud Computing has become the "de facto" Storage for Organizations Because it is:

Cloud Storage has become the Storage of Choice for Organizations.

Reasons Cloud Computing provides Advantages over Other Storage Types

• Elastic Scalability
• Less Equipment Management
• Faster Deployment

Cloud Computing Features

• Pay-Per-Use
• Global Accessibility
• Automatic Backups

Cloud Computing Challenges

• Cost Optimization
• Vendor Lock-In
• Data Transfer Delays or Lag

Hybrid Architecture

Organizations Often Distribute Workloads Across Multiple Environments:

• Utilizing On-Premise Facilities for Sensitive Workloads
• Utilizing the Cloud for Scaling Workloads

Example:

• Storing Transaction Data On-Premise for Compliance
• Storing Analytics Data in the Cloud

Provides an Excellent Agency to Balance Control and Achieve Scalability

Trend: Cloud First is the Industry Norm; Nevertheless, the Optimal Hybrid of Both Should be the Strategy

Five Key Characteristics to Consider When Selecting Enterprise Storage Solutions

Selecting an Enterprise Storage Solution requires emphasis on the following Five Key Characteristics:

• Scale
  Ability to Adapt Seamlessly to Increased Data Volume.
• Performance
  Ability to Provide Quick Response time and Maintain High Concurrency.
• Security
  Ability to Encrypt Data and Control Access to Data.
• Integration
  Ability to Manage the Entire Data Ecosystem to Maximize Value from the Data.
• Cost Efficiency
  Ability to Attain the Optimal Amount of Storage and Compute Resources.

Trends in the Enterprise Data Storage Marketplace

Organization will see changes to the Ecosystem in Which their Enterprise Storage Resides:

New Trends Emerging

• Lake House Utilization
• Artificial Intelligence Tools to Optimize how Data is Utilized
• Real-Time Analytics
• Complete Property Data Ecosystems

Strategic Implications of these Trends

The Current Evolution of the Enterprise Data Storage has Demonstrated to Date that Storage has become an Increasingly Automated and Intelligent as Well as an Integrated Component of an Organization’s Operation.

What Does This Mean to Responsible Users of Data?

Enterprise Data Storage has become a mechanism used by Teams that can Modernize their Enterprise Data Storage and Hard Goods Purchasing Decisions before the Competition.

• Reduced Financial Impact of a Storage Failure
• Improved Overall Operations
• Easier Implementation of AI Initiatives

Key Insight: Enterprise Data Storage Provides Organizations with a Leg-Up on the Competition.

Frequently Asked Questions

Conclusion

Storage is a Strategic Lever; Organizations Must Think About the Organizations Future Storage Needs When Selecting an Enterprise Data Storage System

Organizations Should Consider the Enterprise Data Storage System as More than Just an IT Infrastructure Component, but as a Decision That Has Major Impact on an Organization's:

Organization's Performance

Financial Cost Impact

Scalability

Ability to Accept New Innovations

An Organization’s Enterprise Data Storage System Selection Process Has a Major Effect on the Individual’s Ability to Achieve an Enterprise Data Storage "Platform" as Opposed to Creating a Data Storage "Bottleneck."

Logiciel’s Perspective

Logiciel’s Solutions Co, is Supporting Responsible Data Leaders Designing Intelligence Recommended Data Platforms to Optimize for Long-Term Storage with a Significant Focus on Quality of Performance and Scalability for Data to Address Enterprise Storage Needs.

We Create and Implement Modern Infrastructure Solutions for Enterprise Data Storage Connection, Integrated with All Analytics Systems, Machine Learning Systems and Cloud Ecosystems to be Scalable, Growing with Your Data.

If You Believe That Your Enterprise Data Storage Solution Is Beginning to Impede Your Data Capabilities, it is Time to Reevaluate The Architecture of Your Current Data Storage Process.

Contact Logiciel and Discuss Creating Intelligent Enterprise Data Storage Adoptions for You to Meet Your Growth Objectives.