How to Choose the Best Enterprise Data Warehouse

The top SaaS companies use enterprise data warehouses (EDW), which are centralized data repositories that integrate data from multiple sources across an organization. EDWs empower businesses to analyze large volumes of data for strategic insights and decision-making.

We’ve compiled insights on the key considerations for finding the best strategy for your business.

What You Need to Know to Maximize Value

Enterprise data warehouses are still the most popular data integration platforms for businesses despite the high lifecycle management costs and complexity, with 54 percent utilizing EDWs in 2022.

Choosing the right enterprise data warehouse platform is crucial for maximum value. The wrong choice can saddle your business with spiraling costs, security risks, integration issues, and limited scalability.

How to Determine Your Data Requirements

Before starting your search for an EDW, it's vital to understand your specific needs and requirements.

Data Volume and Variety

• What is the size of your dataset?

• How fast is it growing?

• What types of structured, semi-structured, and unstructured data do you store?

Data Velocity

• How frequently is new data generated?

• What are your real-time vs. batch processing needs?

User Base and Query Types

• How many business users will access the system?

• What types of queries, reports, and analytics will they run?

Security and Compliance Needs

• What regulations or data governance policies must the EDW comply with?

Integration with Existing Systems

• Which source systems and BI tools does the EDW integrate with?

Budget Constraints

• What is your allocated budget for hardware, software, services, and ongoing management?

Clearly defining these requirements will guide your EDW selection and reduce misalignments down the road.

Cloud vs. On-Premises Deployment: What’s the Difference?

Choosing the right deployment model is critical when selecting an enterprise data warehouse. Both cloud and on-premise models have their advantages and disadvantages.

Cloud-Based Enterprise Data Warehouses

In a cloud model, the EDW software, hardware resources, and data files are housed within the vendor's cloud infrastructure, allowing online, remote access. AWS Redshift, Snowflake, and Azure Synapse Analytics are all popular models.

The top benefits of cloud-based enterprise data warehouses include:

Agility and Innovation

Innovate faster with quicker access to bleeding-edge analytics capabilities since the cloud provider continuously enhances services.

Limitless Scalability

Scale independently to support spikes in data volumes and user requests. This is useful in dynamic or seasonal use cases.

Availability

Cloud providers architect redundancy across multiple fault domains for resilience. Achieve over 99.95% uptime via auto-failover.

Pay-Per-Use Pricing

Only pay for the storage, IOPS, computing resources, and services leveraged to reduce wasted overprovisioning and overall TCO.

Faster Time-to-Value

It’s operational in days/weeks since it doesn’t require a lengthy hardware setup. Shift focus to deriving insights instead of infrastructure management.

The disadvantages of cloud-based enterprise data warehouses:

Data Sovereignty

Your data moves outside the organization to align with the provider's security posture as per regulations.

Vendor Lock-In

Although mechanisms exist to migrate data across cloud platforms, they still pose challenges, especially at scale.

Network Dependencies

Your connectivity with the cloud provider impacts data loading speeds and query performance.

Cloud Computing Skills

Retraining staff may be required to leverage cloud management tools, architectures, and services.

On-Premises Enterprise Data Warehouses

In an on-premises model, the enterprise data warehouse runs on infrastructure located within your physical data centers.

Top benefits of on-premises enterprise data warehouses:

Enhanced Security

Maintain full physical control over data access and protection via private infrastructure.

Customization

Since you own the environment, you can tailor EDW deployments and settings as per your specific policies and tech stack standards.

Constant Access

Eliminate internet connectivity issues that disrupt cloud performance. LAN speeds enable faster query responses.

The disadvantages of on-premises enterprise data warehouses:

Substantial Upfront Costs

Large CAPEX outlays are required for data center build-outs, servers, and storage before deriving value.

Administrative Overheads

Your team handles provisioning, patching, monitoring, backups, and upgrades.

Scalability Constraints

Adding capacity requires extensive data migration planning and downtime. It’s hard to match cloud elasticity.

Consider the pros and cons of each model carefully based on business priorities, long-term trajectory, and willingness to digitally transform. Hybrid enterprise data warehouse models blending both cloud and on-premise are also viable, as well as logical data warehouses.

Criteria for Evaluating the Best Enterprise Data Warehouses

Once you have a clear understanding of your needs and deployment preferences, it's time to evaluate potential EDW solutions based on the criteria below.

Data Modeling and Schema Flexibility

Assess how easily the EDW accommodates changes to your data model over time. Look for auto-optimization features and support for schema-on-read modeling alongside traditional schema-on-write. Evaluate the availability of data hierarchy, aggregation, and metadata management.

Scalability and Performance

Evaluate whether the EDW can scale up and out seamlessly to match the explosive growth in your data volumes and user base. Review query response times under load and examine throughput benchmarks for concurrent user queries. Check for in-memory caching, workload management, query optimization, and other advanced performance tuning features.

Security and Compliance

Check the availability of granular role-based access controls, dynamic data masking, and row-level security policies to protect sensitive data. Review built-in auditing, activity logging, and anomaly detection mechanisms. Aim for regulatory compliance certifications such as HIPAA, GDPR, or SOC-2.

Data Integration and Pipelines

Choose a platform with robust data integration architecture, workflows, and pre-built connectors to simplify loading batch or streaming data from disparate sources. Review offerings for data quality, preparation, governance, and lifecycle management.

Analytics and Visualization

Leading EDW platforms integrate advanced analytics capabilities such as machine learning, predictive modeling, and text and image analysis to supplement SQL querying. Some also provide intuitive, no-code drag-and-drop visualization for on-the-fly dashboard creation.

Cloud Architecture and Services

For cloud-based deployment, scrutinize the underlying infrastructure architecture, service level agreements, failover mechanisms, elasticity, and security controls.

License Cost Structure

Calculate the total cost of ownership over three to five years, factoring in license fees, data egress charges, professional services, and hardware refresh needs.

Beyond technical capabilities, the vendor's vision, stability, partnership approach, and market footprint matter, too. Develop a scorecard to rate solutions based on your "must-haves" and ideal wishes.

The Bottom Line

The truth is data enterprise warehouses are from a bygone era and are best suited only for enterprise-level companies. For startups and SMBs, there are much better options, such as data virtualization, logical data warehouse, and zero-ETL.

Partner with Peaka to meet your data integration needs with its state-of-the-art zero-ETL approach, offering hassle-free data integration without having to copy or move your data.

See Peaka’s growing library of custom integrations.