The Battle of Old and New: Traditional vs Modern Data Platforms

Long gone are the days where you had to buy hardware, create server rooms and hire, train, and maintain a dedicated team to run it. Cloud-based data platforms are the new norm. They offer a more agile, scalable, and cost-effective approach to managing and leveraging data. But does that mean the traditional data platforms are dead?

Not necessarily. 

While the rise of cloud-based data platforms has indeed revolutionized the way organizations handle data, traditional data platforms still have their place in certain contexts. The choice between modern and traditional data platforms often depends on factors like the nature of the data, specific business requirements, compliance considerations, and the existing infrastructure. 

Traditional data platforms may offer a sense of control and security for organizations dealing with sensitive data or operating in highly regulated industries. Additionally, some legacy systems may not allow for a seamless cloud migration, making traditional platforms a pragmatic choice for certain organizations.

However, for most businesses, traditional data platforms no longer satisfy the requirements. 

They did a decent job analyzing traditional or structured data to inform business decisions. But as the demand for analytics increased, these solutions couldn’t handle the volume, velocity, and variety of data and analytic workloads prevailing in the modern enterprise.

There are five key challenges inherent in traditional data platforms:

Inflexible Structure

Whenever information must be readily available, IT architecture must be flexible and responsive, facilitating spur-of-the-moment decisions and frequent adjustments.

The lack of flexibility has consistently been a noticeable drawback in traditional data platforms.

Even a simple request to modify a data model can turn into a lengthy process, involving multiple individuals and requiring entirely new data sources. This creates significant costs in terms of time, money, and effort. 

Outdated Technology

The design of traditional data platforms excludes features like parallel processing and in-memory storage, which have been proven to significantly improve data processing capabilities. Additionally, there's a constraint on the amount of data you can store, and given that businesses are currently handling terabytes of data, expanding the on-premise storage capabilities can be highly complex and time-consuming.

Finally, vendors may discontinue support and updates for outdated technology, leaving businesses with unsupported systems. Not only does outdated technology cause issues with software, but also hardware, including processors, memory, storage, and networking.

Slow Performance

As businesses generate and analyze ever-growing datasets, traditional data platforms become slower, delaying the discovery of crucial insights.  This performance bottleneck not only impacts operational efficiency but also slows down the real-time decision-making processes businesses depend on. 

Lack of Governance

Establishing efficient data governance requires achieving a balance among people, procedures, and technology. If any of these components falls short, the entire strategy becomes compromised. Traditional data platforms not only encounter challenges in supporting data governance but may even jeopardize its effectiveness. 

High Costs

The traditional data platform was not designed to efficiently accommodate the scale of users and data we encounter today in a cost-effective manner. The cost of scaling up is high (due to on-prem infrastructure, licensing, upgrading, and operational fees) and the costs related to centralized report generation are on the rise. Also the demand for specialized skills to ingest, transform, consume and govern data is not only costly but also proves to be difficult as the engineering workforce shifts away from on-premises technologies. This underscores the pressing need for a more adaptable and cost-efficient data solution.

Given all the challenges associated with the traditional data platforms, we can see it fails to deliver and keep up with the data demands of any modern, data-driven organization. To maintain a competitive advantage, organizations need data that they can act on at the right time as well as being flexible enough to adapt to changes. 

So, what capabilities do modern data platforms bring to the table? What advantages do they offer and can they live up to the expectations?

A modern data platform, also referred to as a modern data stack, is composed of multiple components, each having its own function.

neptune.ai

1. Data ingestion

This layer involves the transfer of data from diverse sources (databases, server logs, third-party applications, etc.) into a storage medium.

2. Data storage

A data warehouse, data lake, or a lake house is a solution, often cloud-based, used for storing all the accumulated data received from the data ingestion tool. In this repository, the data is accessible and available for analysis.

3. Data transformation

After transferring raw data into storage, it’s necessary to transform it into user-friendly data models. This enables analysts or data scientists to easily query the data for extracting insights, creating dashboards, or building machine learning models.

4. Data analytics/ business intelligence

In this layer, users examine data and generate dashboards to explore that data. Modern data analytical tools are also created with non-technical users in mind, enabling domain experts to address business inquiries independently, without relying on developers and analysts. 

5. Data governance

Modern data platforms prioritize data governance and security to safeguard sensitive information, adhere to regulatory requirements, and oversee data quality. Tools that support this layer incorporate features such as data access control, encryption, auditing, and tracking data lineage.


It’s important to note that a modern data platform setup is modular, making it easy to mix and match different parts and tools. This flexibility allows you to change components as needed and adapt the setup to work with your existing infrastructure instead of replacing it entirely. 

The modular design, in contrast to a monolithic structure, confers the additional advantage of horizontal flexibility, helping you avoid getting stuck with a single vendor. If a tool from one vendor for data storage isn't working well for you, you can easily switch to another vendor that better meets your needs.

In addition to lowering the technical barrier to entry, a modern data stack comes with several benefits. 

User-friendly design: Modern data stacks prioritize business users, reducing technical barriers and enhancing accessibility.

Flexibility: The composable cloud architecture ensures flexibility, eliminating vendor lock-in and consolidating data into a centralized warehouse.

Scalability: SaaS tools offer scalability and cost-effectiveness by allowing you to pay for usage. Resources can be scaled in real-time based on demand, eliminating the need for complex hardware provisioning.

Time efficiency: Off-the-shelf connectors in modern data stacks save time for data engineering and analytics teams. This efficiency allows teams to redirect their focus toward driving impactful business outcomes.

Cloud Pricing Model

As we already mentioned above, high costs are one of the main challenges associated with traditional data platforms, especially licensing and maintenance expenses. A modern data platform however, allows for leveraging different levels of costs savings with its three, most common, pricing models:

• On-demand

• Reserved instances

• Spot instances

With reserved instances, cost savings can go up to 80% depending on the number of years(1-year or 3-year terms), instance type, and region, while with spot instances, cost savings can even go up to 90%, depending on the market auction.

On-demand Pricing

The simplest and most commonly employed pricing model at cloud providers is on-demand or pay-per-use pricing. With this approach, you pay only for the resources you consume. While it’s the most flexible cost model for cloud services, it’s also the most costly one. Pay-as-you-go cloud resources can be easily scaled in real-time, and there is no requirement for a long-term commitment.

Reserved Instances

With reserved instances, cloud providers offer discounts to businesses committing to a specific volume of resources over a defined period, typically lasting one or three years. In exchange for this committed usage, providers offer substantial cost reductions.

While cost savings are a key advantage of reserved instances, it's important to note that if you miscalculate the amount of resources you need, you may end up having to pay for the unused capacity.

Spot Instances

Spot instances are ideal for businesses with adaptable workloads that can tolerate a degree of risk. Available at discounted rates on a first-come, first-served basis, unused EC2 instances allow organizations to efficiently manage fluctuating demand levels while reducing overall cloud costs.

Spot instances are generally well-suited for:

• Batch processing and data analysis

• Fault-tolerant applications

• Stateless web applications

Your organization's choice of cloud cost model will have a direct effect on your overall cloud expenses and will be a crucial factor in your strategy for optimizing those costs. Opting for the appropriate cloud cost models aligned with your requirements is a significant step towards reducing expenses and adopting a more strategic approach.

According to Accenture, 72% of the C-suite say their legacy platforms are holding them back. 

And while they’ve served their purpose well in the past, a modern data platform is now an imperative for organizations aiming to streamline and democratize data exploration, automate routine data management tasks, and accommodate diverse data and analytics workloads.

These platforms provide unmatched scalability, flexibility, and cost-effectiveness when compared to traditional counterparts. 

However, there are cases where a transition might not be immediately suitable. Organizations dealing with highly sensitive information, regulatory constraints, or those with substantial investments in legacy systems may find the transition challenging. In these cases, the need for stringent data security measures and compliance may outweigh the advantages of a cloud-centric approach. 

The decision to adopt a modern or cloud data platform should be a strategic one, carefully balancing the benefits with organizational requirements and constraints.

Consider the nature of your data, the skills and tools already in place, your usage needs, and your future plans. Some of the questions you should answer are:

• Which departments will a cloud data platform impact? 

• What types of queries will you run, and by how many users? 

• What volume of data will users require access to, and how quickly?

• Is there a strategy in place for securely sharing data internally, and possibly with customers or partners?

• How much do you want to invest to monitor and manage availability, performance, and security? 

• Will your existing applications seamlessly integrate with the new platform?

With proper planning and consideration, migrating to a modern data platform can lead to significant benefits for your organization.  You could leverage advanced analytics and AI capabilities to standardize data-driven decisions and processes in all areas of your organization.

And isn't it worth exploring how such a transformation could push your business into an exciting new era of efficiency and innovation?

------------

Join us at Data for Breakfast with Snowflake:

📍Lausanne, March 19, 2024: https://bit.ly/3uY8zUA
📍Zürich, March 21, 2024: https://bit.ly/433kLjo