Rethink Your Data Architecture To Raise The Bar On Innovation

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Businesses are being overcome by an avalanche of data, with some estimates pointing to creating 2.5 quintillion bytes of data per day. However, what should be an embarrassment of riches has become a lost opportunity because it’s becoming clear that existing data architectures are simply unable to accommodate what is being presented to them. By some accounts, only 0.5% of the data gets analyzed and used. Many businesses have simply been collecting data with no way of deploying it. Now living in a data swamp, businesses lack the infrastructure to clear it.

Unfortunately, time is not on their side to find solutions. We have all noticed that COVID-19 has quickened the pace of digital transformation, with companies rushing to find ways to accommodate new ways of working and serving their customers with innovative new approaches while actively managing their data. As I was talking to a few data architect friends, I started wondering what’s needed to redesign data architecture with a fundamentally different but open approach.

Existing data architectures - how did we get here? 

Let’s look back at some of the existing data architecture systems as we evaluate where to go from there. 

Operational data started in the ‘80s because businesses could not get hold of their performance and needed numbers to evaluate their key metrics. As this started to gain interaction, there came a saturation point for businesses to find a place to store these pieces of information. This led to the invention of prem-boxes or data warehouses.

One big problem that businesses faced while using these boxes was the lack of scalability. Also, companies then started looking for answers to detailed questions. This was made possible through the use of artificial intelligence and machine learning.

Another breakthrough that came in 2010 was the data lake. It was cheaper, and businesses could throw all their information in there. But when most businesses aimlessly dump such information in the data lakes, they become data swamps making it impossible to analyze the data and its sources.

To handle data of the different business units, data experts started to use data marts for retrieving client-facing data. This gave enough freedom for businesses to work on various data groups without much hassle.

Where do we go from here? 

For decades, data architecture has been going back and forth — from data warehouses and data lakes — to build a system that sustains the demand of business needs and upgrades. However, the biggest question is whether they can store information like the future demands and make it accessible for analytics at the desired speed. With the rise in demand for data collection, it will be challenging for businesses to adopt any single method that would complement their style. To help solve this ever-growing problem, businesses will require a channel that scales with them. 

In the last few years, we have seen some new approaches to upgrade the existing data architecture. But data replication and the growing number of users demanding the same data inhibit the current architectural scale-up of data marts and warehouses.

In contrast, others developed a better data processing system to extract extra value from big data.

Various companies like SingleStore came up with in-memory databases that made “hot data” readily available for businesses. However, this may not be the best approach since managing large batches of data can be a problem.

Another notable company in this domain - Databricks - offers a cloud-based platform to process large chunks of data within minutes using advanced machine learning models. Snowflake provides a cloud-based “data warehouse-as-a-service” to store and analyze big data. However, these solutions haven’t been as fruitful as they should have been. With the data explosion at its peak, businesses are looking for an architecture that lets them dump their data and personalize how these pieces of information are stored while maintaining a healthy price-performance ratio.

I believe what’s needed is a scalable solution that rethinks data architecture with a fundamentally different, open approach. As an example, Dremio recently launched Dremio Cloud, a SQL lakehouse platform that enables high-performing BI and analytics directly on cloud data lake storage such as AWS S3 or Microsoft Azure ADLS. It’s designed to accelerate the speed at which organizations can maximize their data at scale without requiring software or having to move or copy the data into data warehouses, marts, extracts, or cubes. Further reducing the zone of confusion between data lakes and data warehouses, to run BI and analytics directly on top of data lakes.

Built from the grounds up on open standards, Dremio’s architectural structure removes the complexity of copying and moving data, thus, provides efficient SQL workloads directly on cloud storage. This approach is one of the safest methods to store data, where businesses can keep their data in their personal vendor account. It has boosted business intelligence and analytics with a unique global control plane that directs client queries into specific engines, according to the set user-defined rules. This also allows businesses to connect BI tools to the Dremio Cloud and offer a passwordless experience. In essence, this global control plane provides a single pane of glass experience that enhances observability, ease-of-use, and management at scale.

Notably, the platform breaks down data access barriers so it can be used by data scientists, analysts, the C-level, and other professionals within the organization. The data-processing system promotes business intelligence, fast data migrations and enables you to join data from external sources while continually pushing the boundaries of price, performance, and ease of use.

The next step for data scientists would be to deploy, manage, and get value from datasets and AI algorithms at scale - this is something that a company like Modzy can do. Modzy is a ModelOps and MLOps software platform for businesses. You can deploy it for the enterprise anywhere - on-premise, cloud, or at the edge, with easy integration via open source SDKs and open API.

We have reached a saturation point of collecting data in data lakes and data marts. Companies need a new robust data architecture that scales with their needs and accelerates the speed at which they can access data to keep up with the pace of innovation.