How do you manage growing enterprise data volumes while maintaining data quality, consistency, and governance for large-scale analytics? Most organizations seeking solutions wish to evaluate data without passing it through complex ETL processes. Data and analytics have become buzzwords throughout the last decade and beyond. At a high level, it consists of two distinct processes: data storage management, and data analysis. The ever-increasing volume of commercial data has compelled organizations to investigate alternative methods for storing data effectively, safely, and efficiently. Organizations won’t be able to use analytics to gain business insights unless they’ve completed this step. The key is how fast the stored data can be extracted, transformed, and loaded (ETL) for analytics, and we can accelerate the ETL process. This is where the different approaches to data storage become prevalent. 

Here’s a closer look at the journey of enterprise data, including data warehouse, data lake, and the newest solution on the market today – Data Mesh. 

Data Warehouse

A data warehouse centralizes and processes large amounts of data from multiple sources. It has inherent analytics capabilities that allow enterprises to derive business insights. The Data warehouse follows a “schema-on-write” approach where data is extracted, transformed, and loaded (ETL) when storing it. 

Data Lake

Data Lake essentially refers to a data repository where large amounts of structured, unstructured, and semi-structured data could be parked. Data lake centralizes, organizes, and protects data that follows a schema on reading approach meaning data can be structured at query time based on a user’s need. 

Conventionally, a data lake on its own does not have analytics capabilities. The data lake is usually a cloud-based data repository that needs a data analytics engine on top for data processing actions such as indexing, transformation, and querying. 

Data Mesh

Data mesh is a more modern method of enterprise data management that has emerged in recent years. The enterprise data industry defines data mesh as “a shift in a modern distributed architecture that utilizes platform thinking to develop self-service data infrastructure, treating data as the product.”

This is essentially referring to an architecture that supports distributed data. This system makes data accessible to every user as needed based on their access rights. A data mesh allows data to be distributed across multiple databases and need not be centralized in a single storage lake. This means a data mesh architecture can connect various data sources, including data lakes, into a coherent infrastructure.

A comparative view: Data Mesh vs. Data Lake

While considering these enterprise data management systems, it is crucial to understand that it is not a matter of a “one size fits all.” These approaches have pros and cons, and the choice of which enterprise data management system to use is essentially down to the enterprise’s specific needs. 

The terms “data lake” and “data warehouse” are sometimes misused interchangeably. A data lake is a collection of unprocessed data that must be handled as necessary. In contrast, a data warehouse stores data that has been processed for a specific purpose. Simply said, a data lake is selected when advanced analytics capabilities are required. Still, a data warehouse can be implemented when data is necessary for operational purposes, such as in the financial services sector.

Data mesh, however, works on the principle that data will always be distributed. It works very well for organizations that use multiple databases. It has a limitation in that the speed of querying data from a data mesh is limited by its slowest query. 

As different as they are, the data lake and data mesh could be complementary. For organizations that have data distributed across multiple databases yet want to run faster queries, it makes sense to use a data lake platform for analytics within the existing data mesh architecture.

Data mesh ensures data democratization because it supports decentralized data architecture. Equally, the free flow of data across the organization is a byproduct of data lakes. Looking ahead, the goal of enterprises is to become data-driven and ensure systematic access to data across the organization. This space is still evolving and one to watch out for.

Vatsa Solutions has a team of experts in data engineering that has helped multiple businesses harness the power of data and create meaningful insights from it. Reach out to our experts to learn more about the various options available in enterprise data storage and understand which is best suited for your organization.