Does your organization depend on data to make critical business decisions?

If so, your organization should be leveraging a data lake architecture to extract the most value from your data sources.

The enterprise information management market has been disrupted and continues to experience change that impacts how organizations compete and provide value to customers. The proliferation of cheaper storage hardware and free open source software has enabled organizations to store more data and do more with it through the integration of data lakes. Traditional data warehouses can be rigid and inflexible when it comes to analyzing and incorporating new data formats. Therefore, organizations could be missing opportunities to integrate critical data into their analytics, leading to less informed metrics and reporting. This could potentially be costly to an organization and lead to overlooked opportunities. To ensure opportunities are not overlooked, it is critical that data systems are modernized to allow all data formats to be analyzed.  When modernizing data systems, a data lake should be considered part of an organization’s strategy to capitalize on the vast amounts of business intelligence stored in its data.

Over the coming weeks, we will publish a series of blog posts that provide an in-depth overview of a data lake and how one can optimize your enterprise information management.

But first, let’s cover the basics;

What is a data lake?

A data lake is a storage management system that can store unstructured, semi-structured and structured data in one repository. Meaning that it can store, adapt and analyze most data in its native format. A data lake imposes only a few trivial prerequisites to storage. Other incumbent storage technologies require certain types or formats for persistence. Data lakes allow the data to be easily queried for various organizational data needs. Due to minimal storage requirements, table columns and data types stored in each column (email, date, number, etc.) aren’t defined until the data is queried.

How is this different from a traditional data warehouse?

Traditional data warehouses are structured and bound by storage rules that require strict definition of column types of a table. Because of the structured format, the framework is rigid, and new data formats or representations cannot be easily integrated. As data formats and attributes are continually evolving and changing, a rigid structure could hinder an organization from storing the data (without an investment to change the store structure) and prevent it from making the best use of its data for informed organizational decisions. Because of this rigid structure, we believe that the benefits of integrating a Data Lake with your existing Enterprise Data Warehouse can be the most effective way to achieve the benefits of both while continuing to see a return on your warehouse.

Over the coming weeks, we will be analyzing:

• Data Lakes vs. Data Warehouses
• How Data Lakes can add value to your organization
• How Data Lakes can be integrated into existing Data Warehouses
• Best practices for Data Lake integration