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Ease of Access in Data Lake Deployments

To understand whether or not a data lake is the right framework for your business, it helps to know the similarities and differences between a data lake and data warehouse structures.
Ease of Access in Data Lake Deployments

A data lake is a large pool of raw, unstructured data for which a purpose or application has not necessarily been defined. A data warehouse is a repository for data that has already undergone a filtering and structuring process preparing it for analysis, application, etc.

When it comes to determining which is right for business users, it often relates to matching the technology with the needs of the customers and company as a whole when it comes to things like data preparation, analytics, and real-time data. For example, the popular online multiplayer game of Fortnite utilizes a data lake to store purchase transaction data from their customers. When working with the massive community of gamers that play this game, the company needs to handle petabytes of data from various data sources. The data lake structure builds in this flexibility in storage needs along with delivering fast enough access speeds to keep Epic Games, the creator of the hit video game, data driven in all its decisions.

While a traditional data warehouse structure is not ideal, it depends on just how much data is being stored and how much new data is being created daily. A data lake allows for quicker upload to storage due to the lack of structure in the uploaded raw cloud data. So, to determine if a data lake is an optimal strategy for your company, here are two things to keep in mind.


The first thing to consider is access. With countless tools for working with unstructured data available today, a data lake gives wider access to stakeholders within your business to make actionable decisions derived from these data sets and big data analytics. The challenge lies in ensuring that internal frameworks and training take place to keep this data accessible and ready for analysis and real-time applications. While data warehouses prepare data for specific outputs, data lakes allow for creativity and flexibility in how different departments might analyze the data. If they don’t have the mindset and skills in place to complete this analysis, this could cause slowdowns in productivity and new challenges if your business isn’t proactive.

Secondly, consider the context when determining what data will be stored in the data lake platform. Too often, companies make this transition and feel as though these enhanced and organic insights will just jump out to them from all types of data, being sold on generic pitches with buzz words like “AI, machine learning, predictive data science analysis” without an actual plan for implementation. When looking at moving to a data lake structure, the idea of new and unforeseen insights is a selling point; however, you cannot go into it without a plan of action concerning data and analytics. It’s important to build in some standard processing and analytics routines for this new data lake to ensure data doesn’t become unused and wasteful.