Planning a Data Lake? Prepare for These 7 Challenges

RTInsights Team
Published in
4 min readMar 28, 2024


By: Kausik Chaudhuri

“Build a data lake!” has become one of the standard points of advice for organizations with large amounts of data to store. As data lakes offer a convenient, centralized location that can house data of all kinds, they often seem like an obvious solution for businesses that need to share disparate types of data with multiple stakeholders.

They can be, but only when they’re optimally designed and managed. Data lakes can also present significant challenges, which are critical to understanding before committing your company’s information to a data lake.

Before diving into the challenges, let’s briefly define data lakes.

A data lake is a centralized repository for storing data of all types and at any scale. Its core purpose is to allow organizations to take the disparate data assets they own — such as various databases, documents, media files, and so on — and house them in a central place where anyone who needs to access them can easily do so.

This is what data lakes are meant to do, in theory. In practice, several challenges may hinder the effectiveness of data lakes.

Data lake challenges

Here’s a look at seven key challenges that organizations need to address to get the most out of data lake architectures.

See also: Maximizing the Value of Your Data Lake

1) Cybersecurity risks

When users populate all their data in a single location without managing security features, the data is often at risk of manipulation by threat actors. A data breach targeting the data lake can mean that external users gain access to the data assets the business manages. Unless you implement strict cybersecurity controls, your data lake becomes a prime target for attack.

2) Compliance challenges

Storing data in a central location simplifies compliance in the sense that you know where your data resides, though it also creates compliance challenges. If you store many different types of data in your lake, different assets may be subject to different compliance standards. Data that contains personally identical information (PII), for instance, must be managed differently in some ways than other types of data to comply with laws like DPA, GDPR, or HIPAA.

While a data lake won’t prevent you from applying granular security controls to different data assets, it doesn’t make it easier, either — and it can make it more difficult if your security and compliance tools are not capable of applying different policies to different data assets within a centralized repository.

3) Data integration headaches

Placing your data into a central location to create a data lake is one thing, but connecting it to various applications and the workforce that needs access is another. Until you develop the necessary data integrations — and unless you keep them up to date — your data lake will deliver little value.

Building data integrations takes time, effort, and expertise, and users sometimes underestimate how difficult it is to create successful data integrations. Be sure to prioritize data integration strategy as part of your overall process.

4) Data performance risks

While data lakes can theoretically accommodate any volume of data, in practice, performance often suffers as they scale up. The more data you have in your lake, the more difficult it is to ensure that the data moves quickly, that you can run fast queries on data assets, and so on.

Addressing these risks requires careful attention to the infrastructure that hosts your data lake, which needs to scale as data scales to ensure adequate performance. Optimizing the way data is stored is also important for maintaining optimal performance.

5) Single point of failure

Placing your data in a data lake means creating a single point of failure. If the infrastructure that hosts your lake fails, your data becomes unavailable.

Backups and replications can help in this regard. However, they’re only a partial solution because backup data may not be coordinated with production data, and both options will add additional costs. Plus, it takes time to restore data from backups, especially if you lack a well-designed data recovery plan and the right tools to implement it.

6) Data quality challenges

Keeping on top of data quality can be challenging when you have many different data types stored in a data lake. To optimize data performance and infrastructure utilization, you’ll want to perform tasks like data deduplication. Remember that the vast scale of a data lake, combined with the constantly changing nature of data inside, makes this cumbersome if you lack proper data quality tools and processes.

7) Management challenges

Data lakes are a unique type of data architecture. They’re different from databases, file systems, object storage systems, and other approaches to storing information.

As a result, data engineers who don’t have experience with data lakes may struggle to design and manage them optimally. Not every organi…

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