Data lake challenges and how to solve them?

Swimming in an unknown lake without prior knowledge or life support is a risky business. Something similar can be said about the much-talked data lakes.

Diving into these information repositories without understanding the risk, challenges, and even benefits is not a good idea, as the power of this authoritative data store can change the narrative of your data analytics, machine learning, and business intelligence. 

What is a data lake?

Data lakes are huge repositories of raw data, readily available for users in their raw versions, each with its unique identifier and metadata tags. The data lake does not adhere to a storage format of files and folders but has an open and flat structure with accessibility for users. 

These data lakes are inexpensive alternatives to highly detailed data warehouses. They are designed to handle any type of data (structured, semi-structured, and unstructured) so that users can deploy them in multiple ways, and from multiple sources, as per requirement.

Data accessibility enabled by data lakes has made analysis and insight procurement easier for companies. 

However, with its pros comes specific cons as well. 

Here are some of the data lake challenges companies may counter and how to resolve them. 

9 data lake challenges

1. Expensive implementation

While data lakes are more affordable than data warehouses, building data lakes is expensive. The high-cost of investment in implementing and maintaining a data lake can be a problem for smaller and new companies. Companies may go for free and open-source cloud platforms if they are looking for cost-efficient options. 

2. High scope for a schema mismatch

Since data lakes are repositories where data sources and structure, it gives way to a schema mismatch, when ingesting data from multiple sources, it becomes challenging to ensure the same data is encoded the same way, .i.e, a schema match. Using the data from these heterogenous data lakes has high chances of inconsistency, which would require the data team to comb through the entire file and clean up where necessary. This again leads to a waste of time, money, and resources. 

3. Management discrepancies

A business needs to look at the bigger picture to manage a data lake. It is vital that the data lake can harbor large quantities of unstructured data, deal with redundant data, and ensure its security. Since data lakes are open, it gets difficult to manage these tasks; therefore, data lake management becomes one of the top difficulties for engineers. 

4. Open source

While open source is great for accessibility and democratization, it also means vulnerability to external threats. Data lakes make data easy to use and implement, but they continue to be expensive and require major investments. 

5. Lack of performance

Data lakes, being open source, have too many moving parts. There are many stakeholders, open projects, and accessibility to almost everyone, making it difficult to manage them. While easy to use, data is subject to changes and inconsistencies that can render these data lakes useless or time-consuming. The crux lies in data cleaning right from the start and regular sessions of clearing up the data debris so that data lakes can be deployed as transparent storage. 

6. Lack of specialized skills 

The data lake infrastructure is very different from a data warehouse, making it difficult for conventional data engineers to navigate through the technology. Conventional data engineers lack the technical skills and competency to maneuver through it, and even if they do build their skills, they require time and effort to understand the concepts. There is a dearth of specialized engineers who can understand big data and data lake management, which results in companies investing time and money in training their workforce.  

7. Low data security and governance

Cloud platforms are great for storing data and deploying them for analysis. However, they only work when it comes to securing it. Data lakes are not very good at securing and governing the data at hand and need an additional barricade to safeguard it. The extra layer may result in more time, money, and resource investment. 

8. Slow data loading

Slow data loading can be due to the unstructured and semistructured data present in the data lake. The inconsistency among the videos makes it challenging to interpret. The variation in sizes, and the build-up make it difficult to load the said data, and extract during data engineering, which makes it imperative for businesses to build additional storage pipelines to ensure that data can be configured quickly and loaded within the appropriate timeline.

9. Lack of quality data integrations

Due to the variation in formats, data in these data lakes require proper integrations to be derived and analyzed. Primarily, data lakes lack these integrations, and thus, the data cannot be deployed for analysis and decision-making. 

Conclusion 

Now that we know the challenges, what are the solutions?

The immediate solution is to remove data silos and allow your unstructured data to be more flexible. It means that your data should cater to most use cases, fit different requirements, and have the flexibility to be changed as required. 

Engineers and businesses need to modernize how they build, manage and deploy data lakes.