What Is a Data Lake: How It Differs From a Data Warehouse

Data is king. This truth has remained across most of human history. Today’s businesses are working with cosmically large amounts of data points, which makes it equally challenging to store and analyze any of them efficiently.

Instead of letting countless improvements fall through the cracks, data lakes offer the flexibility and agility for businesses to explore the potential of their data. 

What is a Data Lake?

A data lake is a centralized storage system that amasses enormous volumes of data before it’s altered for various purposes. This data can later be converted into forms more suited for analytics and machine learning.

Data lakes are scalable to any size and aren’t restricted to a specific data type like images, text files, or audio. This makes them a much more flexible section of the data pipeline than other parts like a data warehouse.

While the concept of a data lake is widely accepted, it doesn’t have a predetermined architecture. Due to the massive amount of data feeding into this repository, a data lake requires a lot of power and tools to run smoothly. Every business relies on different controls, which are configured to suit that organization’s needs.

Key Differences Between a Data Lake and Warehouse

A data warehouse is another data storage and analysis architecture optimized for specific business systems. Multiple databases feed into a data warehouse while changing their data into its preferred state. This is known as an ETL (extract, transform, load) process.

These ETL processes allow data warehouses to perform SQL queries much faster than a standard database. Since there’s so much data to go through, the speed of a data warehouse is invaluable when an organization needs to make a quick decision. However, a warehouse requires deliberate setup beforehand because ETL processes must know how to refine the original data.

Additionally, the data inside a warehouse can only be as current as what’s fed into it. This means that if data changes in the database, the warehouse will analyze incorrect information until the following ETL process.

On the other hand, a data lake doesn’t need ETL processes to function. Data in a data lake can be raw and unstructured, which means it can take in information from a more extensive variety of sources than a warehouse. This diversity of data makes data lakes perfect for feeding machine learning and artificial intelligence programs.

The issue is that it’s challenging to reliably compare unstructured data like is used in a data lake. So, a data warehouse is a better choice if the goal is analysis. Both data lakes and data warehouses serve distinct purposes in an organization’s data infrastructure, and no one solution is better than the others.

Benefits of Data Lakes: Why Should You Use It?

Utilizing more data from every possible source is the primary upside of a data lake. A data lake can take in raw data, skipping the data cleanup step required for a data warehouse. This allows it to analyze and compare data in different ways that might be impossible if the original data was changed.

Some reasons to integrate data lakes into your existing infrastructure include:

Better Understanding of Your Consumers 

Customer data comes from many places. It considers how they’re interacting with social media, responding to emails, and what they’re buying. These factors don’t share a metric system, which makes them incredibly challenging to compare and analyze. Data lakes keep them in their raw form so you can better grasp their individual and combined impact in certain areas.

Improve Decision Making

Data lakes operate with the most data and are the best choice for teaching AI and machine learning. Data lakes are also the best place to predict what new products or policy changes will be well received. Knowing what’s more likely to work will ultimately reduce R&D costs considerably.

Challenges Of A Data Lake

Data lakes aren’t without their problems. A single lake often has dozens of sources feeding into it. While this is one of a data lake’s greatest strengths, each source operates with a different schema or organizational structure. So, performing tasks like quality control and data restoration is challenging. If the data in a data lake is inaccurate, it can slow operations and lead to poorly informed business decisions. 

Inconsistent Data Issues

Data lakes profit from absorbing information in real time, unlike data warehouses, which need data to be cleaned beforehand. However, this speed sometimes leads to reliability problems as it’s hard to catch corrupted incoming data. The problem is that comparing data with different measurement systems sometimes leads to inconsistencies.

Slowed Performance

By their nature, data lakes can become massive beasts. In theory, more data means high accuracy, but some query engines can’t keep up with the workload. After a while, the programs slow down and create bottlenecks further down the pipeline. Scaling your hardware and software to match a growing data lake is essential to maintaining efficiency.

Higher Security Risks

Data lakes are typically a one-stop shop for all of an organization’s information. It’s apparent that they’ll be a prime target for ambitious hackers aiming for a data leak. Data lakes lack fluid permission and access control that other storage solutions have and rely on security based on things like metadata definitions.

There’s also no foolproof method for figuring out everything in a data lake. This means that if data is stolen from one, the administrators aren’t sure what they should defend against or warn others for.

Data Lakehouse

A data lakehouse is a hybrid data storage option that takes the best parts of data lakes and warehouses. It steals the scalability of data lakes and combines it with the fast-querying capabilities of a data warehouse.

Data lakehouses also maintain the raw form of data, allowing them to ingest data from the same number of sources as a traditional data lake. However, lakehouses also include several “metadata layers,” enabling efficient querying with SQL tools.

Metadata layers serve as a middle ground between unstructured and structured data. While data lakes don’t attach meta information to data, a lakehouse can identify and extract particular features from data and structure it accordingly.

Data lakehouses can also “evolve” these metadata layers depending on incoming information. The metadata used to structure existing information can change accordingly if a new, more specific category becomes necessary.

Introducing the data lakehouse gives organizations the best of both worlds, but the cleaning process in a lakehouse isn’t as sophisticated as a warehouse. So, the switch may not be for everyone.

Data lakehouses are most suited for organizations looking to beef up their analytics with artificial intelligence. As the amount of incoming unstructured data increases, it’s becoming more necessary to put it all to good use cost-effectively.

Overall, a data lakehouse performs well in both flexibility and analytics and should be a serious consideration for organizations with growing data needs.

Keep Your Data Safe

Data lakes, warehouses, and lakehouses are technologies every business should be intimate with. They help you make sense of the endless flow of data coming in and tell you what direction to take your business.

If your goal is to answer specific questions about your operations, then you should rely on the fast querying of a data warehouse. However, a data lake would be most helpful if you’re hoping for general insights and places to improve.

Either way, the technologies have continued to evolve, and it won’t be long before solutions like the data lakehouse overtake them both. If you find it hard to keep up with the constantly changing world of data storage and security, consider visiting IDStrong and learn how to prevent data loss while leveraging the information at your fingertips!