StorageWhat Is a Data Lake?

What Is a Data Lake?

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A data lake refers to a central location where large amounts of data are stored in a raw, unstructured format with information about the data and unique identifiers. They store data that can be processed later to extract valuable business insights and drive your business forward. 

This type of flexible organization allows for the storage of both structured and semi-structured data without any concern about being locked into proprietary systems such as data warehouses. This is ultimately more durable and cost-effective, though data lakes require an expert eye for effectively managing and processing data.

How to Build a Data Lake

Here are the steps you need to consider if you are seeking to set up a data lake for your business:

1. Choose a flexible cloud storage solution: You can set up data lakes on platforms such as Amazon Web Services and Microsoft Azure. Using one of these services saves you from incurring huge costs upfront since these are cloud services that will only charge you based on how much you use each service.

2. Figure out where your data is coming from: It is helpful to identify the sources of data and the frequency with which new data is added. You can choose to add the data as is or choose to clean it up according to your organization’s requirements.

3. Set up processes: Data will be coming from different sources. You can communicate with various departments to determine the best procedures, workflows, and timelines for publishing data.

4. Test data lake: It is important to test your data lake often to ensure that you are able to successfully retrieve and use data from your data lake. This is especially important to ensure continuity as your business needs grow and change.

5. Using your data: After you have gone through the steps above, you will have a system in place for collecting your data effectively. You will then need to use various extract, transform, and load (ETL) processes to get value from your data. You can use data warehouses and visualization tools to achieve this. Solutions such as Microsoft Power BI and Tableau are useful for crunching the numbers and deriving meaning from your raw data.

Data Warehouse vs. Data Lake

While data warehouses and data lakes have the same purpose in that they are storage locations for data, there are some key differences.

First, a data warehouse has an intended data layout in mind before the data is read. A data lake, on the other hand, can accept data in any format. The data is organized after it is read in the case of a data lake.

Data lakes also require users with expert knowledge of different data types since the data is unorganized and in different formats. Data warehouses are more accessible to a wider audience since the structure is inherently well-defined.

However, the structured nature of data warehouses means that setting one up takes more time to configure and adjust. In contrast, data lakes can be adapted more quickly and easily.

Data Lake Benefits

There are many benefits to be gained from using a data lake:

  • Increased insight into business trends and opportunities
  • Lowered cost of implementation with open source technologies such as Hadoop and Spark
  • No requirement for data organization before processing
  • More flexible analytics methods

Data Lake Challenges

While there are many benefits of data lakes, it is important to be aware of the following challenges as well:

  • Risk of becoming a dumping ground for data that inhibits valuable analysis
  • Requires more experienced and knowledgeable users
  • Costs can spiral if the data lake environment isn’t controlled

Cloud vs On-Premises Data Lake


On-premises data lakes usually offer strong performance. This also means confidential data is kept under your control and there are fewer latency issues when accessing data. 

However, here are some challenges with an on-premises setup:

  • Physical servers can take up a lot of physical space
  • Setup can be a costly and time-consuming process
  • It can be difficult to add more physical servers, which limits scalability


On the other hand, data lakes in the cloud are more cost-effective since you only pay for what you use at any given time. They also don’t require you to set up physical servers, which means cloud data lakes are easier to scale up since you don’t have to add more physical server capacity. 

However, it’s important to be aware of the challenges of cloud-based data lakes as well:

  • Less security for sensitive data
  • Less control over data governance and accessibility

Read more on eWeek: Why Enterprises Struggle with Cloud Data Lakes

Data Lake Examples

It is useful to see successful implementations of data lakes to get a sense of real-world use cases.


Sisense makes use of the AWS ecosystem for its data lake. The company has more than 70 billion records, and it uses its data lake architecture to effectively manage this data. It is able to extract value from the data with various visualization tools, including Sisense’s own visualization software.


Depop is a social shopping app based in London. Thousands of customers who use the app for messaging and purchases create a constant stream of events and data. In turn, the company makes use of Amazon S3 to handle this massive stream of data and use it to inform their business decisions. 


ironSource is an in-app monetization and video advertising platform. It processes streaming data from millions of end devices and therefore needed a solution to handle this massive influx of data. The company chose Upsolver, which can handle streams of up to 500,000 events per second.


Peer39 is a leader in the ad and digital marketing industry. It analyzes more than 450 million web pages to garner the true meaning of the text they contain. This gives advertisers more accurate information, so they can maximize their advertising dollars. Peer39 makes use of Upsolver to handle this massive amount of data.


SimilarWeb is a marketing intelligence company that provides digital world insights. It is able to achieve this by collecting massive amounts of data from various sources. SimilarWeb needs to analyze thousands of terabytes of data, so it uses a combination of Amazon S3, Amazon Athena, and Upsolver to achieve this.

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