There are many different ways possible to manage, store, and extract big data. Two of the major ways include Data Lakes and Data Warehousing. Though broadly spoken, Data Lakes and Data Warehouses are interchangeably used when in reality it is not the case. So today, let’s talk in detail about what data warehouses and data lakes are with the key difference between them to help us understand in detail how Data Lakes are essential for next-generation Data Management. 

What is Data Lake?

We know for a fact that the Volume, Velocity, Variety, and Veracity of the data being generated have increased causing “big data” to be synonymous with data nowadays. But with this amount of data it is important to place a data strategy or a data management system in place to make sure all the operational, transactional, and master data are stored, managed, and leveraged effectively.

As for what a data lake is, instead of talking in complex terminology, let’s take the example of books from the perspective of a book shop. For the end-user books are labeled and organized as per their genre into multiple sections, but for the retailer when he gets them the books are all mixed together and dumped at one site, from where he/she begins the sorting process.

Similarly, with a data lake, it is the place or a central repository of all the data that exists in its natural, raw, granular form. That means the data lake can store unstructured data or semi-structured data, therefore, allowing more flexibility wrt its use for the user. It is for this reason that data lakes are used whenever data scientists or advanced analytics researchers need the data without a predefined structure.

Data Lake vs Data Warehouse

Before we go into the details of the difference, a brief about what Data Warehouse is. Data warehouses store data in one place which has applications in Analytical Reporting,i.e., similar to a data lake it is useful to store data in one place but with the schema predefined ensuring data quality (which requires some amount of data cleansing, etc.)

Even though both the data lake and data warehouse can be treated as central repositories, in essence, they are different when it comes to architecture, usability, and accessibility.

1. 1. The difference with Architecture: Data Lake uses Flat Architecture which allows data to be stored in its native format. Data warehouse, on the other hand, uses a hierarchical format with files and folders in a predefined format, this is useful to access information and make quick decisions
   2. The difference with Use: Data warehouse is synonymous with the use of Business Intelligence, whereas data lakes are used when the users in question are Data scientists and are going to use analytical tools and statistical modeling to transform data into insights.

• The difference in Accessibility:  Data Lakes are relatively cheaper than Data warehouses the data is stored in raw format and not with any predefined hierarchical structure, which is also one of the reasons why Data warehouses take longer to establish. 

There are more differences, but instead of going all about the differences, here is a short summary of the basic differences between Data Lake and Data Warehouse in the image below:

Different Types of Data Lakes

Based on where the data resides, data lakes can be divided into 4 types:

1. On-premise Data Lake
2. Cloud Data Lake
3. Hybrid Data Lake
4. Multi-cloud Data Lake

As the name suggests, On-premise DL involves all the hardware, software, and processes being managed by the in-house IT team, whereas in the Cloud DL, all the infrastructure is outsourced and organizations can access data from the same. In comparison between the two Cloud Data would incur higher operational costs but On-premise needs higher infrastructure costs. 

Hybrid Data Lakes are the ones that combine both the on-premise and cloud DL to suit their needs, and the multi-cloud DL strategy involves storing the data in multiple platforms like Azure, AWS, GCP, etc. This strategy might need greater expertise than an individual cloud platform.

Benefits of Data Lake for Data Management

Some of the benefits that Data Lake provides to better Data Management are 

1. The simplicity of data storage
2. Better Scalability and Versatility compared to Data Warehouse 
3. Ability to integrate Advanced Analytics 
4. Better Flexibility and multi-format integration 

How is Data Lake essential for Modern Data Management?

We have seen the advantages that Data Lakes carry for an organization, but we haven’t spoken about one of the main downsides- the transformation of Data Lake into a Data Dump if the management is not proper. But with proper data management, this downside can be avoided. 

Now that we have spoken about both Data lake and Data warehouse, let’s talk about what Modern Data Management would look like. This would be dependent on what the end-use case would be. With the increasing reliance on data for analysis and decision making, leveraging both BI and Data Science would be important for firms. So, this would need the requirement of a combination of DL-DWH Architecture to achieve the best of both worlds. 

With the convergence of DL/DWH users have the flexibility of doing data exploration and OLAP

reporting from a single solution. Therefore, silos can be eliminated and the data types for which analytics can be performed are increased.

In Conclusion: How to achieve a unified DL/DWH

In order to have an effective data management implementation, it is necessary to 

1. Know what you are unifying and how Data Lakes come into the overall strategy of having MDM.
2. Build High-level data architecture to support traditional BI/reporting/OLAP and modern Al-ML requirements. 
3. Plan for IT Training to utilize low-code/no-code design paradigms and complex SQL coding 
4. Implementing modern Data Engineering Tools to improve performance, reduce costs, and simplify the tools and skills required to load and prepare data. 

Summary

If you’ve made it till here, then one, either you have read the entire article or two, you must be looking for a short summary about the entire article. So this part is to cater to the second set of people. We have spoken about how Data Lakes are important in modern data management by understanding what data lakes are, and how they are different from Data warehouses wrt architecture, use, and accessibility, then we understood how important data lakes are for data management, and then we concluded by discussing the next key architecture of a combined DL/DWH which offsets the drawbacks of both the DL and DWH individually to bring a new reliable and more accessible high-level architecture.

If you are interested in implementing Data Management or Data Warehousing or Data Lake services, feel free to drop a message.

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