In 1971, the first floppy disk was invented with a capacity just shy of 100kb. That capacity is equivalent to roughly just two pages of text. Fast forward 50 years, we now have hard drives with capacities of 20TB. That's approximately an increase to the factor of 200 million. However, companies and organizations are blistering through data limits, often reaching hundreds of terabytes and even petabytes.
As data becomes larger and more complex, the issue of storing and data integration becomes increasingly difficult. That’s why companies need to wisely choose between Data Lakes or Data Warehouses to store big data. Depending on virtualization, security, scalability and much more, companies can utilize these innovative technologies to better manage data warehousing into a data model that fits your company and to your needs of your desired enterprise data warehouse.
What are Databases?
Traditionally, databases are groups of information that house structured data, normally electronically on computers to have a deeper advanced analytics. They are controlled by Database Management Systems (DBMS) and are used to easily access, manage, modify, control, sort and update all data. Through this database, it is possible for data mining to occur to improve data quality and prevent disparate data to occur and become more centralized.
Nowadays, even phones and watches could be considered databases as they store infinite amounts of important information about yourself. Databases throughout business are generally used to improve business processes, store personal information, monitor customer behavior/activity and create business decisions through advance analytics. How can we do this? Through raw data, you can observe if your business process is scalable and the main factor is through technology to be able to compute into cloud data into a program that can help to simplify, understand and have a better data-processing visualization of what is the data telling you.
Recall earlier we mentioned structured data? Well, there is also something called unstructured data and it's important to know the difference to choose the right data solution for your organization to better understand data mining.
This type of data source is the most common and is what you probably already know of. Structured data is essentially data that can fit within “fixed” rows and columns. Alongside being organized, it is very beginner-friendly and easy to understand. For example, addresses, genders, and credit card numbers are all forms of structured data.
On the other hand, unstructured data is not restricted to certain formatting, is highly unorganized and is much more difficult to analyze, update and manage. According to Techjury, 95% of businesses find that managing this type of old or new data is a huge problem to analyze specially since it is data from multiple sources to have clear business insights from such complex data.
Now, with the knowledge of the types of data, there are several different types of databases and knowing them before choosing between data lakes and warehouses is very important. Although all of them have the same basic functionality to store information, each type has unique characteristics that differentiate themselves for different use cases. Let’s quickly go over some of the most common warehouse software to have a competitive advantage concerning the amount of data cleansing and ingestion necessary for your company to be fully-managed.
These databases are the most common and have been used for over 5 decades now. Relational databases refer to the organization of information within the tables. Data is stored in multiple, correlated tables in rows and columns. Relational databases use SQL (Structured Query Language) as the most common language to create, update and manage the data. These databases are very reliable and work well with structured data to provide actionable goals with its real time data.
As the name suggests, these databases run in the cloud offering scalability, usability, and flexibility. They are often subscription-based and don’t require maintenance.
Finally, with all this prerequisite knowledge, let's look at data lakes and warehouses to see which one is better for your business.
What is a Data Warehouse?
Data warehouses are large storage repositories for structured, formatted data that has already been processed for a specific purpose. With its highly structured composition, data warehouses are limited to certain data analyses that can be completed.
Traditionally, large businesses used data warehouses to share, edit and transform data across multiple divisions. Data warehouses are very efficient and can be used to guide data-driven decisions. Additionally, companies are using them to create business intelligence (BI) from the data analytics and insights that are provided.
What is a Data Lake?
On the other hand, data lakes fill the void in which data warehouses fail. Similar to warehouses, data lakes store large repositories of data. Unlike data warehouses, data lakes are very flexible and can perform many different analyses which can then be used for BI. Moreover, data lakes aren’t pre-conditioned to fit a specific purpose. Commonly, data lakes are used by data scientists and engineers and the insights found are then used by companies to make future-looking decisions.
Comparing the Two
In a data warehouse, data is transformed and organized as it’s extracted from the point of origin and stored according to the structure defined in the data warehouse. In a data lake, the data is transmitted and stored in its raw form so that it can be used when needed. For this reason, a data lake can contain all types of data, is less costly and has a quicker processing time.
In most business intelligence strategies today, a data warehouse is used to store data and deliver dashboards or data visualizations (graphs, charts, geographic coordinates, etc.). However, the agile approach is to draw from the data lake for composition with other data and deeper analysis.
Let’s take a look at each element of the two types data storage methods and compare them:
As we explored earlier, data lakes and warehouses differentiate themselves between structured vs unstructured data. With data lakes, data is often unstructured as data is coming directly from the source without being filtered. For warehouses, the opposite is true. They have structured data that is already filtered and organized, ready to be used in a relational database. Also, since data lakes store unstructured data, it is often larger and requires larger capacity. For this reason, there much be appropriate data governance practices in place when utilizing a data lake.
Looking at cost, the premise of big data is to store it efficiently and effectively. That’s why storing data with a data lake is often less expensive as it doesn’t require data to be organized and fit a specific schema. However, depending on the capacity of storage needed, and on location, you may be able to find or purchase better data warehouses to store large amounts of data rather than data lakes and databases.
With the structured nature of data warehouses, the ability for them to be agile and analyze all sorts of data can be challenging. This means that for companies and organizations, data warehouses should be used for pre-defined scenarios rather than evolving requirements. Contrarily, data lakes can do the opposite. Its structureless composition allows for it to scale and offer near-real-time insights as well, however with such composition, only trained data scientists usually work with them rather than other employees.
With both warehouses and lakes, security is of upmost importance. Companies often store sensitive data in warehouses and need it to be secure. As warehouses have been around for decades, they are more developed and have stronger security protocols. By comparison, data lakes are a newer way to store data and security measures are up and coming in the market. When it comes to data security, you will want to evaluate providers on their ability to comply with certain security and data privacy standards such as the European GDPR, etc.
Finally, looking at the potential users for either storage application, data warehouses and lakes are developed with different users in mind. As aforementioned, with the organized, rigid structure of data warehouses, they can be easily used by businesses and employees. Historically, data lakes with their flexible structure were intended to be operated by data scientists in order to get the most out of them. Now tools are being developed to give data lakes interactive, easy-to-use, no-code interfaces that make use of the data and provide insights that business leaders are looking for.
Regardless of whether you use data lakes or data warehouses to store data, the use of data itself has come a long way. Both solutions offer unique attributes that fit different business values and appeal to different end users.
The "data lake vs. data warehouse" conversation is just beginning, but each data storage method is unique due to major differences in data structure, cost, end-users, and overall flexibility. Putting in place the right data lake or data warehouse, depending on your company's needs, can help you grow.
To learn more about how to use a data lake to unify your HR and business data today, discover our PeopleSpheres platform solution here.