A Full Introduction to Data Lake – What Is a Data Lake?

What Is a Data Lake?

What is a data lake? Data lake is used to be a centralized storage repository where you are allowed to store a vast amount of structured, semi-structured, and unstructured data in its native format until it is needed for analytics applications.

Different from a traditional data warehouse, a data lake stores data primarily in files or object storage by using a flat architecture. A data lake is a single store of data including raw copies of source system data, sensor data, social data etc.

Related post: What Is ETL (Extract, Transform, Load) – Definition & Guide

So, what does the data lake be used for? There are four major uses for a data lake.

• You can import any data collected from multiple sources in real time and scale to data of any size.
• You are allowed to catalog data and the data can be better secured.
• You can use different analytic tools and frameworks to access data.
• You can generate different types of insights on data and operate machine learning.

Since data lakes, storing data, can provide users with the features of analytics and artificial intelligence, they can be used in many situations.

For example, Data lakes can be used for businesses to improve their recommendation system that can customize the recommended contents for specific users. Besides, data lakes can be applied in machine learning so that risks can be managed when real-time market data is made accessible.

What Is Security Storage and How to Protect Your Data Storage

What is security storage? How to protect your data storage? What is the best data security practice? You can find answers in this post.

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Since a data lake play an important role in storing data, how do we protect it from loss? Data lakes have their functionality in data security but that is not enough. When it referring to data protection from loss, people prefer to choosing a trustworthy backup tool.

MiniTool ShadowMaker is free backup software, winning a wide range of appraisals. You can use it to back up your important data stored in the data lake and backup schemes and schedules are also available to improve the backup experience.

This tool has a 30-day free trial chance and you can download and install it from the following button.

MiniTool ShadowMaker TrialClick to Download100%Clean & Safe

So, do you need a data lake? The next part will give you some introduction to its challenges and benefits.

Benefits & Challenges of Data Lake

Even though data lakes have many benefits that worth trying, that does not suitable for everyone. The data storage repository also faces some challenges in its development.

The following are the benefits that data lakes boast:

1. Reduce the cost of ownership.
2. Speed up analytics.
3. Enhance the data security.
4. Facilitate artificial intelligence and machine learning.
5. Streamline and simplify data management.

Most businesses will choose to apply data lakes in their data operations. With the help of a data lake, businesses can improve the customer interactions and promote the R&D innovation choices; the operational efficiencies will be improved.

However, there is one big flaw for a data lake. In a data lake architecture, raw data is stored with no oversight of the contents. If you want to make the data in data lakes usable, you need to have defined mechanisms to catalog and secure data or the data cannot be found.

Related post: 4 Best Ways to Store Data for Decades or Longer: Try Them Now

Data Lake Vs Data Warehouse

Many people will confuse the differences between a data lake and data warehouse because they have similar roles in data management.

However, they have two significant distinctions that is the support for data types and their approach to schema.

Compared to data lakes, data warehouse is primarily used to store structured data. It can house different types of data without the demands for a defined schema or a specific plan.

When referring to storing data, data lakes perform it with undefined reason while data warehouse is pre-defined. Processed data in warehouse is ready to be queried but in data lake, data is left raw until it is needed.

The both have different service customer – data lake is mainly used by data scientists while data warehouse by business professionals.

Bottom Line:

Now, after reading this article, you may have your understanding of the data lake. If you need any other help, you can leave your messages below and we will try our best to answer you.