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What is Data Lake: Intro, Pros & Cons, Data Lake vs Data Warehouse

What is Data Lake: Intro, Pros & Cons, Data Lake vs Data Warehouse

A data lake acts as a centralized reserve or repository that enables users to store unstructured, raw, and structured data. Compared to other methods of data storage, such as data warehousing, having a data lake allows for the storage of data that is greater in diversity and sources. This also makes data lakes more flexible with the capability to offer businesses a boost in their data analytics. 

There are multiple types of data lakes built on various technologies. For example, data lakes are popularly built using Hadoop because the software is open-source and offers a distributed filing system called Hadoop Distributed File System. This alone gives the software an edge over others, especially in terms of cost and scalability. 

There are other ways of using data lakes besides Hadoop. Microsoft Azure is a cloud based computing platform that enables users to build data lakes and offers features like Azure HDInsight that incorporates the framework of Hadoop and other similar tools into the Azure software. 

What Makes Data Lake so Popular?

According to research, the data lake market is set to have an expansive growth throughout the decade. It has already established its presence in various sectors, such as I.T, healthcare, manufacturing, entertainment, and more. 

The major selling point of a data lake is that it opens up a way of storing all forms of data into a single storage unit which can also be scaled to a massive extent. In a way, data lakes make it simple for businesses to store their data without worrying about whether the data is coming from a mobile phone or a professional camera. 

Data lakes also support real-time data analysis, which is another important factor for many businesses nowadays. Combined with an open-source platform such as Hadoop, data lakes become a very attractive solution in terms of performance and cost. Many businesses have even reported to have saved around 50% by opting for data lakes instead of other methods.

Data Lake vs Data Warehouse Comparison

While a data lake and data warehouse both are used for storing large volumes of data for analysis and processing, they have some fundamental differences in their design. 

A data warehouse usually stores structured data only, which will be in a fixed format. This also leads to data warehousing methods involving the use of predefined schemas that are focused and suitable for specific business projects or queries. 

On the other hand, a data lake can work with multiple forms of data and store them in their native format. As a result, a data lake has a much more flexible schema. 

Because data ingestion in data warehousing takes time, it is also better refined than the data in data lakes. This can lead to faster query response times when using data warehouses. Another factor that helps data warehouses is that all data is structured. 

Data lake has the advantage of giving businesses instant access to a broader spectrum of data which is not possible in data warehouses. 

Who are the key solution providers for data lake technology in the market?

Due to the explosive growth and potential of data lakes as a storage technology, many firms have created platforms to build data lakes. 

1. Microsoft Azure – Azure from Microsoft is a popular data lake solutions platform and software. It has immense scalability potential along with highly useful features, as mentioned above. 

2. Databricks – The interesting thing about Databricks is that it combines many factors of data lakes and data warehousing methods to provide unique solutions to users. 

3. Amazon Web Service (AWS) – AWS Cloud is a service that provides businesses with many tools and features to build data lakes. It also has a web-based UI design for users to have better access to their data.

If you’d like a personalized approach to your data related solutions, you can also consider VisioSoft, which can utilize various technologies and platforms, such as the ones mentioned above, to provide the best solutions. 

Data Lake: Advantages and Disadvantages

The major advantages of data lakes are:

1. Scalability: Data lakes can scale to store massive amounts of data in a way that allows for flexible usage and analysis. 

2. Reduced Dependency on Data Silos: Due to the centralized nature of data lakes, there is a natural decline in the requirement of data silos, which results in various benefits. 

3. Quick Processing: Data in data lakes is dynamic and real-time. This makes data extraction and exploration easier and quicker for businesses. 

The major disadvantages of data lakes are:

1. Lack of Consistency: Since data in data lakes is unstructured with flexible schema, there is a lack of consistency and pattern as compared to data warehouse. 

2. Security Concerns: It can be twice as difficult to maintain security measures over a data lake compared to other types of data storage methods because of the larger size of data lakes. 

The Future of Data Lake

As an increasing number of businesses are recognizing the importance of data analytics and business intelligence, the growth of data lakes looks promising, especially with the introduction of Lakehouse. Data lakes can help organizations consolidate data from various sources, internal and external data, and data from various systems, such as CRMs, ERPs, and other business applications. 

All of the data can then be analyzed in real-time. Again, data lakes can become highly complex as more data is ingested in the system. That’s where your business can work with a company like VisioSoft to maintain smooth operations over your data lake and receive valuable insight in a simplified manner consistently. 

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