Articles

Data Warehouses, Data Lakes and Smart Data Lakes Explained

by CloudFountain Inc Custom App Development Company
Data is gold. Businesses in the digital world are hungry for data, and why not? The secret to business success and growth lies here!

Many data verticals fall under big data consulting services, such as building data warehouses, big data analytics, data visualization, big data strategy, and whatnot. If you are new to these terminologies, it can be pretty overwhelming but fret not. The article covers three important alternatives for data repositories - Data warehouses, Data lakes, and Smart data lakes.

Without any further ado, let’s start with a simple table to help you get an overview before jumping into details.

 

Data Warehouse

Data Lake

Smart Data Lake

Data structure

Processed/Refined

Raw

Raw but semantic graphs show contextual links between data sets.

Status of data

In use

Not in use

Not in use

Accessible to

Managers or Business owners

Data Scientists, analysts, and architects.

Users, business owners, data scientists, analysts, and architects.

Flexibility & accessibility

Difficult and expensive to amend structured data

Can be easily retrieved and made changes as required

Highly flexible. Contextual linking with semantic graph models allows faster and easier data consumption from repositories.


Data Warehouse

The storage of raw, semi-structured, and structured data from various sources is made possible with data warehouses. These sources are diverse, from spreadsheets to conversational data with customers. Data stored in a warehouse can be historical or current data from which you may attain insights for better decision-making. A data warehouse aims to determine the link between the data ingested from different sources.

Now, this sounds similar to a database, but there is a difference between the two. The database records small data transactions, such as user registration or so. On the other hand, data warehouses are used for more extensive operations such as analytics or data mining.

Ideally, data warehouses are used in healthcare, finance, and retail chain businesses to predict health, create reports, analyze market trends, track inventories, and more. It's imperative to seek consultation if you are looking to build your data warehouse. A big data solutions company can help you determine a complete roadmap for implementing it in your enterprise.

Data Lakes

With IoT came more data. Further, IIoT is and will increase this data multiple folds. In such scenarios where the influx of information is abundant, big data analytics is inevitable to make it more usable and actionable. 

To enable intelligent data analytics and to break the silos of data within an organization, the CTO of Pentaho, James Dixon, put forth Data Lakes. He believes it is a better alternative to data warehouses and marts. 

Simply put, a data lake is a storage repository holding large volumes of data in its original, unrefined, or raw form. It follows a bottom-up approach of ingestion to fill a data lake. Just like a natural lake that gets water from rivers and streams, a data lake also receives a variety of data in different forms (structured, unstructured, or semi-structured) from various sources (databases, social media, emails, websites, IoT, XML).

By combining these diverse data sets, data scientists get analyzed data outflow that helps them retrieve patterns for actionable business insights.

Smart Data Lakes

A smart data lake, also known as a semantic data lake, endures greater flexibility for usage by different users and a semantic graph model format to store data across the data lake. It allows enhanced consumption of all types of data for data analysts, scientists, or any other user for quick detailed insights. 

Unlike conventional data lakes, the data is not left untransformed. The semantic graph model contextually connects data so that you can use it for analytics instantly. Users can access valuable insights from data faster with connected graphs of data. 

Which to choose for your business?

Each of the three is a viable choice. However, many enterprises take a more thoughtful approach by deploying a hybrid model to mitigate their BI needs. To determine the suitable model for your business, you can either consult a big data analytics consulting services provider or determine your types of data sets, the scope of your data, your data science requirements, compliance, cloud needs, and more. 

Finally, your data is your essential asset. To make the most of it, you need to invest your time in researching the best analytical solution. Good luck!

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Created on Oct 10th 2022 07:58. Viewed 314 times.

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