Real-Time Point-of-Sale Analysis with a Data Lakehouse

 Before coming to the Data Lakehouse, let us try to understand, in short, how the use of Data Warehouse is now no more fulfilling the needs of business intelligence (BI) activities. However, the data warehouse was purposely built for BI and reporting; they can only hold structured data, which is not enough to the current variety demands of the business environment.

A data lake was used for this very purpose of storing volumes of unstructured and semi-structured data in any format, including images and video. But as much as it was easy to store data in a data lake, it was not easy to use it. Hence, both of these systems were causing disruptions in the supply chain.

 With ever-increasing customer expectations, it was difficult for businesses to keep track of their inventories in real-time, especially during the pandemic. With an increasing consumer base and high user expectations, a more straightforward method to store and access data and perform BI activities efficiently to meet consumer demands is the need of the hour. For that, Data Lakehouse is an efficient substitute that can store your data (including every type of data- the data warehouse and data lake) in one place to reduce costs and easy access to real-time information.

Point-of-Sale System

A POS System Software has evolved extremely fast to manage the customer and retailer relationship in the best possible way. It has always been an indispensable part of any business organisation. The traditional use of processing billing receipts and storing data on a local server has advanced exponentially to fulfil a dynamic range of business needs. A Point of Sale software can seamlessly handle Omni channel integrations and provide unified ecommerce platforms. In the present scenario, where accessing and analysing real-time information is an absolute requirement, businesses cannot thrive on the traditional Point of Sale system.

Data Warehouses

The data warehouse architecture seems stale for the current business requirements. Why? First of all, it doesn't provide insights into the information in real-time. When we look at the history of data warehousing, it used to help business leaders to analyse previously-stored data for Business Intelligence activities. Due to the limited connectivity of the store to the storage house, all the information and data related to the business and customers were fed after the stores were closed for the customers. This data could be used by the experts and the business leaders the next day. That means no real-time access.

Also, another problem of data warehouses was their incapability to store unstructured information like images, videos, and text. This was a big problem with increasing technological capabilities. People expect to use all sorts of data, including videos and photos. To meet these demands, Data lakes were introduced.

Data Lakes

The lakes were the second generation of a data analytics platform, efficient in storing all data types, but accessing the data in real-time was extremely difficult. Although they provide lower-cost solutions for data storage but data lakes are also difficult to set up and compute. So, to solve this problem, Business experts brought a two-tier system in use comprised of a data lake and data warehouse. That means storing the data first into lakes and then into the warehouses. This again results in delays to access data and creates several complexities. Also, keeping data at two different places separately is not real budget-friendly. 

To overcome this challenge, Data Lakehouse was introduced. It provides a combined solution for both the problems organisations face in the data lakes and warehouses. Data Lakehouse can easily store structured, unstructured, and semi-structured data. And it is also easy to track, access and analyse data from a Lakehouse. So, with their help, business leaders can access any information in real-time and take necessary steps quickly.

Let us take a look at the capabilities of Data Lakehouse-

Reliable and easy data management

The data lake house can store data efficiently like the lakes, and the data stored in a lake house can be easily accessed and managed. Data Lakehouse enables its users to manage tasks. However, it comes with particular challenges. While a data lake could efficiently store any information, many setbacks couldn't provide ease in transactions and other management functions. It's just that these challenges can be overcome in fewer steps by data engineers and analysts. 

Real-time Point-of-Sale Analysis

POS software is not limited to its traditional uses. Its scope has widened exponentially. Apart from billing, it can perform all tasks such as inventory management, devising loyalty programs, order placements, evaluating employee performances, and many more things that make it an invaluable centrepiece in any business. The most significant thing is that you get to experience near real-time into store activities. This allows you to take the necessary steps and actions in time. Because it handles multiple forms of data simultaneously, the process might get complicated. So, you need to understand your data transmission patterns to employ the best mode for data transmission. 

In Summary

Using data lake house patterns in your POS system can bring immense benefits to your business. But to fully use its capabilities, you need to understand its complexities and devise necessary strategies.