• November 17, 2021

Data Warehouse Architecture or Data Lake Architecture for Business Intelligence

Data Warehouse Architecture or Data Lake Architecture for Business Intelligence

Many people have some idea about Business Intelligence Architecture but are not very sure about the backbone of the implementation i.e. Data Warehouse or the Data lake requirements. This article discusses what Business Intelligence is and its advantages and compares DataWarehouse and Data Lake Architecture for Business Intelligence

What is Business Intelligence?

This term has many definitions and interpretations. We’ll pick one, Business Intelligence or BI using business analytics, data mining, data visualization, data tools, infrastructure, and best practices to help organizations make more data-driven decisions. Sounds complicated?

In easier terms, it is using the data generated yesterday for effective decision-making tomorrow. The process includes gathering all the technical requirements and using the data to solve technical problems. This is to help companies transition from the “gut” instinct to inculcate data-driven solutions.

Uses of BI

  1. Visualization

Spreadsheets might be intuitive and easy to use, but when it comes to user experience, let’s be honest they are boring. So, Business Intelligence uses the principles of spreadsheets to manipulate the data but presents them in an easy-to-understand and easy to visualize format with the help of Dashboards, Charts, etc. For the user to view and understand the story presented in one glance.

  1. Descriptive Analytics 

Everyone knows that of the types of Data Analytics – Descriptive, Diagnostic, Predictive, and Prescriptive Analytics; Descriptive Analytics is the foundation and is helpful for organizations that are interested in analyzing their past data and are also the stepping stone for the other complicated analytics. As such, Business Intelligence forms the mechanism of Descriptive Analytics for organizations. With BI, users can have a single source of truth instead of going through multiple spreadsheets and reconciling their data.

  1. Diagnostic Analysis

We have just spoken about Descriptive Analytics, which can be seen with correlation or the charts that have been created in the dashboards. But with Business Intelligence Architecture, you can also drill down into the data to understand the “Why” behind the relationships. Again, BI cannot help you with predictive analytics or prescriptive analytics but a little forecasting can be done. In our opinion, it is more useful and accurate to use BI solutions for Descriptive and Diagnostic Analysis.

  1. Single Version of truth

One of the key requirements to perform good quality data analysis of any kind is, obviously, the Data. When you have all the data in a single place extracting information from it is easier. For example, answering questions about the number of customers can have multiple answers at various geographic locations and levels. For proper reconciliation knowing a single version of the truth is important.

Data Lake or Data Warehousing Architecture for BI 

Before we get into what is better or what is suitable for BI. Let’s understand the basic difference. Business Intelligence is the front end i.e. the area which is mostly concerned with the end-user. But the back-end is normally regarded as the Data Warehouse Architecture. With Raw Data scattered in multiple operational databases, a central storage unit like a Data Warehouse or a Data Lake makes sense for the use of Business Intelligence.

One of the key differences between the architecture of Data Lake and Data Warehousing is that unlike having to process data to fit a predefined schema of data warehouse, the data lake ingests raw data in its native format. Data Warehouse Architecture is commonly referred to as ETL and Data Lake Architecture is commonly referred to as ELT. The process can be visualized below: 

In other words, A data warehouse follows the schema-on-write pattern i.e. the design fits the answer expected. In a three-layered data warehouse, the first layer is a Staging layer whose purpose is to extract the source data from the source systems and reduce the workload from the operational systems. In the next layer of the Data warehouse, the technical rules are reinforced and functional business rules are applied. The Next layer above this is the Data Mart layer where fact and dimension entities are modeled and are presented to the end-user. 

One of the rigidity in Data Warehouse is that for adding new elements in it, you need to change the design, restructure or refactor the data stored, which would require a considerable amount of time and resources. To overcome this, we have Data Lake Architecture. In this, all the enterprise’s structured, unstructured, and semi-structured data can and should be stored in the same place.  

Unlike the Data Warehouse’s Schema-on-write pattern Data Lake has a Schema-on-read pattern that means it does not require defining the data structure and schema in the first place, which gives an advantage for data science projects and users to find insights into their data. Data Lakes are used for cost efficiency and exploration, making raw data available for processing. There would definitely be concerns with this kind of process as, without proper governance, data lakes can turn into data swamps. The governance process can be established by defining who owns the data, who defines it, who will be responsible for any data quality issues, etc.

Now that you know the working difference between Data Lake and Data Warehouse, you must have understood that Data lake provides an advantage of cost and efficiency while dealing with Big Data. But without the quality and structure of Data like that of a Data Warehouse, users don’t trust their data. So it all boils down to what the users want to achieve with their data. For example, if a company does not plan to be involved in Advanced Analytics, there is no point in investing in advanced Data Lake Solutions that allow AI and Machine Learning to happen. Or there are ways you can unify this Datawarehouse and Data Lake approach to provide the best of both worlds. 

This unified DW/DL can help organizations achieve a new and richer data architecture that can incorporate distinct but integrated, overlapping, and interoperable architectures which can help you get the advantage of both architectures while reducing the disadvantages of the two. 

In conclusion, both Data Warehouse Architecture and Data lakes can be useful depending on the strategy and the path the organization wants to walk on but wrt Business Intelligence organizations normally have a Data warehouse schema due to its structure but are beginning to implement Data Lakes as an additional layer to provide more flexibility.