Implementing a Data Lake or Data Warehouse Architecture for Business Intelligence 2024?

Data Lake Architecture

The business intelligence we understand as it is today only exists with a data warehouse. Due to the rise of Big Data, there is requirement of new ways for managing data. The BI approaches used traditionally for Enterprise Data Warehouse building and data marts aren’t capable of keeping up with data demands. This has led to the development of BDL (Business Data Lake) as a data repository model. It can, at a minimal price, accommodate the handling and storage of enormous amounts of data. Which is in its raw format of structured and semi-structured data.

This technique allows for displaying an overview of business operations and enables the capability to conduct ‘line of business analyses specific to a specific company. Data Lake storage permits data to be kept in its original, unmodified form without reorganization, dashboard, or visual adjustments to accommodate extensive analytics, data processing, or machine learning. The metadata is only kept to trace historical data and evaluate it to allow for further adjustments.

In this blog, you will learn about Business Intelligence and then contrast Data warehouse and Data Lake design for Business intelligence.

What Is Business Intelligence?

Business intelligence (BI) is genuinely about using information collected from the past and present to make better decisions for the future. It is the job that ensures that the raw data gets transform into information that offers insights and allows the making of decisions. Consider what businesses have been striving to accomplish for an extended period, and one would assume they would invest in tools and technology designed to address issues through analysis and data collection. BI, however, is not just about technology or tools. BI aims to use technology, data, and other tools to generate business information.

In a nutshell, BI includes gathering functional business requirements and translating them into technical solutions by designing data models. Implement ETL technology to transform raw data from operational source systems into insights. Do this before moving it onto an analytics/destination database for storage. This database can visualize, for example, a real-time automatic dashboard. Enterprises use it to make informed choices using historical data instead of relying on their gut instinct.

Importance Of a Smart BI Architecture

It is only possible to conclude this trip by discussing the necessity of incorporating a savvy BI structure into your business. Although we have made it to the various components, it’s crucial to highlight the main advantages. A solid BI structure is a guideline for collecting, organizing, and effectively managing business data that can be transformed into information that aids in better decisions. We’ll look at a few points further.

Data That Is Used Correctly

Many organizations try to use the potential of data-driven systems but only sometimes succeed. This is because the information being gathered is in various types and formats, making it challenging to organize and manage. 95% of businesses consider the requirement to handle incomplete data an issue. However, a properly implemented BI framework will leave such matters behind. Since it has a structured way to manage the information.

Remove The Load From The IT Department

Since the beginning, the IT department has various analysis tasks. These tasks comprise generating reports on performance using data that aids employees and managers in making strategic decisions. As markets become ever more competitive, the daily requirement to analyze data has led to IT staff being overwhelmed and lacking enough time to handle the need. A sophisticated BI design system can significantly ease the IT department from creating tedious reports and give them time to concentrate on more important matters like cybersecurity and properly functioning the organization’s systems.

Improved Effectiveness

In addition to the information above, Implementing the correct BI structure in your company can free the IT department of tedious report-writing tasks and improve your company’s overall efficiency. The BI platform will allow employees to automate their reports quickly and access real-time information anytime. It will enable employees to make data part of their business strategy rather than waiting several days or hours for the data to arrive as an unstructured report.

Make Your Money

A negative aspect of the concept of a BI framework is the data scattered across various applications managed by different departments. It will likely result in no synergy among departments and other functions, a loss of effectiveness, and higher costs for the business. However, BI applications save organizations cash and time as they provide a centralized database of company information. The long-term goal is for each stakeholder in the organization to be able to communicate with others and create a unified environment all over the company.

Importance Of Data Lakes Data Warehouse For Business Intelligence

Business intelligence data lakes and warehouses are crucial in managing and storing data. Data warehouses are the backbone for structured data storage, allowing for effective data retrieval and the complex queries essential for a comprehensive business analysis. They use Online Analytical Processing (OLAP) to combine, summarize, and process data. This facilitates accurate analyses and informed decision-making across all levels of an organization.

In addition to Data Warehouse, data lakes are flexible and adaptable storage solutions for unstructured or raw data. They can manage huge volumes of data stored in various formats. They allow companies to keep all kinds of data, from social media interaction to sensor data, without the requirement for instant structuring. This flexibility is vital for analytics based on big data, in which a wide range of data sources need to be examined and analyzed.

With data lakes, data warehouses facilitate the three primary functions of business intelligence: data wrangling, storage, and analysis. Data wrangling, often assisted by extract, transform, and load (ETL) processes, assembles the raw data into practical formats. Data lakes provide the first storage facility for raw data, which is then cleaned and stored in storage facilities for structured data analysis. This structure allows businesses to deal with data promptly, starting from raw data and transforming it into actionable data.

Understanding Data Warehouse Architecture 

Business Intelligence is typically associated with data warehousing. When BI is the front end, and the system for data warehousing is the backend or infrastructure to achieve business intelligence. This way, we’ll discuss BI from a data warehouse perspective. The main goal of the BI application is to transform operational data into valuable data. The raw data comes from various databases created and optimized to allow applications to operate rather than for research or analysis. To access just one data field in one field, it’s necessary to have ten joins! Here’s the scenario. Central data storage, also known as the data warehouse, has been proposed.

The data warehouse solution was developed in the late 1980s and was designed to give data or insight. Data warehouses combine information from an enterprise’s databases across the various systems it derives from. The database is considered relational since we can join multiple tables with the joint field presented as a physical data model. The schema of the database defines relationships between tables. Common SQL database types include MySQL as well as PostgreSQL.

What’s More

The data sources for the data warehouse via the ETL procedure called Extract, Transform, and Load. Data warehouses follow the pattern of schema-on-write, with match structure to the expected questions. In other words, it collects the data from main applications with a specific design and structure. 

The Data Warehouse architecture is when we transfer data from database A into database B; we have to know the database’s structure B and the best way to modify the database’s data to accommodate the data format in B. For example, knowing naming conventions, the data nature of the fields in database B stores, and so on. The data warehouse engineer can use diverse architectures when building a data warehouse. The most common data warehouse designs depend on layer-based approaches. 

Landing Zone/Staging Area

The database can read batch data from the primary system. The purpose of the database is to retrieve information from primary systems or sources to decrease the load on operating systems. The staging area comprises tables that mirror the sources’ structure and includes every table and column from the sources and the principal key. The functional business rules are not usable in this area. However, specific hard-tech business regulations are in place, such as data type matching (string length or Unicode characters). These business rules are technical and don’t alter the significance of the data; they alter the method by which the data is arranged.

The Data Warehouse layer

The transformation process begins here by applying functional business rules. Functional business rules alter input data to meet business demands. The earlier business rules implement into the data warehouse structure, and greater dependencies with higher layers over the database.

Data Vault modeling

Dan Linstedlt created this method in the 1990s. It’s built upon three types of entities: The Hubs, The Links, and The Satellites. The Hubs constitute the principal foundations of The Data Vault Model, which provides the business keys that business users employ to identify businesses and their objects. Keys for business include customer identifiers, product identification numbers, and badge number of an employee, and others connect Hubs and record the connection between Hubs (business objects). 

Satellites are the most common place for storing metadata. Beyond that, Satellites store all aspects of a company object or relationship. They add contextual information about the business anytime. It inlcudes Hubs or Links—the context of the business changes with time. The purpose of having satellites is to keep track of these changes and historical information.

Understanding Data Lake Architecture

Since data stored in data warehouses must undergo a rigorous control process before being stored. Adding additional data elements involves changing the structure by implementing or restructuring organized storage to store information and an ETL for loading the data. This procedure can take time and effort when you have vast amounts of data to store. That is when a data lake idea enters the equation and is essential to extensive data management.

The concept of a Data lake first emerged in the early 2010s. In simpler terms, it is the idea that all of the company’s semi-structured, non-structured, and structured information can and should be kept in one data center. Apache Hadoop is an example of an adequate data infrastructure capable of managing and storing vast quantities of structured and unstructured information, providing the basis for efficient Data Lake architectures.

What’s More

The data lake uses a schema-on-read approach. The data lake stores raw data and configures a degree that doesn’t initially require the definition of data schema and structure. Transferring data into the lake of data adds it to the lake without gatekeeping requirements. Whenever you need to open it, we apply the rules to the program that reads the data rather than defining the data schema in advance. Instead of the standard Extract of, Transform, and Load commonly used within data warehousing, the data lake world uses this process: Extract, Load, and Transform. Use data Lake for cost efficiency as well as exploration purposes.

Therefore, the Data Lake Data architecture enables businesses to get insights through the managed and processed data and the raw data that wasn’t readily available to analyze. Exploring raw data could cause business-related questions. However, the main issue regarding big data lakes is that without proper governance, the data lakes could quickly become unmanageable data swamps.

Does Data Lake Replace Data Warehouse To Support BI 2024?

Although Data Lakes offer multiple advantages over Data Warehouses regarding massive information collection and cost efficiency. They could be more reliable without confirming their integrity. Aggregating raw data from multiple sources, tidying it, and ensuring that it is of good quality before using it in business models takes about 80% of the work and time that data experts often ignore.

It is difficult to overlook the advantages of well-organized and high-quality data available to the structure and management of Data Warehouses. Data Warehouse. Companies still require a fundamental collection of KPIs that define the state of their business. The reporting process, particularly that of a regulatory type, requires high data control.

Businesses must be crystal-certain of the goals they want to accomplish using technology and data in their overall business plan. For example, an organization that doesn’t want to be involved in data science may gain little data science. Also analytics software designed to expand ML or AI technology. Organizations that organize and manage their data using a glossary arrangement aren’t likely to get the most value by throwing all their available information into a Data Lake.


Implementing a data lake or warehouse is more essential than ever to harness the power of business intelligence. Since data is growing in size, variety, and speed, companies must adopt reliable data storage technology. Helping with thorough analysis and informed decisions. Data lakes can deal with raw, unstructured data and enable the most innovative analytics using big data and data warehouses that provide well-structured environments that facilitate efficient data querying and report-writing.

By strategically integrating the data lake and warehouse, companies can ensure they have the right tools to handle various data sources. Hence simplifying ETL processes, and gain helpful insights. This double approach improves operations efficiency and encourages a data-driven mindset that allows businesses to remain ahead of the curve. In the coming years, as we enter our digital world, synergies between data lakes and data warehouses will be crucial. They will help in unlocking the potential of business intelligence and creating long-term increases.

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