The Difference between Database and Data Warehouse

Global Data 365
October 10, 2024

For corporations of all sizes and sectors, the world of Big Data keeps expanding. The performance and profitability of any business rely mainly on the volume, consistency, and reports of the information they gather. However, the real challenge lies in how companies analyze, gain input from, and take action on the collected data. Understanding the difference between database and data warehouse is crucial in this process, as each plays a distinct role. The ability to distinguish between database vs data warehouse can significantly impact how effectively raw data is transformed into valuable insights.

It requires organizations to learn the practice of corporate data management so that workers can effectively produce, archive, view, handle and interpret the data they need to succeed at their work. So, when it comes to gathering, storing, and analyzing data, what could prove to be the right decision for your company? The most common types of data storage in enterprise data management are databases and data warehouses. So what is the difference between a database and data warehouse, and which one is the right choice for your company?

What is a Database?

A database is a digital system used for storing, managing, and retrieving data in a structured format. It acts as a centralized repository where information can be organized into tables, making it easy to access and manipulate. Databases are essential in various applications, from small-scale software solutions to large enterprise systems, and they support a wide range of data types, including text, numbers, and multimedia.

In essence, databases form the backbone of modern information systems, supporting activities in sectors like finance, healthcare, retail, and more. They enable businesses to harness their data for decision-making, reporting, and operational efficiency, thereby driving growth and innovation.

What is a Data Warehouse?

A Data Warehouse can be defined as a system that collects and stores data from several diverse resources within an enterprise. In comparison to a database, a data warehouse’s infrastructure is designed to get the data out, and not just by technical tools, but for regular users like finance professionals, executives, management, and other workers.

The objective of a data warehouse is specifically business-oriented: it is intended to promote decision-making by enabling end-users to consolidate and interpret data from multiple sources. Being the basis for BI and analytics, it takes out information from existing databases, defines a series of rules to covert the data, and then transferring it into a single central repository to view and manage easily.

A data warehouse stores information of the transfer level and supports the larger reporting and analytical needs of an organization, providing one basis of reality for building semantic models or the provision of organized, simplified, and aligned data for tools, such as Excel, Power BI, or even SSRS. Companies that have a higher level of data or analytical needs tend to use a data warehouse.

Key Differences: Database vs Data Warehouse

With more volume and complexity of data used in the organizations, they want to receive more analytical insight, which is why data warehouses are receiving more visibility for database reporting and analytics. The key distinctions of database vs data warehouse is that databases contain accumulated data that are organized. Whereas data warehouses are data systems constructed from various information sources, as they are used to analyze information.

Data Type

Database: Contains detailed transactional data.
Data Warehouse: Stores summarized, aggregated, and historical data.

Purpose

Database: Used for real-time transaction processing, storing current data for operational tasks.
Data Warehouse: Optimized for large-scale analytical queries and historical data analysis.

Data Structure

Database: Optimized for read/write operations (OLTP – Online Transaction Processing).
Data Warehouse: Optimized for read-heavy operations (OLAP – Online Analytical Processing).

Users

Database: Primarily used by operational staff (e.g., clerks, IT staff).
Data Warehouse: Mainly used by business analysts, data scientists, and decision-makers.

Data Volume

Database: Holds less data, focused on current transactions.
Data Warehouse: Handles large volumes of historical data from multiple sources.

Performance

Database: High performance for inserting, updating, and deleting transactional records in real-time.
Data Warehouse: High performance for complex queries and large-scale data retrieval.

Schema Design

Database: Normalized schema (e.g., third normal form) to ensure data integrity.
Data Warehouse: Denormalized schema (e.g., star or snowflake schema) for faster query performance.

Data Processing

Database: Processes a large number of small transactions.
Data Warehouse: Processes complex queries requiring significant data aggregation.

Concurrency

Database: Supports multiple users. Data Warehouse: Supports a lower number of users.

Storage Cost

Database: Typically cheaper per unit of data.
Data Warehouse: Higher storage costs due to large datasets and complex processing.

Data Source

Database: Captures current data from operational systems like CRM, ERP, and other applications.
Data Warehouse: Aggregates data from multiple sources, including databases, external systems, and log files.

Example Use Cases

Database: E-commerce transactions, banking systems, inventory management.
Data Warehouse: Business intelligence reporting, trend analysis, forecasting.

Features	Database	Data Warehouse
Purpose	Used for real-time transaction processing, storing current data for operational tasks.	Optimized for large-scale analytical queries, storing historical data for reporting and analysis.
Data Type	Contains detailed, transactional data.	Stores summarized, aggregated, and historical data.
Data Structure	Optimized for read/write operations (OLTP – Online Transaction Processing).	Optimized for read-heavy operations (OLAP – Online Analytical Processing).
Users	Primarily used by operational staff (e.g., clerks, IT staff).	Mainly used by business analysts, data scientists, and decision-makers for insights and reporting.
Data Volume	Holds less data, focused on current transactions.	Handles large volumes of historical data from various sources.
Performance	High performance for inserting, updating, and deleting transactional records in real-time.	High performance for complex queries and large-scale data retrieval for analysis.
Schema Design	Normalized schema (e.g., third normal form) to eliminate redundancy and ensure data integrity	Denormalized schema (e.g., star or snowflake schema) for faster query performance
Data Processing	Processes a large number of small transactions.	Processes complex queries requiring significant data aggregation
Concurrency	Supports multiple users.	Supports a lower number of users.
Storage Cost	Typically cheaper per unit of data.	Higher storage costs due to large datasets and complex processing requirements.
Example Use Cases	E-commerce transactions, banking systems, inventory management.	Business intelligence reporting, trend analysis, forecasting, decision support.
Data Source	Captures current data from operational systems like CRM, ERP, and other applications.	Aggregates data from multiple sources, including databases, external systems, and log files.

Importance of Databases and Data Warehouses for Businesses

Companies can reap the benefits of both database and data warehouse for reporting and analysis in different ways. Let’s see why:

- Data Quality and Accuracy

Data warehouse includes transferring information from different sources, standardising it, naming it, arranging it, and making sure the uniform restrictions are sorted and labelled. This ensures better confidence in the information being displayed, minimizes organizational errors, and gives better possibilities for partnership as independent business sectors like sales, marketing, and finance all depend on similar reporting from the data repository.

- Power Business Intelligence

One of the greatest advantages of data warehousing is the rising scope and efficiency of data storage. By optimizing access to the data of your organization, you are strengthening the leadership’s willingness to adopt a smarter plan centred on a more complete and effective solution. Data warehouse-powered business intelligence provides deeper insight into sales operation, financial stability, and much more.

- Increased ROI

The use of data warehousing helps organizations to save money on their analytics, and as a result, a larger amount of profit is generated. As the expense of data warehousing reduces, this effect grows exponentially, and by using BI software and data warehousing in coordination to effectively democratize data and slash headcount in reporting and analytics operations, companies can generate a return on investment faster.

- Improved Efficiency

Data warehouses are designed for speed, in particular to providing large businesses quick access to retrieval and analysis of data. Instead of devoting useful numerical data, data warehouses are all about the ability to edit and maintain specific data records. By making sure that the data can be obtained, collated, and processed as easily as possible, the process of making important business decisions in an instant becomes easier.

Best Way to Build a Data Warehouse

It is popularly known that there are as many ways to create data warehouses as there are companies to develop them. Every data warehouse is special, as it adheres to the requirements of business users in numerous functional areas in which firms face diverse market environments and competitive forces.

- Creating the Staging Area

Before analysis of the data, it goes through the process of retrieval, conversion, and loading of data. As the warehouse is as strong as the data stored within it, for the success of your company it needs to match department requirements and objectives.

- Building an Environment

Usually, data warehouses have three main physical settings: development, testing, and manufacturing. And these three settings will exist on entirely different physical services.

- Data Modelling

Data Modelling is the process of visualizing the distribution of data in your data warehouse. Before constructing a data warehouse, it is important to know where and why data goes. This is why data modelling is used.

- Choosing Your Extract, Transfer, Load Solution

ETL Solution is the process you will use to extract data from your existing storage solution and place it in your warehouse. That is why it is pertinent to carefully choose the right ETL solution for your warehouse.

- Create Front-End

It is important to have front-end visualization, so users can instantly comprehend and utilize the results of data queries. BI tools like Power BI work best for visualization, and you can also customize your own solution.

- Queries Optimized

Having your queries optimized is a complicated process that answers your required needs. Make sure that your manufacturing, testing, and development setting have similar resources to prevent lagging.

In Conclusion

Database and data warehouse serve different functions in practice. If you are contemplating about building your own database vs data warehouse, then it is one indication that the organization is dedicated to the practice of effective corporate data management.

Every company has different needs to build a data warehouse and database, which is why Global Data 365 designed a reporting and BI solution that provides the user with a pre-built data warehouse and cubes set ready to be used. With a wide dashboard library and report templates, Jet Analytics is built to provide you useful insight day one into your results. In the years to come, the accuracy, durability, and usability of data will be the key differentiator for firms of all types. That is why organizations would want to make sure that they are placing themselves up for sustainable growth by selecting the best infrastructure and storage.

To know more about database vs data warehouse and how you can implement it in your business, contact us now.

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What are Data Lakes?

Global Data 365
May 21, 2021

The huge volume of data collected by today’s company has entailed a drastic change in how that data is stored. Data stores have expanded in size and complexity to keep up with the companies they represent, and data processing now needs to stay competitive, from simple databases to data warehouses to data lakes. As enterprise businesses collect vast amounts of data from every imaginable input through every conceivable business feature, what started as a data stream has developed into a data flow.

A new storage solution has emerged to resolve the influx of data and the demands of enterprise businesses to store, sort, and analyze the data with the data lake.

What are Data Lakes?

Data Lakes are type of centralized repository that stores all types of data—structured, semi-structured, and unstructured—in its raw format. Unlike data warehouses, which standardize data before processing, a data lake holds data without any transformation, allowing for future analysis and exploration. This raw data can later be structured for specific purposes, making it a powerful resource for businesses that deal with diverse data sources like IoT devices or event tracking.

What Does It Contain?

The foundation of enterprise businesses is a collection of tools and functions that provide useful data but seldom in a structured format. The company’s accounting department may use their chosen billing and invoicing software, but your warehouse uses a different inventory management system. Meanwhile, the marketing team is dependent on the most efficient marketing automation or CRM tools. These systems rarely interact directly with one another, and while they can be pieced together to respond to business processes or interfaces through integrations, the data generated has no standard performance.

Data warehouses are good at standardizing data from different sources so that it can be processed. In reality, by the time data is loaded into a data centre, a decision has already been taken about how the data will be used and how it will be processed. Data lakes, on the other hand, are a larger, more unmanageable system, holding all of the structured, semi-structured, and unstructured data that an enterprise company has access to in its raw format for further discovery and querying. All data sources in your company are pathways to your data lake, which will capture all of your data regardless of shape, purpose, scale, or speed. This is especially useful when capturing event tracking or IoT data, while data lakes can be used in a variety of scenarios.

Benefits of Data Lakes

Versatility: Data lakes store data in any form—whether it’s CRM data from marketing or raw transaction logs from inventory systems.
Flexibility: Since data is stored in its original format, it can be processed, transformed, and analyzed whenever needed.
Scalability: Data lakes, like Azure Data Lake, handle data of any volume, shape, or speed, making them ideal for large-scale enterprises.

Application of Data Lakes

Data lakes find applications across multiple industries, enabling:

Healthcare: Early disease detection and personalized treatments.
Finance: Fraud detection and market trend prediction.
Retail: Customer behavior analysis and inventory optimization.
Manufacturing: Predictive maintenance and production workflow enhancements.

Data Collection in Data Lakes

Companies can search and analyse information gathered in the lake, and also use it as a data source for their data warehouse, after the data has been collected.

Azure Data Lake, for instance, provides all of the features needed to allow developers, data scientists, and analysts to store data of any scale, shape, or speed, as well as perform all kinds of processes and analytics across platforms and languages. Azure Data Lake simplifies data management and governance by eliminating the complications of consuming and storing all of your data and making it easier to get up to speed with the queue, streaming, and interactive analytics. It also integrates with existing IT investments for identity, management, and security.

That being said, storage is just one aspect of a data lake; the ability to analyse structured, unstructured, relational, and non-relational data to find areas of potential or interest is another. The HDInsight analytics service or Azure’s analytics job service can be used to analyse data lake contents.

Data Collection and Analysis

Data lakes are especially useful in analytical environments when you don’t understand what you don’t know with unfiltered access to raw, pre-transformed data, machine learning algorithms, data scientists, and analysts can process petabytes of data for a variety of workloads like querying, ETL, analytics, machine learning, machine translation, image processing, and sentiment analysis. Additionally, businesses can use Azure’s built-in U-SQL library to write the code once and have it automatically executed in parallel for the scale they require, whether in.NET languages, R or Python.

Microsoft HDInsight

The open-source Hadoop platform continues to be one of the most common options for Big Data analysis. Open-source frameworks such as Hadoop, Spark, Hive, LLAP, Kafka, Storm, HBase, Microsoft ML Server, and more can be applied to your data lakes through pre-configured clusters tailored for various big data scenarios with the Microsoft HDInsight platform.

Learn More About Microsoft HDInsight

Future-Proof Data

For companies, data lakes reflect a new frontier. Incredible possibilities, perspectives, and optimizations can be uncovered by evaluating the entire amount of information available to an organization in its raw, unfiltered state without expectation. Businesses may be susceptible to data reliability (and organizational confidence in that data) and also protection, regulatory, and compliance risks if their data is ungoverned or uncatalogued. In the worst-case scenario, data lakes will have a large amount of data that is difficult to analyse meaningfully due to inaccurate metadata or cataloguing.

For companies to really profit from data lakes, they will need a clear internal governance framework in place, as well as a data catalogue (like Azure Data Catalogue). The labelling framework in a data catalogue aids in the unification of data by creating and implementing a shared language that includes data and data sets, glossaries, descriptions, reports, metrics, dashboards, algorithms, and models.

Built your BI Infrastructure

The data lake will remain a crystal-clear source of information for your company for several years if you set it up with additional tools that allow for better organization and analysis, such as Jet Analytics.

At Global Data 365, you can contact our team to find out more information on how to effectively organize your data or executing big data systems seamlessly.

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