Data Warehousing vs. Databases: Understanding the Differences

Storing large amounts of data means finding the best solution for your business.

If you and your company must process years of historical data or online transactions, set Data warehousing solutions Databases can serve you well. The thing is, they have very different purposes, but the terms are interchangeable. It’s crucial to figure out which situations require one and which require the other.

What is data warehousing?

A Data warehousing It is a centralized system that collects data from different sources for analysis. It uses online analytical processing (OLAP) to quickly evaluate large amounts of data, providing analysts with information they can use to formulate business decision-making strategies. Old data can be stored in a data warehouse for comparison to help inform these decisions.

What is a database?

Databases store real-time information about specific parts of a business, such as customer information, daily transactions, or health records.

Databases can fulfill requests to find information or provide reports that provide insights about that information, but they do not have the inherent analytical capabilities of a data warehouse. They also use online transaction processing (OLTP) instead of OLAP to process data quickly.

In-depth analysis using the database is not possible unless the data is extracted and imported into a separate analysis tool. Therefore, many enterprises use both database and data warehousing solutions to meet various aspects of their needs.

Data warehouse type and database type

three main types Data warehousing Can be used to store and analyze information.

• Enterprise Data Warehousing (EDW). These data warehouses are centralized systems that can provide analytical insights across a large number of connected warehouses or repositories. By connecting them into a warehouse, businesses can organize data more efficiently and gather insights across a range of data points.
• Operational Data Storage (ODS). Often used in conjunction with EDW, you can instantly refresh ODS and enter new data into EDW to make more accurate decisions. ODS is primarily used for reporting and controlling data from different sources.
• database. As a subset of the overall data warehousing, data marts are usually oriented to a certain component of the business, such as finance or sales.This ensures teams only see data relevant to their work, speeding up the analysis process

database There can be more variations. Some of the most common are:

• Layered. Data stored in hierarchies are classified according to different levels within the organizational system. Most materials operate as a parent-child relationship structure, with higher-level materials branching out into various sub-levels.
• network. Web databases can connect information to other data in a two-way manner. This contrasts with a layered repository that only moves in one direction.
• object-oriented. The data here are organized as independent entities with no specific relationship to other types of data in the database. These are often used to manage complex data structures that need to be manipulated before analysis.
• cloud. Rather than storing data on local servers or devices, data is stored in the cloud as a flexible way to organize and share data remotely. Any database stored in a cloud computing system falls into this category.
• NoSQL. These databases can contain structured and unstructured data and use different data models to store this information. NoSQL can use models such as key-value pairs or graphs, rather than the standard table structure with a basic column and row layout that a simple type of database might use. For example, a key-value pair is two related data elements that have a data set definition (such as gender, color, price) and an added value (such as male/female, green/blue, 100/1000).

Best practices for data warehousing

When businesses must deal with databases that cannot meet their analysis needs, they can pull the data into a data warehouse to gain a deeper understanding of the information stored. If you’re considering making the switch, consider these best practices.

Determine the analytics needs of your business

Businesses that know exactly how they want to use their data are often the most successful. By investing time and money in finding the data analytics solution that best suits your goals, you can reduce costs and improve decision-making throughout your organization.

When deciding on a good solution, it’s a good idea to look at the type of material you already have and what you might need it for in the future. If your business only needs to store data and record daily tasks, such as transactions, customer orders, inventory, or accounts, then a database will do the trick.

But if you need to look at long-term historical data and analyze different data (instead of simply storing it), then data warehousing is a better choice. Data warehousing provides critical insights that can lead to competitive advantage and lower operating costs.

Determine how often to import data

Standardizing data before importing is essential for correct warehouse organization and operations. It also helps reduce the risk of errors when analyzing data.

From there, you must make decisions about batches and methods of collecting data for analysis. Using Change Data Capture (CDC) improves the accuracy of Loop Nast by helping to collect information from the database in real-time.

Define permissions and access

Because data warehousing information is collected from multiple sources, you must understand security measures. Some of the data you have may contain sensitive customer or business information, or be subject to compliance requirements in your industry. Regularly evaluate who has access to the data repository and their permissions.

Consider cloud-based options

Cloud-based data warehousing can provide greater flexibility for teams working remotely. They can also be useful backup locations for data that you access infrequently, especially if you have limited on-site data storage.

Top data warehousing solutions

Data warehousing is designed to process and evaluate data in a way that helps teams make better business decisions. They are the centralized hub for all company information.

To be included in the data warehousing category, a platform must:

• Contains data from multiple or all branches of a company
• Consolidate data before transferring it to the data warehouse through extract, transform, and load (ETL) processes
• Allows users to perform queries and analyze stored data
• Provides multiple deployment options
• Integrate with third-party reporting and business intelligence tools
• As an archive of historical information

*The following are the top five leading data warehousing software solutions from G2’s 2024 Winter Grid Report. Some comments may be edited for clarity.

1. Amazon Redshift

Amazon Redshift Provides scalable data warehousing solutions that make analyzing business data simple and cost-effective. The tool is optimized for data sets ranging from hundreds of gigabytes to petabytes or more.

What users like most:

“Creating and managing tables on Amazon Redshift is very easy. The SQL syntax is simple and well-documented, and it really helps me query and aggregate data. Supports multiple data types, even geometric data types for geographic applications.”

– Amazon Redshift ReviewFilippo C.

What users don’t like:

“The least useful part of Amazon Redshift is that it doesn’t fully support data types like JSON and XML, as working with data in unsupported formats can lead to all kinds of tedious tasks.”

– Amazon Redshift ReviewRaghavendra L.

2. Google Cloud BigQuery

and Google Cloud BigQuery, enterprises can simplify data analysis using multi-cloud data warehousing integrated across the entire organization. This tool is a serverless repository that can handle all types of data with ease.

What users like most:

“Google BigQuery is an efficient and easy-to-use data analysis service on Google Cloud Platform. The online query editor interface is well organized, can execute complex queries smoothly, and allows me to handle large data sets and ETL steps. BigQuery is scalable , seamlessly integrates with other Google Cloud data services and third-party analytics solutions.”

– Google BigQuery ReviewHosam K.

What users don’t like:

“There is no option to pause the cloud editor environment. So one has no choice but to delete it because if you don’t delete it and you want to pause it for a period of time, you will still be charged. And you will be lost in the environment All configurations and settings are made, and new configurations and settings must be set.”

– Google BigQuery ReviewMuskan C.

3.Snowflake

snowflake It is a cloud-based data warehousing tool that integrates siled data to discover and share information within the company. The platform provides access to the data cloud to create solutions for data warehousing, data lakes, data engineering, data science, data application development and data sharing.

What users like most:

“Snowflake really excels when it comes to handling data of all shapes and sizes. Snowflake is designed to be used by people of all backgrounds, whether you’re a tech savvy or new to the world of data lakes. The SQL-based approach makes management and Querying your data lake is a breeze. Snowflake’s cloud-native setup keeps everything running smoothly.”

– snowflake reviewSean R.

What users don’t like:

“Many open source tools don’t work with Snowflake, which can create system integration challenges.”

– snowflake reviewAnkit G.

4. Data block

data block Smart platforms use artificial intelligence to unify data within the warehouse and provide analytical insights into the stored information. The tool combines generative artificial intelligence with Lakehouse’s unified strengths, powering an intelligence engine to understand the unique value of each company’s data.

What users like most:

“It provides Atomicity, Consistency, Isolation, and Durability (ACID) transactions, which is a huge support for data consistency. It is very convenient to take advantage of features such as time travel and schema evolution when building scalable solutions. In addition , it also reduces data storage costs without compromising powerful decentralized programming.”

– Data block commentsPranshu G.

What users don’t like:

“If your data lake is unevenly distributed, data browsers can be very slow and cumbersome. Cold-starting a cluster can take a very long time, at least with the way our cluster is set up.”

– Data block commentsMatthew V.

5.Teradata advantages

Teradata Vantage It is a powerful cloud analysis and data platform that uses artificial intelligence to integrate and analyze business data. The tool delivers harmonized data, trusted artificial intelligence, and faster innovation so users can make better, more confident decisions.

What users like most:

“Teradata is a mature data analytics platform. We use it very extensively, so we leverage its ability to execute complex workloads. We like the detailed logging so we can fine-tune our workloads.”

– Teradata Vantage ReviewsRichard S.

What users don’t like:

“Given the extensive functionality and breadth of features offered by Vantage, there is a learning curve to mastering the platform. It may take some time and training for users to effectively take full advantage of its capabilities.”

– Teradata Vantage ReviewsShubham D.

Transform your data

There is no single way to store and analyze data, so it’s not a question of which is better, a database or a data warehouse. It all depends on your business needs. Both are powerful tools, and they’re even better when you combine their powers to help you make smarter decisions for your company.

Learn more about your data Data visualization tools Convert numbers and metrics into charts and graphs.