6 Alternatives to Data Warehouses

Data Warehouses

Data warehouses provide businesses with big data storage solutions. Data stored in a data warehouse is mostly used for analysis and reporting. All data gathered from various sources in a business is stored in a data warehouse for ease of accessibility.
It stores both current and historical data that can be used in the future for reporting or forecasting. It uses extract, transform, and load processes. There are many different alternatives a business can choose in place of a data warehouse.

Operational Data Store

An operational data store is an alternative to a data warehouse that helps with eliminating the problem where the latter doesn’t contain up-to-date data. An ODS acts like a central database where organizations can get current data pooled from multiple sources.
An ODS system has an advantage to businesses because it stores data in its original file type regardless of the thousands of files stored in it. It is easier for businesses to make queries from data stores in an ODS system. Data in the ODS system is updated regularly, either daily or hourly, making business operational data available in a single destination. This is why data in an ODS system is easy to use for business reporting.

Data Mart

A data warehouse is a large type of storage but if you want to deal only with a small segment of information for keeping your business alive, a data mart will be good enough. It is a smaller type of storage that can serve a small business or a specific department within a large business. Due to its small size, it can only draw data from limited sources as much as it can hold. Departments such as sales, human resources, marketing, or finance will feel comfortable installing a data mart to serve its needs.
Due to a smaller volume of data held in a data mart, it is easier to query and save time. Users can access the data as frequently as they want. It is cheaper to implement a data mart, plus it is an agile system that is easy to build or change. Data in the system is easy to segment, partition, and is open to change. Each department might decide to implement its data mart but if the business is small, one data mart can serve it smoothly. Compared to a data warehouse, it is a less complex system that any authorized person can handle with little help from expert programmers.

Data Lake

A data lake is created for businesses that handle larger volumes of data that go beyond a data warehouse. It stores data as-is and doesn’t require pre-structuring before storage. Large organizations take advantage of a data lake because it can store data of any size. Corporates use data stored in data lakes for various types of analytics like dashboards, big data analytics, visualizations, or machine learning to help them make better decisions. This type of data gives companies an advantage because it is huge, meaning it is drawn from many sources for providing deeper analytics.
Businesses can generate greater value from the data and have an edge against competitors. Due to its ability to store data from a large variety of IoT devices, it is easy to take note of opportunities and act upon them fast to pull more customers, increase production, make better marketing decisions, and build better systems.


Hadoop is a newer technology used for storing data in clusters and for running applications. It has a powerful processing power that can process unlimited concurrent tasks. Once a business stores data and programs it to a Hadoop cluster, it is stored in HDFS. What processes data in Hadoop is the MapReduce, then the YARN divides its tasks. Hadoop is a flexible system that doesn’t require data reprocessing before storage. You can store both structured and unstructured data then decide the way you want to use it later. It is an open-source platform that is cheaper to install and maintain.
Due to its scalability, it’s easy to grow it as data produced by a business increases. Because it is a distributed system, it offers businesses high computing power with fast processing speeds.

Random Access Memory

For many years, random access memory has been used as temporary storage that loses data once a computer is switched off. Lately, technology has made it possible to convert RAM into a storage device. Unlike hard disk storage, RAM can process data at a speed of 50 times more. You require software to create a virtual RAM drive for storage. To achieve more storage, companies need to connect multiple RAMs in parallel to help them store large volumes of data that can be processed at a great speed.
The technology used for storing data in RAM is called in-memory computing. It is the best way to eliminate input/output requirements because data stored in RAM is available for use instantaneously. Due to RAM speed, businesses can process large volumes of data fast and predict current patterns in the market.

Cloud storage

Cloud is a computer model for storing data on the internet. A company pays a cloud provider to store data on their behalf. It is a type of on-demand service that a business can pay as they use or make a payment to store a certain amount of data at a certain period.
Some companies that provide cloud storage offer free storage for a limited amount of data. Companies that want to store large volumes of data must subscribe to get more space. It is a better alternative to buying specific storage infrastructure to install in the office or remotely. The greatest advantage of cloud storage is that it has a global scale that allows companies to access their data anytime, anywhere. It has a great degree of agility, providing durability to data.
A business must purchase cloud storage from a vendor. This is the entity that owns the cloud, manages it, and provides it as a pay-as-you-go model. The vendors invest in the system because of security, capacity, and data durability. The biggest disadvantage of cloud storage is that a business cannot access its data if it fails to pay.

Volodymyr Sava
Volodymyr Sava is a professional writer. He has the Breakthrough Power of Lateral Thinking. His writing is mind-blowing.

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