The international data warehousing market is expected to expand by 8.3 percent between 2019 to 2024, surpassing a total market value of $20 billion by 2024. This means that a data warehouse is no longer a catchword or an innovative idea; it has now become a mainstream data storage methodology. Several data-driven enterprises are turning towards data warehousing tools as a go-to solution to manage their growing data volumes.
This article will cover the different use cases of data warehouse tools, techniques, and the factors to consider when choosing a data warehouse solution.
Data Warehouse Tools: Why We Need Them?
A data warehouse is a repository that comprises information from one or multiple sources. For example, an e-commerce company can use a data warehouse to integrate and combine diverse customer information, such as customer email addresses, the cash register, comment cards, etc. The main benefit of a data warehouse is its role in streamlining data for business intelligence (BI). However, the process of ETL in the data warehouse is essential for the smooth movement of data from one architectural tier to another.
Unlike traditional data warehouses, modern data warehousing solutions automate the repetitive tasks involved in designing, developing, and deploying a data warehouse design to meet fast-changing business requirements. For this reason, many companies leverage data warehousing tools to gather insights.
List of Features that Data Warehouse Tools Should Have
Here are a few use cases and examples of data warehouse applications addressing various data management challenges:
1. Data Cleansing
Many companies use data warehousing for leveraging historical data for critical business decisions. Hence, it is essential to ensure that only high-quality data is loaded into a data warehouse. This can be done by making data cleansing a part of the data warehousing process, which can help detect and remove invalid, incomplete, or outdated records from the source datasets.
2. Data Transformation and Loading
Data transformation involves modifying data into a compatible format with the target system, such as a database, to simplify data loading.
Many data warehouse management tools offer built-in transformations, such as aggregate, lookup, join, and filter, making data processing easier to streamline the data integration step in a data warehouse.
3. Business Intelligence and Data Analysis
Data warehousing and Business Intelligence (BI) are two distinct but closely interlinked technologies that assist an enterprise in making informed decisions. Organizations have much information in raw form in the digital era, generally stored in a data warehouse. It is crucial for data warehouse analytics tools to have BI functionality to aid data retrieval as it helps generate business insights.
How to Choose Data Warehouse Tools?
Having a consolidated, properly configured data warehouse architecture for a data-driven business is indispensable. To execute queries and perform multifaceted analyses, you need an effective data warehouse design tool so that different teams across the organization can access and utilize data with ease.
However, choosing a data warehouse software tool that fits all your business requirements needs careful consideration. After all, shifting from one DWH tool to another can be laborious and disrupting. So, the more thought you put into your selection, the easier you’ll make things for yourself in the future.
Here are five key factors that you need to consider when choosing a warehousing platform:
1. Cloud vs. On-Premise
When choosing a data warehouse software tool, the first point to consider is going for a cloud or on-premise data warehouse software. If you’re looking for a low-cost data warehouse software with no servers, hardware, and lower maintenance costs, you should go for a cloud-based data warehouse.
Conversely, if securing data is a priority for your business, then an on-premise data warehouse architecture might be the right way to go, as it gives you complete control over information security and access. Moreover, on-premise data warehouse solutions generally deliver higher speed than their cloud-deployment alternatives due to lower chances of latency issues.
When it comes to performance, access and processing speed are two important considerations for any data warehousing tool. While searching, ask yourself which data warehouse management tool will give you faster query performance? How swiftly can you extract data from the source systems and load it into the destination systems? Which tool will help your data warehouse architecture to maintain an ideal performance level?
Data integration tools in data warehousing offer varied performance levels depending on how they are structured. To maintain the optimum performance of your data warehouse, use a tool that ensures that your data is cleansed, de-duplicated, transformed, and loaded accurately.
Also, choose a data warehouse software tool that supports frequently used source data formats and target data structures. This will allow you to access diverse datasets for timely decision-making quickly.
If your company is rapidly expanding, you want to choose a data warehouse analytics tool that scales your business. For example, go for a tool that offers quick and seamless cluster resizing without continuous monitoring to ensure compliance with the dataset requirements.
You can determine the scalability of various data integration tools for data warehousing in terms of cost, resources, and simplicity. Some tools need more maintenance but are cost-effective. Similarly, you’ll find some DWH tools that are horizontally scalable, meaning that they offer optimum performance even if you add more nodes to your data warehouse. Also, if correctly optimized, such tools can be relatively economical.
4. Automation Capabilities
The conventional approach to data warehousing is replaced by an automated alternative to address the growing needs of data volume and enable faster time-to-insight. DWA tools automate the repetitive steps involved in designing, developing, and deploying a data warehouse. To ensure that error-free data is loaded into the data warehouse, your selected data warehousing tool should be able to automate the data cleansing process right from the profiling of source data to its validation before loading it into the data warehouse.
Unlike traditional data warehousing tools, modern ones support workflow automation and data model design patterns, such as Vault, Inmon, and Kimball. They offer automation at each step, from designing the data warehouse to mapping and generating ETL code to load information in the data warehouse. By streamlining the process, modern data warehouse tools can radically decrease data warehousing projects’ time, expenses, and risks.
Business expansion usually involves integrating diverse data sources, such as cloud sources, in-memory formats, and databases, leading to growing volumes of heterogeneous data. It is essential to select a DWH tool that can integrate data from different applications and information systems in such a scenario.
Astera Data Warehousing Builder for Business
A snapshot of Astera Data Warehouse builder in action
Companies are looking for a scalable and agile solution to integrate, store, and manage vast amounts of data and streamline their data journey. By using the correct data warehousing tool, they can simplify data warehousing.
Astera Data Warehouse Builder helps companies automate building an enterprise data warehouse from scratch. It expedites transformation and integration, schema structure modeling, and delivers a high-performance data warehouse through a unified and intuitive platform.
Want to learn more about Astera data warehouse builder? Contact us to get bespoke advice.