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 as a go-to solution to manage their growing data volumes.
In this article, we’ll cover the different use cases of data warehouse tools, techniques, and the factors to consider when choosing the right solution.
Data Warehouse Tools: Areas of Use
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.
Here are a few use cases and applications that show how data warehouse solutions are helping organizations address data management challenges:
1. Data Cleansing
Many companies use data warehouse tools and techniques for leveraging historical data for critical business decisions. Hence, it is important 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making data cleansing a part of the data warehousing process, which can help detect and remove invalid, incomplete, or outdated records from the source datasets.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 format which is compatible with the target system, such as a database, so as to simplify data loading.
To streamline the data integration step in a data warehouse, many data warehouse management tools offer built-in transformations, such as aggregate, lookup, join, and filter, which makes data processing easier.
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. In the digital era, organizations have abundant information in raw form, which is generally stored in a data warehouse. To aid data retrieval, it is crucial for data warehouse management tools to have BI functionality as it helps generate business insights.
Considerations for Selecting a Data Warehousing Tool
For a data-driven business, having a consolidated, properly configured data warehouse architecture is indispensable. To execute queries and perform multifaceted analyses, you need an effective tool so that different teams across the organization can access and utilize data.
However, choosing a data warehouse software tool that fits all your business requirements needs careful consideration. After all, shifting from one 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 some factors you should consider when selecting your data warehouse tool:
1. Cloud vs. On-Premise
The first point to consider when choosing a data warehouse analytics tool is whether to go for a cloud or an on-premise solution. If you’re looking for a low-cost solution with no servers, hardware, and lower maintenance costs then you should go for a cloud data warehousing solution.
Conversely, if securing data is a priority for your business, then on-premise data warehouse tools may be a better choice as they give you full 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.
Which tool will give you faster query performance? How swiftly can you extract data from the source systems and load into the destination systems? Which tool will help your data warehouse architecture to maintain an ideal performance level?
Data warehouse tools offer varied performance levels depending on the way they are structured. To maintain 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 tool that offers support for the frequently used source data formats and target data structures. This will allow you to quickly access diverse data sets for timely decision-making.
If your company is rapidly expanding, you want to choose a data warehouse analytics tool that scales with 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 scalability in terms of cost, resources, and simplicity. Some tools need more maintenance but are cost-effective. Similarly, you’ll find some 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 its 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. For example, to ensure that error-free data is loaded into the data warehouse, your selected 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 data warehouse. By streamlining the process, these tools can radically decrease the time, expenses, and risks of data warehousing projects.
Business expansion usually involves integrating diverse data sources, such as cloud sources, in-memory formats, and databases, which ultimately leads to growing volumes of heterogeneous data. In such a scenario, it is important to select a tool that can integrate data from different applications and information systems.
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 right data warehousing tool, you can clean, transform, and integrate disparate data, automate processing, maintain it in a report-oriented data structure, and allow end-users to carry out analyses at enterprise-scale for improved BI.
Still not sure which data warehouse tool to choose? Contact us to get bespoke advice.