The international market for data warehousing is expected to expand by 10.7 percent between 2020 and 2028, surpassing a total market value of $51.8 billion by 2028.
This means that a data warehouse is no longer a catchword or an innovative idea. It has now become a popular data storage methodology. Several data-driven enterprises are turning towards data warehousing tools as a go-to solution to manage their growing data volumes of structured and unstructured data.
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 Do You Need Them?
A data warehouse is a storage 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.
Typically, a data warehouse gathers scattered data from operational data stores. These stores are generally transaction systems stored in relational database management system, such as SQL Server.
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.
4 Important Features that Data Warehouse Tools Should Have
1. Data Cleansing
Many companies use data warehousing to leverage historical data for critical business decisions. Hence, ensuring that only high-quality data is loaded into a data warehouse through data processing is essential. 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making data cleansing a part of the data warehousing process, which can help detect and remove invalid, incomplete, or outdated records from the source datasets.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. These steps include aggregate, lookup, join, and filter, to streamline the data integration step in a data warehouse. Integrated data provides a holistic data profile and is necessary for effective analysis.
3. Data Governance and Metadata Management
Data Governance and Metadata Management play critical roles in a data warehouse tool. Data governance ensures the integrity, compliance, and effective management of data through policies, processes, and controls. It encompasses activities like monitoring data quality, tracking data lineage, implementing data security measures, and adhering to data privacy regulations.
On the other hand, metadata management focuses on managing information related to data, including its structure, attributes, and relationships.
Efficient data governance and metadata management are vital for ensuring data transparency, reliability, and compliance. They empower organizations to maintain accurate and consistent data, meet regulatory requirements, and make well-informed decisions.
4. 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.
Business Intelligence and Data Warehouse Considerations
Source: RhapsodyTech
How to Choose The Best Data Warehouse Tool? 6 Factors to Consider
A consolidated, properly configured data warehouse architecture for a data-driven business is vital. You need an effective data warehouse design tool to execute queries and perform multifaceted analyses. This allows teams from across the organization to easily 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 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 cloud or on-premise data warehouse software. If you’re looking for cost-effective data warehouse software with no servers, hardware, and lower maintenance costs, you should choose a fully managed cloud-based data warehouse.
If securing data is a priority, then an on-premise data warehouse architecture might be the right way to go. This option gives you complete control over information security and access. Moreover, on-premise data warehouse solutions generally deliver higher speed than their cloud alternatives due to lower chances of latency issues.
2. Performance
Regarding performance, access speed, 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 top 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 decisions.
3. Cost
Businesses need to consider multiple cost factors before choosing a data warehouse solution. These factors include:
- BI and reporting.
- Storage required.
- Maintenance and training.
- Manpower.
BI and data visualizations are critical for presenting data to stakeholders, which is why many DWH solutions offer dashboards for reporting. You can either opt for an enterprise solution or go for an open-source tool. While open-source solutions are cheaper, they require dedicated developers to code and maintain.
The amount of storage required is another important factor. Cloud data warehouses offer scalable storage and charge you per gigabyte/terabyte of data – a perfect option for large enterprises.
You also need to consider the cost of setting up, learning, and maintaining a data warehouse. No-code solutions, like Astera Data Warehouse Builder, can be set up in days and can be used by non-technical users. No-code tools also reduce the need for dedicated developers, reducing labor costs.
4. Scalability
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 less expensive.
Similarly, you’ll find some DWH tools that are horizontally scalable. This means that they offer high performance even if you add more nodes to your data warehouse. Also, if correctly optimized, such tools can be relatively economical.
5. Automation Capabilities
The conventional approach to data warehousing is replaced by automation 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 solution 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 and consuming your data model in the BI tool of your choice.
Unlike traditional data warehousing tools, modern ones support workflow automation and data models 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, skipping the tedious process of SQL querying.
By streamlining the process, modern data warehouse tools can radically decrease data warehousing projects’ time, expenses, and risks.
6. Integrations
Business expansion usually involves integrating diverse data sources, such as cloud sources, in-memory formats, and databases. This results in growing volumes of heterogeneous data. Hence, it is essential to select a DWH tool that can integrate data from different applications and information systems.
Astera Data Warehouse Builder for Business
Astera DW Builder
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 can help you 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. Some key features of Astera Data Warehouse Builder include:
- Native connectors built-in to support seamless integration with popular databases, web applications, and leading cloud service providers, such as Amazon, Azure cloud, SQL Server, PostgreSQL, Vertica, Google Cloud, Salesforce, HubSpot, SAP Hana, and others, ensuring smooth data integration across various platforms.
- Robust ETL and ELT engines with advanced capabilities to efficiently handle extensive workloads and optimize query performance, enabling fast and efficient data processing.
- Schema-agnostic data modeling and intuitive no-code development features significantly reduce the time required to bring your data warehouse into production, allowing for quicker implementation and deployment.
- Comprehensive data model verification module that ensures the deployment of dimensional models or data vaults without errors, ensuring data accuracy and integrity throughout the data warehousing process.
- Unified metadata-driven architecture that facilitates the integration of structured, semi-structured, and unstructured data from diverse sources, enabling a holistic view of data and maximizing data insights.
- Granular role-based access control mechanism that enables fine-grained control over user roles, granting and restricting access to different users based on their specific needs, enhancing data governance and security measures.
Want to learn more about Astera data warehouse builder? Contact us to get bespoke advice.
