In data warehouses, large data sets are stored and analyzed. It is intended to provide a single, consistent view of data from multiple sources. Drawing on multiple sources, data warehouses use innovative transformations to collect and organize data into cohesive insights ready for analysis, helping decision-makers make informed choices.
Enterprises use data warehouses to manage and process the massive amounts of data they generate. The most well-known products include Oracle Data Warehouse, SAP HANA, Microsoft SQL Server, IBM DB2, and Teradata. Each offers unique features, such as support for user-defined objects for query optimization methods, database storage capacities, and sophisticated features like data mining analysis and integration.
A data warehouse can help improve business reporting by providing a single source of truth for all data. This allows for more accurate and consistent reporting across the organization. A data warehouse also provides a platform for data analysis, providing deeper insights into business performance.
Data warehouses simplify the report generation process by providing organizations with a single data repository that can be used to quickly and easily generate timely, accurate reports and provide a 'single version of truth.'
Business data is consolidated and integrated using data warehouses, which allow for the preservation and storage of information from many sources. This can save expenses in various ways, including the need for less human labor and lower data entry costs.
Data warehouses also increase the accuracy of reports and cut down on data duplication. By ensuring a single version of data, companies can save money by ensuring that decisions based on flawed data are avoided. They can also save on redesigning reports due to incorrect data.
Finally, companies can utilize the built-in analytics capabilities of data warehouses to identify cost savings and drive strategic decision-making. By having access to accurate and consolidated data, companies can make more informed decisions, reduce costs, and optimize business processes.
Your business will save time and money through data warehousing, allowing all users to make data-driven decisions more efficiently.
1. Reduced Data Storage Costs: Data warehouses can reduce the amount of data storage needed by consolidating data from multiple sources into one central repository.
2. Improved Data Quality: By providing all data in one place, data warehouses can help improve data quality.
3. Increased Efficiency: Data warehouses can increase efficiency by providing a single source of data for reporting and analysis.
4. Reduced Maintenance Costs: Data warehouses can reduce the time and money spent on maintaining multiple data sources.
5. Improved Decision-Making: Data warehouses can provide a comprehensive view of data that can help improve decision-making.
6. Reduced Time to Market: Data warehouses can help reduce the time it takes to develop new products and services.
7. Improved Customer Service: Data warehouses allow customer service representatives to access a single source of information.
Using the wrong tool is like using a sledgehammer on a nail to hang a picture on your wall. Even if something is expensive or widely used, there are better fits for your organization.
While choosing a data warehouse system, be sure to take security and scalability into account.
Most data warehouses are Online Analytical Processing (OLAP) databases built for lots and lots of columns and are not intended to be used as normal backends for websites. Most databases we'll see in regular use are Online Transactional Processing (OLTP) databases, which are built for row-oriented data, relationships, indexing, and many other things focused on performance. They both have their cases, but you generally will hit the benefits of OLAP databases once you grow a bit.
This story is about using the right tool for the job, an essential part of our work at SUMO Heavy.
Snowflake is one of the largest data warehouse products in the world, which comes at a price. The billing model is complex, and they target enterprise companies. They compete in the cloud data industry with products such as Amazon RedShift, FireBolt, and Vertica, among others. One issue with these large data products is that many companies often buy into them without knowing their true use cases, misuse them, or need help understanding how to build for them with predictable costs.
Two years ago, our team was brought into a project at one of our clients where the client’s team insisted on using Snowflake for the backend of a consumer-facing web application for custom reporting. This scenario was against our recommendation, as the issue here is that it is being treated as an Online Transactional Processing (OLTP) database, which needs to be addressed. Snowflake's engine is built for Online Analytical Processing (OLAP) based database structures and queries.
For this illustration, we won't dive into the technical details, but OLTP is what you'd think of when your typical relational database systems come to mind, such as MySQL, PostgreSQL, etc. They are great for querying normalized data quickly to drive web applications. On the other hand, OLAP is traditionally used for analysis across millions or billions of rows of data, where speed is critical.
If you'd like to read more, Snowflake has a great page on this topic. Here's a quote on intended users:
OLTP systems are customer-facing and designed for use by frontline workers such as store clerks and hotel reservation specialists, as well as online shoppers. OLAP systems are business-facing and are used by data scientists, analysts, and business users such as team leads or executives. These decision-makers access data using analytics dashboards.
Using Snowflake as the backend for the web applications had two significant negative impacts:
As a result, a new data architect is working on the projects and taking a new approach by implementing APIs to sit in front of Snowflake for our apps to integrate with and moving the data into PostgreSQL, where we can continue to use the same APIs. This will be more than enough for the millions of rows being utilized.
Something to keep in mind is that all data warehouse companies will tell you that you need them. Before buying into a platform, it's imperative that, as an organization, you:
Data warehousing is a complex, large-scale operation that affects every part of your business. Starting with something powerful but more vendor agnostic such as PostgreSQL, your organization can take its time moving to a larger platform with more vendor-lock. If properly architected with well-built data pipelines pulling from source data, the new transition from ETL to ELT gives much more flexibility when migrating to a new platform.
We have experience with both Redshift and Snowflake, and both are wonderful tools when the fit is right. The most crucial aspect of your data warehousing journey is planning carefully, including everyone in your organization with data needs, and evaluating before purchasing.
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