Simply defined, a data warehouse is a system that pulls together data from many different sources within an organization. On top of this system, business users can create reports from complex queries that answer questions about business operations to improve business efficiency, make better decisions, and even introduce competitive advantages.
It’s important to understand that a data warehouse is definitely different than a traditional database. Sure, data warehouses and databases are both relational data systems, but they were definitely built to serve different purposes. A data warehouse is built to store large quantities of historical data and enable fast, complex queries across all the data, typically using Online Analytical Processing (OLAP). A database was built to store current transactions and enable fast access to specific transactions for ongoing business processes, known as Online Transaction Processing (OLTP).
So, data warehousing allows you to aggregate data, from various sources. This data, typically structured, can come from Online Transaction Processing (OLTP) data such as invoices and financial transactions, Enterprise Resource Planning (ERP) data, and Customer Relationship Management (CRM) data. Finally, data warehousing focuses on data relevant for business analysis, organizes and optimizes it to enable efficient analysis.
How Are Data Warehouses Used?
A data warehouse is a decision support system which stores historical data from across the organization, processes it, and makes it possible to use the data for critical business analysis, reports and dashboards.
Furthermore, there are some great benefits to leveraging a data warehouse architecture for your requirements. This includes:
- High performance querying on large volumes of data
- Simpler queries to allow in-depth data exploration
- Collects historical data from multiple periods and multiple data sources from across the organization, allowing strategic analysis
- Provides an easy interface for business analysts and data ready for analysis
Stepping back, data warehouse use-cases focus on providing high-level reporting and analysis that lead to more informed business decisions. Use-cases include:
- Carrying out data mining to gain new insights from the information held in many large databases
- Conducting market research by analyzing large volumes of data in-depth
- An online business analyzing user behavior to make business decisions
More specifically, organizations have been using data warehouses for some really interesting business use-cases. For example:
Data warehouses are constantly evolving to support new technologies and business requirements – and remain relevant when it comes to big data and analytics. This means that if you’re leveraging older data storage systems, you might be running into some problems supporting new and advanced data analytics solutions. Remember, a data warehouse is not a database. Solutions like those form Panoply offer the world’s first smart data warehouse, which provides end-to-end data management. Based on a self-optimizing architecture, it uses machine learning and natural language processing to go from source data to analysis in minutes. Leveraging this kind of system can take your data needs to the next level.
Simplifying Big Data and analytics.
This is a big point bridging from the previous note. It’s absolutely key to understand just how much is possible with big data platforms like Hadoop. But can your current infrastructure support it? One big use-case for data warehouse design is the integration with big data systems like Hadoop. For example, Panoply makes it easy to combine your Hadoop HDFS data into your Panoply Smart Data Warehouse, giving you instant cloud access to your HDFS data without any ETL or ELT process. HDFS, or the Hadoop Distributed File System, is an open source data storage software framework. Panoply’s end to end data management solution is able to load Hadoop Data into your Panoply Smart Data Warehouse with only a few clicks, giving your analysts and scientists instant access.
One key aspect in working with data is the ability to integrate with other key systems. For example, maybe you have a lot of data in your data warehouse architecture. How are you integrating it with data visualization? Or, do you have integration with things like reporting and analytics? A great use-case for data warehousing is to integrate with amazing data services ranging from everything like business intelligence (BI), to data visualization (Tableau). For example, you can quickly integrate Amazon Kinesis Firehose reporting and analysis into your Smart Data Warehouse with the Panoply Amazon Kinesis Firehose integration. Panoply imports all of your data into your own secure Smart Data Warehouse, where it can be accessed with all of your other stored data. From there, the integration reduces your analysis time, helping you to find insights that improve your business. With this data you’ll be building custom reports and dashboards in no time.
Preparing for a data-driven future
The future of data warehousing revolves around your ability to integrate and work with data. Leading data warehousing systems will allow you to leverage integration as a great way to get the most of your data without requiring a complicated data infrastructure. Working with next-generation data warehousing revolves around simplicity, more data delivery capabilities, and working to advance the business.
This might be a lot to take in; but it doesn’t have to be hard to get started. If you have data requirements that are being complicated by your current systems, look at a data warehouse as a real-world option to make things easier. Remember, the future will absolutely be driven by your ability to work with and house data. And, this can’t be done with traditional databases. Those organizations that capture the full benefits of data will be the ones which can deeply understand the market and evolving customer requirements.