What is a data warehouse?
Today’s enterprises rely on the effective collection, storage, and integration of data from disparate sources for analysis and insights. These data analytics activities have moved to the heart of revenue generation, cost containment, and profit optimization. As such, it’s no surprise that the amounts of data generated and analyzed, as well as the number and types of data sources, have exploded.
Data-driven companies require robust solutions for managing and analyzing large quantities of data across their organizations. These systems must be scalable, reliable, and secure enough for regulated industries, as well as flexible enough to support a wide variety of data types and use cases. The requirements go way beyond the capabilities of any traditional database. That’s where the data warehouse comes in.
Learn about BigQuery, Google Cloud’s modern and serverless data warehousing solution.
A data warehouse is an enterprise system used for the analysis and reporting of structured and semi-structured data from multiple sources, such as point-of-sale transactions, marketing automation, customer relationship management, and more. A data warehouse is suited for ad hoc analysis as well custom reporting. A data warehouse can store both current and historical data in one place and is designed to give a long-range view of data over time, making it a primary component of business intelligence.
A cloud data warehouse solution is managed and hosted by a cloud services provider. This gives you the inherent flexibility of a cloud environment along with more predictable costs, which can be based on usage or a fixed amount. The up-front investment is typically much lower and lead times are shorter than on-premises solutions because you don’t have to buy hardware, thereby reducing CapEx. You can also achieve operational efficiencies from the serverless / NoOps nature of cloud data warehouses.
A cloud data warehouse lets you outsource the management hassle to cloud providers who must meet service level agreements. This provides operational savings and can keep your in-house team focused on growth initiatives.
It can provide better uptime compared to on-premises data warehouses
Cloud providers are obligated to meet SLAs and provide better uptime with reliable cloud infrastructure that scales seamlessly. On-premises data warehouses have scale and resource limitations that could impact performance.
It’s built for scale
Cloud data warehouses are elastic, so they can seamlessly scale up or down as your business needs change.
It’s cost effective
With cloud, you gain flexible pricing by paying for what you use or choosing a more predictable flat-rate option. Some providers charge by throughput or per hour per node. Others charge a fixed price for a certain amount of resources. In every case, you avoid the mammoth costs incurred by an on-premises data warehouse that runs 24 hours a day, seven days a week, regardless of whether resources are in use or not.
It supports real-time insights
Cloud data warehouses support streaming data, allowing you to query data in real time in order to drive fast and informed business decisions.
It supports machine learning and AI initiatives
Customers can quickly unlock and operationalize machine learning use cases in order to predict business outcomes.
Do you need a data warehouse?
Some businesses and industries require data analysis that is not only massive in scale, but also ongoing and in real time. For example, some service providers use real-time data to dynamically adjust prices throughout the day. Insurance companies track policies, sales, claims, payroll, and more. They also use machine learning to predict fraud. Gaming companies must track and react to user behavior in real time to enhance the player’s experience. Data warehouses make all of these activities possible.
If your organization has or does any of the following, you’re probably a good candidate for a data warehouse:
- Multiple sources of disparate data
- Big-data analysis and visualization—both asynchronously and in real time
- Machine learning/AI
- Streaming analytics
- Custom report generation/ad hoc analysis
- Data mining
- Data science
Consolidate siloed data
Quickly pull data from multiple structured sources across your organization, such as point-of-sale systems, websites, and email lists, and bring it into one location so that you can perform analysis and get insights.
Make decisions in real time
Analyze data in real time to proactively address challenges, identify opportunities, gain efficiency, reduce costs, or proactively respond to business events.
Enable custom reporting and ad hoc analysis
Keep historical data on a separate server from operational data so that end users can access it and run their own queries and reports without impacting the performance of operational systems or needing to get help from IT.
Incorporate machine learning and AI
Collect historical and real-time data to develop algorithms that can provide predictive insights, such as anticipating traffic spikes or suggesting relevant products to a customer browsing a website.
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