What is a data warehouse?

A data warehouse, also called an enterprise data warehouse (EDW), is an enterprise data platform used for the analysis and reporting of structured and semi-structured data from multiple data sources, such as point-of-sale operations, marketing automation, customer relationship management, and more.

Data warehouses contain an analytical database, critical analytical components and procedures. They support interim analytics and custom reporting, such as data lines, queries, and business applications. They can combine and integrate large amounts of current and historical data in one place and are designed to give a long-term view of the data over time. These data warehouse capabilities have made the data warehouse one of the key elements of enterprise analytics that help support informed business decisions.

‍

Traditional and cloud-based data warehouse

Traditional data warehouses are hosted in-house, and data flows from relational databases, processing systems, business applications, and other source systems. However, these data warehouses are often designed to capture a subset of data in bulk and store them according to strict schemes, they are not suitable for spontaneous queries or real-time analysis. Companies also have to purchase their own hardware and software for the in-house data warehouse, making scaling and maintenance expensive. Storage space in a traditional data warehouse is often limited compared to processing, so data is quickly converted and then discarded to keep the storage space free.

Today's data analytics activities have become central to all core business activities, including revenue generation, cost containment, improving operations, and enhancing customer experiences. As data evolves and diversifies, organizations need more robust data warehouse solutions and advanced analytical tools to store, manage, and analyze large amounts of data across their organizations.

These systems must be scalable, reliable, secure enough for regulated industries, and flexible enough to support a wide range of data types and big data use cases. They also need to support flexible pricing and calculation, so you only pay as much as you need instead of estimating your capacity. These requirements go beyond the capacity of most legacy data warehouses. As a result, many businesses are turning to cloud-based data warehouse solutions.

A cloud data warehouse runs on a fully managed service in the cloud, in addition to the capabilities of a traditional data warehouse. Cloud data warehouse offers instant scalability to meet changing business requirements and powerful data processing to support complex analytical queries.

Because the cloud service provider manages and maintains the physical infrastructure, the upfront investment in on-premises data warehouse solutions is often much lower and lead times are shorter.

‍

How does the data warehouse work in the cloud?

Like a traditional data warehouse, cloud data warehouses collect, integrate, and store data from internal and external data sources. Data is usually transferred from a source system using a data processing line. The data is extracted from the source system, converted, and then loaded into the data warehouse in a process known as ETL. Data can also be sent directly to a central repository and then converted using ELT processes. From here, users can access, extract and report data using different business intelligence (BI) tools. Cloud data warehouses should also support flow use cases to operate on data in real or near real time.

Cloud data warehouses provide structured and semi-structured data storage, processing, integration, cleaning, installation, and similar services in a public cloud environment. You can also use them in combination with a cloud data lake to collect and store unstructured data. With some providers, it is even possible to combine your data warehouse and data lake to maintain and centrally manage a single copy of your corporate data.

When it comes to cloud data warehouse services, different cloud providers can take a variety of approaches. For example, some cloud data warehouses may use a cluster-based architecture similar to a traditional data warehouse. In contrast, others adopt a modern serverless architecture that further reduces data management responsibilities. However, most cloud data warehouses provide built-in data storage and capacity management features and automatic upgrades.

Other key features that cloud data warehouses have are:

• Self-service ETL and ELT data integration
• Disaster recovery features and automatic backups
• Compliance and data management tools
• Built-in integrations for business intelligence, AI and machine learning

‍

Advantages of building a data warehouse in the cloud

Companies are increasingly moving away from traditional data warehouses and moving to the cloud, taking advantage of the cost savings and scalability that managed services can provide.

Here are the main advantages of cloud data warehouse;

Scaling

Cloud data warehouses are flexible, providing virtually unlimited storage and capacity. You can easily scale them up or down as your business needs change and only pay for what you use.

‍

Machine learning and artificial intelligence initiatives

You can quickly leverage and operationalize the possibilities of machine learning models versus cloud data warehouses and AI technologies to optimize other areas from data mining, forecasting business outcomes and data lifecycle management to business processes and operational costs.

‍

Better uptime

Cloud providers are committed to meeting SLAs and providing better uptime with reliable cloud infrastructure that scales seamlessly. On-premises data warehouses have limitations of scale and resources that can affect performance.

‍

Cost predictability

With the cloud, you get more flexible and predictable pricing. Some providers charge by transaction volume or per node. Others, on the other hand, charge a fixed price for a certain amount of resources. In any case, you avoid the huge costs caused by an in-house data warehouse that operates 24/7, regardless of whether resources are in use or not.

‍

Operational savings

A cloud data warehouse is fully managed and allows you to delegate management issues to cloud providers that must meet service level agreements (SLAs). This results in operational savings and allows your in-house team to focus on growth initiatives.

‍

Real-time analytics

Cloud data warehouses allow you to query data in real time, providing more powerful computing that supports data flow. As a result, you can access and use data much faster compared to an on-premises data warehouse, so you can gain more accurate insights faster and make more informed business decisions.

‍

‍

What is the data warehouse used for?

Cloud data warehouse offers a range of solutions that can benefit an organization. Here are some of the most common data warehouse use cases:

Making decisions in real time: Analyze data in real time to proactively address challenges, identify opportunities, gain efficiencies, reduce costs, and proactively respond to business events.

Consolidating data in silos: Quickly pull data from multiple structured sources in your organization, such as point-of-sale systems, websites, and email lists, and bring it together in one location so you can analyze and gain insights.

Enables business reporting and instant analytics: 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 affecting the performance of operational systems or waiting for help from IT.

Application of machine learning and artificial intelligence: Collect historical and real-time data to develop algorithms that can provide insights, such as predicting traffic increases or recommending related products to the customer browsing a website.

‍

Many businesses and industries need not only large-scale, but also continuous and real-time data analysis. For example, some service providers use real-time data to dynamically adjust prices throughout the day. Insurance companies track policies, sales, claims, payrolls, and more. They also use machine learning to predict fraud. Game companies must monitor and react to user behavior in real time to improve the player experience. Data warehouses make all these activities possible.

If your organization owns or does any of the following, you're probably a good candidate for a data warehouse:

• Multiple different data sources
• Big data analysis and visualization - both synchronously and in real time
• Machine learning models and other AI-driven processes
• Flow analysis
• Custom report generation and ad hoc analysis
• Data mining
• Data science and geospatial analysis

‍

How to choose a cloud-based data warehouse solution?

When choosing a cloud-based data warehouse, it is crucial to evaluate how the solutions work and to have a deep understanding of the current use cases that your cloud data warehouse should support.

Beyond storage capabilities, there are many considerations to consider when choosing between different providers, such as architecture, scalability, security, pricing, performance, and more. For example, you may find that a solution that is simple to scale is not so easy to scale, or you may need to retrain all data analysts and purchase additional licenses to upgrade your existing system.

Beyond looking at the differences between vendors, it's also important to consider what specifically your move to a cloud data warehouse will require and how this relates to your existing IT investments and specific business needs.

Enterprise data warehouses play a central role in an organization's decision-making process. Therefore, you need to make sure you have a deep understanding of business requirements, current use cases, and gaps in existing solutions. It can be useful to involve key stakeholders in the process early in the process to help understand the consequences of replacing a legacy data warehouse solution, functional requirements for meeting challenges, and detailed technical information about data sources, tools, frameworks, and more.

Contact us to learn about enterprise data warehouse solutions and BigQuery, Google Cloud's cost-effective, serverless, multi-cloud enterprise data warehouse.

‍