Companies increasingly depend on insights from their business data to form decisions that drive performance.
What is a Data Warehouse?
A data warehouse is a technology solution that compiles structured data from multiple sources so that it can be better analyzed for insights. A data warehouse is commonly compared to a data lake.
A data warehouse only stores data that has been modeled or structured, while a data lake stores it all—structured, semi-structured, and unstructured. Essentially, the data structure and requirements are not defined until the data is needed. The following table shows a quick breakdown of the differences between the two data solutions.
The Value of Data
Data, and the intrinsic value it holds, is certainly nothing new. Throughout history, organizations have used data to advance their mission and goals—from agriculture to different technological advances across industries. However, the way that data is managed and used today is significantly different. And the way it will be managed ten years from now will also be significantly different.
At the start of the millennium, we entered the beginning of the digital age. 2002 was the first year that digital storage capacity surpassed total analog capacity. The compounded annual growth rate of data is expected to be around 60 percent. So data continues to grow, not only in volume and velocity but in complexity.
Data Warehouse in the Public Cloud
Many executives have yet to take on the cloud. For cloud providers like Google Cloud, the challenge is helping customers take the first steps towards employing big data in the cloud.
According to an O’Reilly Media survey of its community, 40 percent of respondents said they use or plan to use the cloud for production data warehousing,
“With the cloud, you’re always going to be cutting edge. You just cannot do that with on-premises solutions. Lots of people think cost is the number-one reason to choose the cloud, but that’s the wrong way to look at it. When you’re building a business case for the cloud, your primary considerations should be speed, scalability, and flexibility.” – Marc Clark, Director of Cloud Strategy and Deployment at Teradata.
Why Choose a Cloud-Native Data Warehouse?
Companies are seeking out data warehousing to handle large volumes of data, accommodate new sources of high-velocity data, and to organize their data assets for improved visibility.
Cloud-native data warehouses are built to automatically manage concurrency, data growth, query performance, and core data operations such as disaster recovery and backups. Cloud-native data warehouses can substantially reduce operational burdens.
Google’s BigQuery, for example, evolved from the company’s internal data warehouse (known as Dremel). It was designed to cut down the analysis and management of the massive, fast-moving data that powers Google’s various businesses.
Cloud data warehouses that accelerate organizations will display three important characteristics:
- Serverless computing
- Separation of storage and compute resources
- Strong integration with machine intelligence services
Running a business with confidence and a reliable data and analytics platform is critical. It’s up to leadership to champion initiatives to upgrade and implement data warehouses for future capacity, speed, interoperability, and analytics.
Modern data warehouses are still logical data architectures at heart, although the data is physically distributed. Modernizing warehouse data depends on the data platform modernization for appropriate storage, capacity, interfaces, in-place processing, and multi-structured data support.
Modernizing warehouse data (to embrace dimensionality, real-time and unstructured data, and detailed sources for analytics) may depend on data platform modernization for appropriate storage, capacity, interfaces, in-place processing, and multi-structured data support. This is why modern data warehouses are still logical data architectures at heart, although the data is physically distributed across an increasing number of platform types, including new ones such as those based on columns, clouds, appliances, graphs, complex event processing, and Hadoop.
Online analytical processing (OLAP) continues to be the most common analytics method. OLAP is typically considered part of a business intelligence strategy and its focus is to analyze large quantities of data. However, modernization introduces methods known as advanced analytics. These are based on technologies for mining, clustering, graph, statistics, and natural language processing (NLP) and they are part of the Artificial Intelligence analytics group.
Reports produced by analytics and insights and are a common and simple source of truth that the organization can rely on. Modernizing these reports includes:
- Bringing them online for greater distribution and ease of use
- Giving them a visual presentation and organizing them around metrics and KPIs to support performance measurement
- Personalizing them so users can quickly find what they need
While a transition to the cloud offers speed, scale, and modern technology solutions,, the migration can also bring about unexpected changes. A data warehouse migration should be organized as a multiphase project. Prioritize high-value components, such as business analytics, that should be migrated during the early phases.
Many large and medium businesses are in the process of becoming data-driven organizations that produce large amounts of data across different processes and internal entities.
Wizeline’s Advanced Analytics platform provides ways to start small and scale, offering customers powerful capabilities to accelerate business insights. Generating quick wins by applying cloud services and infrastructure as a code. As opposed to the traditional approach of selecting a few vendors, buying expensive licenses, and waiting for complicated implementation projects.
Insights & Smart Analytics
Insights are the value obtained through the use of analytics. Analytics can help your business make the transition into a more data-driven world.
First, you need to democratize your data insights. You need to be able to deliver insights fast and foster data collaboration at scale. As mentioned above, a cloud-native data warehouse will have a strong integration with machine intelligence services.
The simplification of a cloud service to create models, automated workflows, and do data analysts allows the team to perform predictive analytics right out of the box and with high accuracy. Focus on building sustainable teams that produce valuable work across the organization and understand that AI is a team sport that takes time.
At Wizeline, we believe that building world-class data management and analytics doesn’t have to be overly complex or costly. Our engineers have worked with companies to build solutions that leverage their existing technology and cloud services.
Our data experts have the ability to assist with any part of your data lifecycle; providing best practices, solving analytical issues, creating feature-rich dashboards, or and helping you with your cloud-native warehouse needs.
About the Wizeline Data Practice
The data and AI practice at Wizeline puts forth the best talent and most innovative solutions so clients can transform traditional operations into data-driven businesses.
The team connects, transforms, and prepares information so companies can make informed decisions, reduce operational burden, optimize resource utilization, and create competitive advantages. Building the right data environment, developing efficient pipelines, engineering powerful data features, and creating and refining algorithms and models. Data capabilities can span a range of disciplines and skills. At Wizeline, these include data science, data analysis, and data engineering.
If you are interested in learning about different cloud-native solutions or would like us to help you complete a data warehouse maturity assessment, reach out to us at email@example.com.