Learn the difference between a cloud data warehouse and traditional data warehouse, plus best practices for migrating to a cloud data warehouse for improved agility and scalability

What is a Cloud Data Warehouse?

A cloud data warehouse is at the heart of a structured analytics system. It serves as a central repository of information that can be analyzed to enable a business to make better-informed decisions. Businesses require relevant insights from across the organization—whether they’re related to new products, fraud detection, optimal pricing, or maximizing customer loyalty. A cloud data warehouse delivers agility, standing up in minutes rather than months, and can be scaled up or down as required. In order to continue to deliver value and fit into a modern analytics ecosystem, legacy on-premises data warehouses need to modernize by moving to the cloud.

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Data integration and data management are critical to cloud data warehousing. You need a comprehensive data management solution in order to discover relevant data across your organization, migrate it to your cloud data warehouse, and keep the cloud data warehouse updated with fresh and trustworthy data on a regular basis. To accommodate data that comes from sources outside the company, your integration and data management solution needs to be able to handle any data type (structured, semi-structured, or unstructured), any user, any data source, and any data integration pattern.

In this article, we’ll explain how a cloud data warehouse differs from a traditional data warehouse, how your business will benefit from a cloud data warehouse, and best practices for migrating to a cloud data warehouse. 

What is a data warehouse?

data warehouse runs on a specialized database that’s specifically designed and optimized for data warehouse operations, rather than for transactional system operations. Data flows into a data warehouse from transactional systems, relational databases, line of business applications, and other sources, typically on a regular cadence. A data warehouse is focused on data quality and presentation, providing tangible data assets that are actionable and consumable by the business.

Traditional data warehouse vs. Cloud data warehouse: What’s the difference?

Traditional, on-premises data warehouses are expensive to scale and don’t excel at handling raw, unstructured, or complex data. Designed using 1990s data-management practices, traditional data warehouses can’t keep up with today’s increase in end-users, data volume, processing workloads, and data-analysis use cases.

With the cloud, an organization can simplify and accelerate the development of their data warehouse, reducing IT costs and the total cost of ownership. By taking full advantage of cloud technology, businesses can evolve and adapt their data strategies—and overcome the challenges of scalability, elasticity, data variety, data latency, adaptability, data silos, and data science compatibility. The cloud also provides the opportunity to enhance data governance and security with an integrated data management solution.

6 reasons to move to a cloud data warehouse

A cloud data warehouse enables businesses to rapidly launch new analytics initiatives. It allows you to adapt to changing workloads quickly, expanding or reducing capacity to accommodate fluctuations in data volume and concurrent users. This ability to scale out also results in faster processing speeds, giving organizations the agility to respond to changing business demands by spinning up resources for new analytics projects. Ultimately, by generating deeper insights about your customers and their purchase journey, PxM drives sales, builds loyalty, and ensures exceptional customer experiences.

Modernizing your data warehouse provides a number of additional benefits, including support for:

  • On-premises, cloud, and multi-cloud environments. Today’s typical deployments involve at least four separate cloud environments, as well as a number of on-premises systems. The ability to seamlessly connect legacy applications with a cloud-based analytics warehouse—without isolating any of the data that they store and manage—is critical.
  • All types of data and data latencies. Businesses today need to be able to analyze a range of data types—including structured, semi-structured, and unstructured—from a multitude of sources, such as batch, real-time, and streaming. 
  • All data users. Data scientists, data analysts, data engineers, and report writers each have different data needs. With data ranging from raw to highly transformed, with lineage and traceability throughout, a cloud data warehouse can support all users, ideally.
  • Data quality, data protection, and data governance. Managing and mitigating risk is a core function of data management and modern data warehousing. It’s vital to maintain data quality as well, because poor data quality creates the potential for misinformation and can impede decision making.
  • End-to-end data management. To effectively manage a modern data supply chain, you need processes for data ingestion, data-stream processing, data integration, data enrichment, data preparation, definition, and cataloging, the mapping of data relationships, data protection, and data delivery.
  • AI and machine learning (ML). Modern data management requires AI and ML in order to efficiently perform data discovery, tagging, matching, mapping, and description.

Getting started with your data warehouse migration

The first step in developing a cloud data warehouse is to build a data strategy. By defining what you hope to accomplish and identifying the steps and services involved in achieving those goals, you can map out your data warehouse modernization. Here’s what goes into a successful data strategy:

Create a business case. What value will a cloud data warehouse bring to your business? Your answer—whether it’s greater agility, faster query performance, increased data capacity, or cost savings—will help determine your migration path.

Assess your existing data warehouse. Invest in an enterprise data catalog solution to get a clearer picture of the data in your data warehouse.

Decide how to migrate. You can choose whether to lift and shift your on-prem data warehouse “as-is” to the cloud, migrate your on-prem data warehouse incrementally to a new cloud data warehouse, or build a net-new cloud data warehouse. Many companies choose to migrate their data warehouse incrementally, focusing on key use cases to deliver the quickest value.

Select a technology platform and data management environment. Decide whether you want to manage the infrastructure yourself (IaaS) or let the cloud provider do so (PaaS). Cloud-based data management tools that can span both cloud and on-premises are the most versatile and can minimize disruption to the business when moving data incrementally between on-premises and cloud environments.

Migrate and operationalize. For best results, define test and acceptance criteria at the beginning of your migration. Plan the testing, then execute the migration process to move schema, ETL, data, metadata, users, and applications. Execute the test plan, then operationalize the cloud data warehouse. 

Cloud data warehouse success stories

Cloud data warehouse modernization has helped businesses around the globe become more efficient and agile and prepare for the demands of the digital age. Here are just two examples of how companies have benefited from migrating to a cloud data warehouse:

  • Kelly Services connects talented people to companies in need of their skills. Focused on helping companies meet the evolving demands of the modern workplace, the organization needed a way to manage talent effectively, drive job placements, and gain a complete view of job candidates and opportunities.
    By building a modern data architecture with Informatica Intelligent Cloud Services and Microsoft Azure, Kelly Services was able to create timely and targeted placement opportunities through talent-related data. The company was also able to streamline job placements by leveraging synergies between Informatica and Microsoft for faster application development. Finally, Kelly Services made it easier to offer the right opportunity to the right talent by integrating data from customer and partner systems using master data management (MDM).
  • A wholly owned subsidiary of National Interstate Corporation, National Interstate Insurance was able to process insurance applications up to nine times more efficiently by using Informatica Intelligent Cloud Services. A specialty property and insurance casualty company, National Interstate also enabled employees to locate information from sales and service systems in minutes instead of days or weeks by using Informatica Cloud Application Integration to synchronize Salesforce data in real time with an on-premises underwriting platform.
    The company was also able to improve business pricing and underwriting decision-making by turning data into actionable insights, thanks to their ability to automatically update a centralized data warehouse each night. As a result, National Interstate has enhanced their customer service and increased their profitability.

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