Person managing product information

A data warehouse is a central repository that aggregates structured data. As the name implies, a data warehouse is neatly organized, with metaphorical halls of labeled shelves of structured data sources (like SQL databases or Excel files). It isn’t a cluttered storage space with items stacked and piled haphazardly. And, anyone who’s gone looking for their golf clubs in a messy garage, only to find them hidden behind the holiday decorations, can appreciate the value of an organized data warehouse.

What is a data warehouse?

Data warehousing takes your customer or sales database — or any structured data set — and provides an infrastructure that allows real business intelligence to be pulled from various data sources.

Simply, data warehouses save time by unifying data from various sources. Easier-to-find data is easier to use. When you have data sets from disparate sources like internet of things (IoT) devices, sales and product data and info from multiple databases, properly stored information in a central location gives you a foundation for faster, more accurate data analysis.

With a consolidated view of their critical data, business users can rapidly make informed decisions on key initiatives. 

Why a database doesn’t make a data warehouse redundant

Different tools are best for different jobs. A complex query in an operational database will put that database into a fixed state. If you use a transactional database, you can’t have that slowdown. Data warehousing allows you to analyze a large amount of data without a negative effect on transaction processing.

You can’t afford to throttle transactional or operational data, nor can you slow the process of providing your company’s decision-makers with up-to-date business analytics and insights.

The data quality advantage

A data warehouse won’t live up to its true potential without top-notch data management and data mining to help you convert raw data into usable insights. Just as fresher ingredients add more flavor, data analytics work best when they’re based on high-quality data.

Machine learning and other AI techniques can automatically enforce data quality rules that map, transform and clean your data to delete duplicate entries, old information and data errors. These processes ensure all types of data that populate your data warehouse are clean and organized.

 

How traditional data warehouses and cloud data warehouses compare

The difference between traditional data warehouses and cloud-based data warehouse architecture comes down to two major aspects: proximity and flexibility.

A traditional data warehouse is on-premises. This can be essential for certain regulatory requirements, but often, the main reason for a traditional data warehouse these days is a connection to mission-critical work.

For instance, let’s say your chief revenue officer needs to make an immediate query on your data warehouse. They need to do this quickly and regularly to make decisions that will impact the next few weeks. In this case, housing enterprise data on-premises delivers continuity. With all the training and learning invested in on-premises data warehouses, you don’t want to lose the ability to view data that has real business implications during a cloud migration.

The cloud offers many benefits, as do data warehouses that live there. They are cheaper, faster and infinitely more scalable. Cloud-based data warehouses allow easier access for multiple users, offer better data governance and protection and process all forms of data (structured, semi-structured and unstructured data) with greater efficiency. Better insights, more quickly and at scale.

The best of both worlds

It doesn’t have to be traditional versus cloud. You can do data integration with both traditional and cloud data warehouses. This allows you to perform data mining on disparate sources of data without moving your data from one warehouse to another.

A combo of extract, load, transform (ELT) and extract, transfer, load (ETL) processes can work together to move raw data from its source to your data warehouse or subsets like data marts, whether they’re in the cloud or on-premises.

 

Data warehouses vs data lakes 

Both data warehouses and data lakes are aggregation systems. The difference is data warehouses store structured data, whereas data lakes aggregate unstructured data from sources like social media, streaming platforms or IoT.

Due to the growing need to process large amounts of data from unstructured sources, data lakes are growing in popularity. Businesses rely on several data sources — and need to data-mine both structured and unstructured information. Data warehouses, comparatively, can’t handle different data formats and workloads. They are a tried-and-true aggregation system, but are neither flexible nor scalable for unpredictable workloads.

However, while the value of handling unstructured data is high, data warehouses remain useful in their steadfastness. They’re consistent, predictable and high-performing for structured data, which means data warehouses give you a level of fidelity and confidence. But, due to scalability, many enterprises are moving on-premises data warehouses to the cloud as a more cost-effective solution in the long run.

Whether your cloud data warehouse or lake is on Snowflake, Microsoft Azure, Google Cloud or Amazon Web Services (AWS), Informatica can help you derive value from your data.

Explore Informatica’s intelligent cloud services.

 

The benefits of a data warehouse

  • Ensure you’re working with clean data

    Data warehouses provide infrastructure that facilitates high-quality business intelligence. Data visualization is a powerful tool for identifying trends and building business strategies, but it’s only as useful as the quality of your data. In a data warehouse, you can more easily remove redundant and dated information to make sure you get the decision support you need.

  • Add efficiency to storage that pays off with better analytics

    Data warehouses help save time when it comes to data storage. Not only do they ensure all different sources of data are organized, cleansed and stored, but they make batch analytical processing possible daily. Beyond that, using a data warehouse is key to good database management — it allows you to tap into essential data analytics without slowing down data flows to your operational systems. While cloud data warehouses offer big efficiency boosts (and are very secure), a company may opt for an on-premises data warehouse to address regulatory requirements, data privacy or latency issues.

  • Lower costs for quicker insights

    Clean data in a data warehouse means better, faster insights that inform more lucrative decisions while helping you sidestep detrimental ones. And, if you run your data warehouse in the cloud, the ability to scale at a lower cost can really pay off — more efficiency in the cloud equates to big savings.

  • Improve long-term decision-making

    Big data continues to prove the old saying, “Those who don’t learn from history are doomed to repeat it.” With more information than ever at the fingertips of business decision-makers, there are fewer excuses for missed opportunities. Don’t let a lack of information and insight become a roadblock. With both new and historical data to process, your data warehouse system can help you deliver more hits and fewer misses.

 

Informatica’s data warehouse solutions

From data quality to data cataloging and data governance, Informatica has the most comprehensive data management solutions, no matter which data warehouse you use. Whether you want to move to the cloud or need a hybrid solution, see how Informatica can bring better data analytics to your field:

Every industry can benefit from better data management and integration that powers more effective business insights. While the data may vary, the solutions — along with Informatica’s best practices — can be applied to ensure security alongside powerful data warehouse management.

 

Data warehousing customer success stories

Discover how Informatica’s data management and integrations solutions led to big gains for these companies.

  • Abu Dhabi Department of Culture and Tourism (DCT)

    To gauge economic impact, Abu Dhabi’s DCT needed to measure the quantity and activity of visitors to build complex data configurations and flows — all while moving their data warehouse to the cloud. With data coming in from hotels, museums and tourist sites, they needed an automated system to organize the information and make it easier to use. With Informatica Intelligent Cloud Services, Informatica Data Quality and Cloud Data Integration, the Abu Dhabi DCT not only moved to the cloud, but they cleansed their data to enable more accurate tourism insight, all while saving more than 2,000 person-hours annually.

  • Feeding America

    Solving the issue of hunger is no small feat, and to do it, Feeding America needed a better system to efficiently process food and financial donations. They needed a cloud-based solution to help them maximize every donation. With Informatica Intelligent Cloud Services and Cloud Data Integration, Feeding America compiled data from over a dozen internal systems — adding in live data — to make sure the right donations in the right quantities make it to the right locations. And, this secure system led to a 20x increase in financial donations, too.

You can get more out of your data warehouse too. See how Informatica can help.

Explore Informatica’s intelligent cloud services.