Large organizations and modern companies run on and gather myriad types of data. When businesses utilize data warehousing, they are able to easily aggregate all of their data and develop insights needed to remain competitive.
FoundSM offers a comprehensive business data warehousing solution that combines first, second, and third-party data. We will help you with the process of setting up a data warehouse and the management of processing, integrating, and storing data. Once this is complete, you can see all of your real-time data and begin data analysis.
What is a Data Warehouse?
Data warehousing is a place where large amounts of data are stored from multiple heterogeneous sources so that analysis and actionable goals can be created by a business or organization. Data Warehousing is a key component of any business that uses analytical techniques on business data.
Data warehousing was originally introduced in 1988 by a couple of IBM researchers. They needed to come up with a way to store computer systems, as their systems became more complex and handled more and more data.
What is the Difference Between a Data Warehouse and Database?
Data Warehouses and databases are similar in nature but are built to serve different purposes. A data warehouse was built to store large quantities of data from multiple sources and provides a longer view of an organization’s data over time. Meanwhile, standard operational databases and transactional databases are ideal for focusing on shorter-term and real-time data updates.
Data Warehouse Architecture
The data warehouse architecture is determined by each organization’s needs and is generally split into three types of architecture: single-tier, two-tier, and three-tier
A single-tier data warehouse is meant to minimize the amount of data stored within the system. This setup strives to remove any data redundancy and is not utilized by most businesses, as most warehouses require much more complex setups.
A two-tier setup separates data sources from the warehouse. While this architecture is efficient at data storage, it’s also not suitable for most businesses, as it experiences connectivity issues while limiting the number of end-users who can access the data.
This is the most common type of data warehouse architecture as it organizes information from raw data to valuable and actionable insights. This type of architecture is also organized into three tiers.
- Bottom Tier: This is the database layer of the warehouse. Here the data is cleansed, transformed, and put back into this layer.
- Middle Tier: This tier uses an online analytical processing (OLAP) server and acts as a mediator between the end-user and the database.
- Top Tier: The top tier is the tools and APIs you use to get the data out of the warehouse. This can be anything from query tools, reporting tools, or data mining tools.
Differences between Data Warehouses, Data Marts, and Data Lakes
As mentioned earlier, a data warehouse is a great way to store business analytics data and it is great to give insights to different parts of data.
- A Data Warehouse is best suited for structured data that is defined by a schema.
- A Data Lake can hold both structured and unstructured data, along with raw data like social media posts, log files, images, or internet clickstream records.
- A Data Mart is similar to a data warehouse but it only holds the data of one specific department of your business.
Data warehouses, data lakes, and data marts all serve different purposes and are essential to data insight.
What to Expect with our Data Warehousing Services
When we are first setting up a data warehouse for a client, there are a number of different items that we must do in order to make sure we are set up for success. Here is what you can expect from us during this part of our engagement.
The Google BigQuery Data Warehousing Solution
What is Google BigQuery?
BigQuery is a cloud-based enterprise data warehouse run by Google. It was designed to store and analyze terabytes of data in a matter of seconds. If you use Oracle, IBM, or Microsoft ecosystems, you will be right at home in this tool as it supports standard SQL.
There are a number of different benefits of using Google BigQuery. A few of them are:
- BigQuery machine learning can offer real-time analytics
- Transfer data between Google properties like Google Ads, YouTube, and Google Marketing Platform
- Automatic Backups with easy restorations
BigQuery is a powerful tool that organizations of all sizes can use and that’s why it is our main solution for creating a data warehouse.
Benefits of Data Warehousing
There are some great benefits to having a data warehouse to store all of your organizational data. Here are just a few of the benefits of a data warehouse.
- Generate Return on Investment (ROI) quickly and efficiently access all of your data in one place.
- Improve data consistency and quality with all of your data located in one place, no need to worry about inaccurate reporting on any of your data.
- Deliver business intelligence that gives you better insight into your business.
- A competitive advantage when you can see a clear snapshot of your business, you can start to create goals and find your competitive advantage.
- Better decision-making access to a clear and concise view of all your data makes for better reporting and more accurate decision making.
- Forecast with ease when you can get a better view of historical data, you can start to plan future projections.
- Organized historical data ease of having all of your past and present data in one location.
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