We’re here to talk about data storage and data warehouses, their architecture and what they have to offer.
A data warehouse (DWH) is a data storage solution designed to collect, process and analyze an organization’s data. Analysing that data allows an organisation to ‘really’ see the big picture surrounding their business, empowering them to make data driven decisions on how to develop individual areas or as a whole.
Data Warehouses are there to accumulate and clean data from a company’s database management systems in order to create a single source of data. Because of that, DWHs are home to the most accurate information within an organisation.
Once stored correctly, the data can be analyzed and visualized using business intelligence (BI) tools. Advanced BI functions include finding patterns and relationships in data (data mining), artificial intelligence, machine learning, and result visualization tools. They help businesses identify unlooked for trends within their trading patterns and new market opportunities, allowing them to react quickly based on accurate data and forecasts.
But that raises a question: why use a separate storage for analytics instead of analyzing the data in each DBMS separately? DWHs are databases too, after all.
What’s The Difference Between A Data Warehouse And A Transactional Database?
Data Warehouses and transactional databases aren’t the same. Data storage solutions are designed to analyze incoming data hourly, daily, or at some other regular interval. They’re also deployed on top of a Database Management System, which is why they can quickly process large amounts of data collected over multiple years. In reality, Data Warehouses are tools used for complex analysis of data gleaned from a variety of sources: goods, transactions, personnel, logistics, and more.
Database Management systems are earmarked more for everyday work than for analytics. Their information is updated in real time. CRM, ERP, and many other systems and programs are built on database functionality. After up-to-date information reaches the main database, anything significant is passed on to the DWH. That’s what fills in the complete picture.
Data Warehouse Architecture
DWHs can have multiple levels. Here are the most important ones:
Data sources. This is where the initial data is collected. Information is gleaned from the website, the billing system, CRM and ERP systems, and other databases before being sent to storage.
Storage. All the disparate information the DWH gathers is structured and molded into the format the company is looking for. It’s this component that ensures data completeness and integrity.
Showcase. At this level, the data array is structured to facilitate analysis. Even though data warehouses showcases are primarily designed to handle relatively simple tasks, they also work for complex analytics and more unusual projects. You can build showcases using the Managed Service for ClickHouse®.
Service level. This manages the three previous levels. With its help, DWHs monitor data and quickly correct any errors that arise.
Access and business logic. This level aggregates data from showcases and storages. Users build analytics and work with dashboards and charts depending on their access.
Data Storage Solutions Other Than Data Warehouses
Storage solutions contain converted and structured data ready for processing and analysis. That makes data warehouses convenient tools for solving business problems. But data warehouses aren’t the only way to store and analyze data. For example, think about data lakes and data marts. Both are common approaches to big data. Let’s see how they match up to data warehouses.
In data lakes, data is received and stored raw and unstructured. That’s helpful when you’re looking to process and analyze data from various external sources that aren’t a natural fit for the company’s overall profile. For example, you can process the data you need to design marketing strategies.
Data marts store information pertaining to a particular area or department within an organization. The showcase is built from data requested more frequently or needed to perform specific tasks. Given that the storage isn’t bogged down by extra calculations, an approach like this makes it easier to find the data you need.