A data warehouse is essential for businesses that wish to drive engagement and make critical data-driven decisions. This blog presents all the basic concepts of data warehouse, from what a data warehouse is to how many components it has.
- What is Data Warehouse?
- How Does a Data Warehouse Work?
- Confusing Data Warehouse Terms Explained
- Data Warehouse Architecture
- Data Warehouse Features
- Benefits of Data Warehouse
- Types of Data Warehouse
- Stages of Data Warehouse
- Components of Data Warehouse
- Does Your Business Need Data Warehouse? Answer 5 Questions
- How to Choose a Cloud Data Warehouse: 5 Criteria
- Closing Words
What is Data Warehouse?
A data warehouse is a data management system designed to store your enterprise data, collected from a diverse range of internal and external sources, to support Business Intelligence (BI) and analytics purposes.
This data warehouse is then combined with a business intelligence tool, allowing users to uncover trends and implement sophisticated analyses. Your data warehouse makes sure the information can simply be queried by your selection of Business Intelligence (BI) solutions.
Decision makers access and analyze the information from a data warehouse to extract valuable business insights.
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The data warehousing history started back in the late 1980s when Barry Devlin and Paul Murphy founded the “business data warehouse”.
How Does a Data Warehouse Work?
To explain the data warehouse meaning, you just need to picture the data warehouse as a repository where all the data from various sources and systems comes in.
The data can be structured, semi-structured and unstructured, but once it flows into the data warehouse, it will be transformed and processed ready for Business Intelligence and analytics tools. As time goes by, historical data is built up within the data warehouse, making it a significant “single source of truth” for your business.
Besides its support for BI and analytics tools, data warehouse is also utterly important need for data mining which looks for patterns in your data.
Some of you may have heard of databases, data lakes or data marts. Those are the terms that can be confusing or cause misunderstanding. Don’t worry, we’re here to clarify them.
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Confusing Data Warehouse Terms Explained
1. Data warehouse vs Database
A data warehouse is different from a database. While a database’s sole purpose is to store data from a sole source (such as transactional data), data warehouse processes data from multiple sources to not only store but also read big data to uncover the data relationships or trends.
ETL (Extract, Transform, Load) tools gather information from various locations, covering databases and other sources like websites and files, and put it in a single centralized data warehouse. The ETL process can occur automatically or manually under scheduled pre-defined conditions. When data is in the warehouse, it can then be configured and manipulated.
|Read the detailed comparison of Database vs Data Warehouse|
2. Data warehouse vs Data lake
A data lake centralizes all data including both unstructured and structured data while it’s a requirement for data to be in table format to enter a data warehouse. Since data warehouse needs to query data using SQL, that’s why tabular format is required.
Therefore, data lake only requires raw data, but data warehouse needs curated data. Users of data lake are business analysts, data scientists, data developers, data engineers and data architects, while users of data warehouse are business analysts, data scientists, data developers.
3. Data warehouse vs Data mart
Data mart is a part of data warehouse, so it also stores curated data, but it is designed to serve a specific team such as marketing, sales or finance teams. To be specific, data warehouse collects large amount of data from many sources (around 100’s of gigabytes to petabytes), while data mart includes a sole source or a portion of data in the data warehouse (can be only 10’s of gigabytes). The data from data warehouse is complete and detailed for the whole organization to use while there might only be summarized data in data mart for a single department to use.
Data Warehouse Architecture
Data warehouse architecture is built based on a three-tier structure.
1. Bottom tier
A bottom tier is the data warehouse server which contains a metadata repository and specialized data marts. A gateway is an application program interface which acts as an extraction tool to get data from operational databases and external sources. At the data warehouse server, the gateway allows the customer program to generate SQL codes.
2. Middle tier
A middle tier is the OLAP server which is in charge of querying for the data warehouse. The OLAP server is run by ROLAP model (Relational OLAP) or MOLAP model (Multidimensional OLAP).
3. Top tier
A top tier which consists of front-end tools is in charge of displaying results from OLAP. The OLAP data generated from data mining of additional tools is also displayed in the top tier.
Data Warehouse Features
When the data warehouse receives data from operational systems, all data is read-only and unchangeable. When new data is transferred here, previous data is not deleted. This is the factor that makes data warehouse non-volatile and remain as the “single source of truth” of your organization.
The data warehouse is designed with the sole purpose of centralizing data from various sources in a single database. Not only that, the data warehouse processes and maps the data in a digested and consistent format. This is the crucial foundation for further BI and data analytics activities.
The information stored in the data warehouse is categorized according to the subject. For example, if you want to analyze your company marketing data, you need to build a data warehouse to focus on collecting the marketing data. With the data warehouse, you will be able to easily generate visual reports and answer critical questions such as “Which caused the high abandon rate in the checkout step?” “Which website pages need improvement?”
Benefits of Data Warehouse
With the information above, you have gathered a deeper understanding of data warehouse. Let’s sum up the advantages of data warehouse together:
- The data quality and consistency is enhanced.
- End-users in the company can access data more easily.
- Directors of the company can make better business decisions backed by data.
- Historical data is stored and preserved over time, making it a valuable source for the company.
- Unify data from different systems and sources in one place.
- End-users can make queries and reports with the data warehouse without obstructing the operational systems.
Types of Data Warehouse
Type 1: Enterprise Data Warehouse (EDW)
Enterprise Data Warehouse (EDW) is a centralized warehouse which organizes and represents data from diverse sources across the entire enterprise. Enterprises can choose Cloud Data Warehouse or Data Warehouse On Premise. EDW distributes data and grants data access according to divisions and offers decision support service. In short, EDW is the only source of truth for all the organization’s data.
Type 2: Operational Data Store (ODS)
Operational Data Store (ODS) is designed to collect data from multiple sources to process lightweight data such as operational reports and real-time analytics. With ODS, the data can still be collected and processed while the business operations are still on-going.
Type 3: Data Mart
Data mart is a portion of the data warehouse. The only difference of data mart is that it is built and served for a specific business unit such as marketing or finance team. The data in data mart can be summarized, not detailed data.
Stages of Data Warehouse
1. Offline Operational Database
While the company operation is on-going, the data from the operational system flows to another server. In this stage, the process of loading, processing and reporting of the data does not interfere with your company’s operation.
2. Offline Data Warehouse
The data from operational database is transferred to data warehouse regularly. The data warehouse processes and transforms the data here to meet its standards.
3. Real time Data Warehouse
For this stage, the data transferred from operational database to data warehouse is conducted in real time. For example, for airline booking systems, whenever there is a new booking, the data is updated to the data warehouse.
4. Integrated Data Warehouse
Whenever there is a transaction in the operational system, the data warehouse is updated. Then, the data warehouse also generates transactions back to the operational system.
Components of Data Warehouse
1. Load manager
The load manager, also known as the front component, is in charge of extracting and loading data into the warehouse. The load manager also prepares the data by transforming it and getting it ready to enter the data warehouse
2. Warehouse manager
The warehouse manager is in charge of managing the data within the data warehouse. It analyzes the data to assure data consistency and generates aggregations and denormalization. It also transforms and merges source data while ensuring archive and back up data.
3. Query manager
Query manager is also called the backend component which manages all queries. It schedules and directs queries to the tables inside the data warehouse. With the help of query manager, users receive data in the digested format.
4. End-user access tools
End-user access tools contain five categories, including:
- Data Reporting
- Query Tools
- Application development tools
- EIS tools
- OLAP tools and data mining tools
Does Your Business Need Data Warehouse? Answer 5 Questions
Question 1: Is your company drowning in data?
Ask yourself: “Do you have multiple sources of information that aren’t connected or integrated?”
Let’s say, your customer service records are stored in one system and your financial reports are stored in another. It will then be difficult to match up two parts of the business that aren’t directly connected.
At this point, you may want to find a way to combine them into one place before you can analyze them together. Data warehouses purpose is to consolidate several types of information from multiple sources and make them available for analysis.
Question 2: How volatile is your data?
Volatile data is constantly changing. For example, if you sell products online, the prices and inventory levels will change constantly throughout each day. Because of this, you may have to update your database multiple times per day.
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A data warehouse lets you keep track of this kind of volatile information while still performing complex analyses on it later. Data warehouses can be updated frequently and easily by users across the organization.
Question 3: Do you have reporting problems?
Many businesses use Excel spreadsheets to produce reports, but this can be time-consuming and expensive. Using Excel can lead to errors or inconsistencies in your data.
A data warehouse benefits you in consolidating all your data in one place so that everyone has access to it whenever they need it. It’s easier for you to report on anything from sales to inventory – without having to run reports from multiple sources or ask other people for help.
Having a data warehouse also makes sense if you want to use BI tools such as self-service reporting or dashboards.
Question 4: Is your current data strategy ineffective?
- Your data is unstructured and manually pulled from a variety of disparate sources.
- You don’t have any way to track customer behavior across multiple channels.
- You can’t make informed decisions about what products sell well together.
- You aren’t sure why certain campaigns failed while others succeeded.
That’s mean, your company is working hard just to keep up with its own data needs.
Your current data strategy is ineffective. It’s time for an overhaul of your approach to storing & processing data!
Question 5: Can your business benefit from data mining?
Data mining technique is an effective way to increase sales and improve profits. Data mining involves using software programs that search through substantial amounts of data to find patterns and correlations between different variables.
For example, if there is an increase in customer complaints about product quality within the last six months, then there might be something wrong with your production process or packaging materials that need attention immediately.
Data warehouse combining with data mining supports business intelligence (BI) by providing access to historical data that can be used for analysis and reporting purposes.
How to Choose a Cloud Data Warehouse: 5 Criteria
Consider these 5 factors when choosing a cloud data warehouse for your business:
1. It suits your business needs
Though cloud data warehouses are developed to be generalizable across industries and business departments, it’d be better if you outline your plan to use yours, as the factors for evaluating the providers can vary based on the use case and your business’s own demand.
2. Its technical internals
Cloud data warehouses are different regarding data requirements and assumptions. Some warehouses provide semistructured data in structures such as Object or Array, while some others don’t.
The degree of flexibility required by a business shall determine which approach works best. For example, if a business needs to store data where the structure is not necessarily predefined, the warehouse with the looser structure above may be a good choice.
3. The ecosystem
When selecting a cloud data warehouse, a company should consider where existing data and applications reside. For instance, if most of the data in a system is already in S3, using Redshift or Snowflake on AWS could result in performance gains due to physical data locality. And even if you have to sync data from compute and storage resources in various availability zones, staying within AWS makes sure the data transfer paths are on highly optimized infrastructure rather than having to traverse the public Internet.
4. The cloud data warehouse should meet the security requirements
The cloud data warehouse you choose should support the level and type of security it needs. Though all major cloud data warehouse providers update their security systems regularly and patch vulnerabilities, the systems’ configurations and defaults are various.
For instance, how encryption is handled varies across the major cloud data warehouses. Specifically, BigQuery encrypts data at rest and in transit by default, while Redshift requires database encryption to be explicitly enabled.
Besides, you should also consider factors such as key management and access control.
5. You should understand resource bundling and billing
Different cloud data providers bundle resources and calculate costs differently. Redshift, for example, bundles storage and compute resources together. This means simple pricing, but users also have to accept predefined instance type values for memory, storage, and I/O.
For example, BigQuery has a more granular pricing structure and charges for storage, bytes read, and streaming inserts. Unlike Redshift, its hardware resources are free. Its total costs are therefore less predictable, as they’re primarily a function of bytes read in queries, so it can be hard to accurately predict usage.
Azure’s data warehouse bundles the lower-level technical factors of cost related to compute, like logical CPU cores and I/O, into a “Data Warehouse Unit” (DWU). Cost calculations therefore turn out to be a function of storage and DWUs. With this, users can pause DWU usage, and charges then accrue only for storage.
Likewise, Snowflake abstracts physical resources into credits, which rise in number proportionally with the number of virtual warehouses and the amount of resources within each. A virtual warehouse is a cluster of machines that load data, handle queries, and perform other data manipulation operations. Storage is separate and billed per terabyte monthly.
Most cloud data warehouse providers also have flat-rate pricing available. For example, Redshift provides a pricing model named Reserved Instances that offers discounts if an organization commits to and pays for resource usage for a year or more. Reserved Instances enable businesses with large deployments to manage their costs since usage are more predictable.
It’s true that sometimes estimating costs with high accuracy before using a data warehouse might be a hassle. However, you can conduct a simple analysis of expected workflows so things can be easier. You just need to ask relevant questions, such as:
- How much data do you expect to integrate each month?
- How frequent is the data updated?
- How often are the analytics jobs that you run and how much data do they read?
Answering these questions can help you calculate expected workflows so that you can compare the providers and make the final decision.
What’s more, all the major vendors in the market provide free trials. Consider requesting a demo or trial to define a rough estimate of what costs will look like at scale.
Let’s analyze a data warehouse example of an Australian fashion retailer
The retailer had over 150 GB of data from over 10 data sources including Sales, Marketing, Product Reviews, Customer Support. It managed the huge pile of customer information, transactions, POS units from 2500 stores in over 15 countries.
However, the data was not unified in a single database, which makes it difficult for the retailer to process the data and make correct business reports and forecasts.
With enormous amount of data stored in several business management software, Synodus centralizes the data in a single data warehouse. The data from any source is then updated in real time every 5 minutes.
A data warehouse can be a powerful tool, not just for storing and analyzing your data, but it can be a crucial step towards improving your business as a whole. Ultimately, the decision to build one or not depends on the needs of your specific business and what you expect to get from it in the long run. If you’re considering whether to invest in data warehouse software, it’s important that you weigh the pros and cons. Doing so will allow you to make an informed decision about your business’ needs and give you a better idea of which features are worth prioritizing.