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A data warehouse is a central repository of information that can be analyzed to make more informed decisions. Data flows into a data warehouse from transactional systems, relational databases, and other sources, typically on a regular cadence. Business analysts, data engineers, data scientists, and decision-makers access the data through business intelligence (BI) tools, SQL clients, and other analytics applications.
A data warehouse architecture is made up of tiers. The top tier is the front-end client that presents results through reporting, analysis, and data mining tools. The middle tier consists of the analytics engine that is used to access and analyze the data. The bottom tier of the architecture is the database server, where data is loaded and stored. Data is stored in two different types of ways: 1) data that is accessed frequently is stored in very fast storage (like SSD drives) and 2) data that is infrequently accessed is stored in a cheap object store, like Amazon S3. The data warehouse will automatically make sure that frequently accessed data is moved into the “fast” storage so query speed is optimized.
What Is a Data Warehouse
Data warehouses serve as a central repository for storing and analyzing information to make better-informed decisions. An organization’s data warehouse receives data from a variety of sources, typically on a regular basis, including transactional systems, relational databases, and other sources.
A data warehouse is a centralized storage system that allows for the storing, analyzing, and interpreting of data in order to facilitate better decision-making. Transactional systems, relational databases, and other sources provide data into data warehouses on a regular basis.
A data warehouse is a type of data management system that facilitates and supports business intelligence (BI) activities, specifically analysis. Data warehouses are primarily designed to facilitate searches and analyses and usually contain large amounts of historical data.
A data warehouse can be defined as a collection of organizational data and information extracted from operational sources and external data sources. The data is periodically pulled from various internal applications like sales, marketing, and finance; customer-interface applications; as well as external partner systems. This data is then made available for decision-makers to access and analyze.
So what is data warehouse? For a start, it is a comprehensive repository of current and historical information that is designed to enhance an organization’s performance.
Key Characteristics of Data Warehouse
The main characteristics of a data warehouse are as follows:
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Subject-Oriented
A data warehouse is subject-oriented since it provides topic-wise information rather than the overall processes of a business. Such subjects may be sales, promotion, inventory, etc. For example, if you want to analyze your company’s sales data, you need to build a data warehouse that concentrates on sales. Such a warehouse would provide valuable information like ‘who was your best customer last year?’ or ‘who is likely to be your best customer in the coming year?’
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Integrated
A data warehouse is developed by integrating data from varied sources into a consistent format. The data must be stored in the warehouse in a consistent and universally acceptable manner in terms of naming, format, and coding. This facilitates effective data analysis.
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Non-Volatile
Data once entered into a data warehouse must remain unchanged. All data is read-only. Previous data is not erased when current data is entered. This helps you to analyze what has happened and when.
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Time-Variant
The data stored in a data warehouse is documented with an element of time, either explicitly or implicitly. An example of time variance in Data Warehouse is exhibited in the Primary Key, which must have an element of time like the day, week, or month.
Database vs. Data Warehouse
Although a data warehouse and a traditional database share some similarities, they need not be the same idea. The main difference is that in a database, data is collected for multiple transactional purposes. However, in a data warehouse, data is collected on an extensive scale to perform analytics. Databases provide real-time data, while warehouses store data to be accessed for big analytical queries.
Data warehouse is an example of an OLAP system or an online database query answering system. OLTP is an online database modifying system, for example, ATM. Learn more about the OLTP vs. OLAP differences.
Data Warehouse Architecture
Usually, data warehouse architecture comprises a three-tier structure.
Bottom Tier
The bottom tier or data warehouse server usually represents a relational database system. Back-end tools are used to cleanse, transform and feed data into this layer.
Middle Tier
The middle tier represents an OLAP server that can be implemented in two ways.
The ROLAP or Relational OLAP model is an extended relational database management system that maps multidimensional data processes to standard relational processes.
The MOLAP or multidimensional OLAP directly acts on multidimensional data and operations.
Top Tier
This is the front-end client interface that gets data out from the data warehouse. It holds various tools like query tools, analysis tools, reporting tools, and data mining tools.
How Data Warehouse Works
Data Warehousing integrates data and information collected from various sources into one comprehensive database. For example, a data warehouse might combine customer information from an organization’s point-of-sale systems, its mailing lists, website, and comment cards. It might also incorporate confidential information about employees, salary information, etc. Businesses use such components of data warehouses to analyze customers.
Data mining is one of the features of a data warehouse that involves looking for meaningful data patterns in vast volumes of data and devising innovative strategies for increased sales and profits.
Types of Data Warehouse
There are three main types of data warehouse.
Enterprise Data Warehouse (EDW)
This type of warehouse serves as a key or central database that facilitates decision-support services throughout the enterprise. The advantage to this type of warehouse is that it provides access to cross-organizational information, offers a unified approach to data representation, and allows running complex queries.
Operational Data Store (ODS)
This type of data warehouse refreshes in real-time. It is often preferred for routine activities like storing employee records. It is required when data warehouse systems do not support reporting needs of the business.
Data Mart
A data mart is a subset of a data warehouse built to maintain a particular department, region, or business unit. Every department of a business has a central repository or data mart to store data. The data from the data mart is stored in the ODS periodically. The ODS then sends the data to the EDW, where it is stored and used.
Data Warehouse Example
Let us look at some examples of how companies use data warehouses as an integral part of their day-to-day operations.
Investment and Insurance companies use data warehouses to primarily analyze customer and market trends and allied data patterns. In sub-sectors like Forex and stock markets, data warehouse plays a significant role because a single point difference can result in huge losses across the board.
Retail chains use data warehouses for marketing and distribution, so they can track items, examine pricing policies and analyze buying trends of customers. They use data warehouse models for business intelligence and forecasting needs.
Healthcare companies, on the other hand, use data warehouse concepts to generate treatment reports, and share data with insurance companies and in research and medical units. Healthcare systems depend heavily upon enterprise data warehouses because they need the latest, updated treatment information to save lives.
Data Warehousing Tools
Wondering what Data warehouse tools is? Well, these are software components used to perform several operations on an extensive data set. These tools help to collect, read, write and transfer data from various sources. What do data warehouses support? They are designed to support operations like data sorting, filtering, merging, etc.
Data warehouse applications can be categorized as:
- Query and reporting tools
- Application Development tools
- Data mining tools
- OLAP tools
Some popular data warehouse tools are Xplenty, Amazon Redshift, Teradata, Oracle 12c, Informatica, IBM Infosphere, Cloudera, and Panoply.
General stages of Data Warehouse
Earlier, organizations started relatively simple use of data warehousing. However, over time, more sophisticated use of data warehousing begun.
The following are general stages of use of the data warehouse (DWH):
Offline Operational Database:
In this stage, data is just copied from an operational system to another server. In this way, loading, processing, and reporting of the copied data do not impact the operational system’s performance.
Offline Data Warehouse:
Data in the Datawarehouse is regularly updated from the Operational Database. The data in Datawarehouse is mapped and transformed to meet the Datawarehouse objectives.
Real-time Data Warehouse:
In this stage, Data warehouses are updated whenever any transaction takes place in the operational database. For example, an Airline or railway booking system.
Integrated Data Warehouse:
In this stage, Data Warehouses are updated continuously when the operating system performs a transaction. The Datawarehouse then generates transactions which are passed back to the operational system.
Data Warehouse Architecture
The architecture of a data warehouse is determined by the organization’s specific needs. Common architectures include
- Simple. All data warehouses share a basic design in which metadata, summary data, and raw data are stored within the central repository of the warehouse. The repository is fed by data sources on one end and accessed by end users for analysis, reporting, and mining on the other end.
- Simple with a staging area. Operational data must be cleaned and processed before being put in the warehouse. Although this can be done programmatically, many data warehouses add a staging area for data before it enters the warehouse, to simplify data preparation.
- Hub and spoke. Adding data marts between the central repository and end users allows an organization to customize its data warehouse to serve various lines of business. When the data is ready for use, it is moved to the appropriate data mart.
- Sandboxes. Sandboxes are private, secure, safe areas that allow companies to quickly and informally explore new datasets or ways of analyzing data without having to conform to or comply with the formal rules and protocol of the data warehouse.
Components of Data warehouse
Four components of Data Warehouses are:
Load manager: The load manager is also called the front component. It performs all the operations associated with the extraction and load of data into the warehouse. These operations include transformations to prepare the data for entering into the Data warehouse.
Warehouse Manager: The warehouse manager performs operations associated with the management of the data in the warehouse. It performs operations like analysis of data to ensure consistency, creation of indexes and views, generation of denormalization and aggregations, transformation and merging of source data, and archiving and backing-up data.
Query Manager: Query manager is also known as the backend component. It performs all the operation operations related to the management of user queries. The operations of this Data warehouse components are direct queries to the appropriate tables for scheduling the execution of queries.
End-user access tools:
This is categorized into five different groups 1. Data Reporting 2. Query Tools 3. Application development tools 4. EIS tools, 5. OLAP tools and data mining tools.
Who needs a Data warehouse?
DWH (Data warehouse) is needed for all types of users:
- Decision-makers who rely on the mass amount of data
- Users use customized, complex processes to obtain information from multiple data sources.
- It is also used by people who want simple technology to access the data
- It is also essential for those people who want a systematic approach to making decisions.
- If the user wants fast performance on a huge amount of data which is a necessity for reports, grids, or charts, then a Data warehouse proves useful.
- A data warehouse is the first step If you want to discover ‘hidden patterns’ of data flows and groupings.
What Is a Data Warehouse Used For?
Here, are the most common sectors where Data warehouse is used:
Airline:
In the Airline system, it is used for operation purposes like crew assignment, analyses of route profitability, frequent flyer program promotions, etc.
Banking:
It is widely used in the banking sector to manage the resources available on the desk effectively. A few banks are also used for market research and performance analysis of the product and operations.
Healthcare:
The healthcare sector also used Data warehouses to strategize and predict outcomes, generate patient treatment reports, share data with tie-in insurance companies, medical aid services, etc.
Public sector:
In the public sector, data warehouse is used for intelligence gathering. It helps government agencies to maintain and analyze tax records, and health policy records, for every individual.
Investment and Insurance sector:
In this sector, the warehouses are primarily used to analyze data patterns, and customer trends, and to track market movements.
Retain chain:
In retail chains, Data warehouse is widely used for distribution and marketing. It also helps to track items, customer buying patterns, and promotions and is also used for determining pricing policy.
Telecommunication:
A data warehouse is used in this sector for product promotions, sales decisions and to make distribution decisions.
Hospitality Industry:
This Industry utilizes warehouse services to design as well as estimate their advertising and promotion campaigns where they want to target clients based on their feedback and travel patterns.
Steps to Implement Data Warehouse
The best way to address the business risk associated with a Datawarehouse implementation is to employ a three-prong strategy as below
- Enterprise strategy: Here we identify technical including current architecture and tools. We also identify facts, dimensions, and attributes. Data mapping and transformation are also passed.
- Phased delivery: Data warehouse implementation should be phased based on subject areas. Related business entities like booking and billing should be first implemented and then integrated with each other.
- Iterative Prototyping: Rather than a big-bang approach to implementation, the datawarehouse should be developed and tested iteratively.
Here, are key steps in Datawarehouse implementation along with its deliverables.
| Step | Tasks | Deliverables |
|---|---|---|
| 1 | Need to define project scope | Scope Definition |
| 2 | Need to determine business needs | Logical Data Model |
| 3 | Define Operational Datastore requirements | Operational Data Store Model |
| 4 | Acquire or develop Extraction tools | Extract tools and Software |
| 5 | Define Data Warehouse Data requirements | Transition Data Model |
| 6 | Document missing data | To-Do Project List |
| 7 | Maps Operational Data Store to Data Warehouse | D/W Data Integration Map |
| 8 | Develop Data Warehouse Database design | D/W Database Design |
| 9 | Extract Data from Operational Data Store | Integrated D/W Data Extracts |
| 10 | Load Data Warehouse | Initial Data Load |
| 11 | Maintain Data Warehouse | On-going Data Access and Subsequent Loads |
Best practices to implement a Data Warehouse
- Decide a plan to test the consistency, accuracy, and integrity of the data.
- The data warehouse must be well-integrated, well-defined, and time-stamped.
- While designing Datawarehouse make sure you use the right tool, stick to the life cycle, take care of data conflicts, and are ready to learn you’re your mistakes.
- Never replace operational systems and reports
- Don’t spend too much time on extracting, cleaning, and loading data.
- Ensure to involve all stakeholders including business personnel in the Data warehouse implementation process. Establish that Data warehousing is a joint/ team project. You don’t want to create a Data warehouse that is not useful to the end users.
- Prepare a training plan for the end users.
Why We Need Data Warehouse? Advantages & Disadvantages
Advantages of Data Warehouse (DWH):
- Data warehouse allows business users to quickly access critical data from some sources all in one place.
- The Data warehouse provides consistent information on various cross-functional activities. It is also supporting ad-hoc reporting and queries.
- Data Warehouse helps to integrate many sources of data to reduce stress on the production system.
- Data warehouse helps to reduce total turnaround time for analysis and reporting.
- Restructuring and Integration make it easier for the user to use for reporting and analysis.
- A data warehouse allows users to access critical data from a number of sources in a single place. Therefore, it saves users time in retrieving data from multiple sources.
- The data warehouse stores a large amount of historical data. This helps users to analyze different time periods and trends to make future predictions.
Disadvantages of Data Warehouse:
- Not an ideal option for unstructured data.
- The creation and Implementation of a Data Warehouse is surely time confusing affair.
- Data Warehouses can be outdated relatively quickly
- Difficult to make changes in data types and ranges, data source schema, indexes, and queries.
- The data warehouse may seem easy, but actually, it is too complex for the average user.
- Despite best efforts at project management, the data warehousing project scope will always increase.
- Sometime warehouse users will develop different business rules.
- Organizations need to spend lots of their resources on training and Implementation purposes.
The Future of Data Warehousing
- Changes in Regulatory constraints may limit the ability to combine sources of disparate data. These disparate sources may include unstructured data which is difficult to store.
- As the size of the databases grows, the estimates of what constitutes a very large database continue to grow. It is complex to build and run data warehouse systems which are always increasing in size. The hardware and software resources available today do not allow to keep a large amount of data online.
- Multimedia data cannot be easily manipulated as text data, whereas textual information can be retrieved by the relational software available today. This could be a research subject.
Data Warehouse Tools
There are many Data Warehousing tools available in the market. Here, are some most prominent ones:
1. MarkLogic:
MarkLogic is a useful data warehousing solution that makes data integration easier and faster using an array of enterprise features. This tool helps to perform very complex search operations. It can query different types of data like documents, relationships, and metadata.
https://www.marklogic.com/product/getting-started/
2. Oracle:
Oracle is the industry-leading database. It offers a wide range of choice of data warehouse solutions for both on-premises and in the cloud. It helps to optimize customer experiences by increasing operational efficiency.
https://www.oracle.com/index.html
3. Amazon RedShift:
Amazon Redshift is a Data warehouse tool. It is a simple and cost-effective tool to analyze all types of data using standard SQL and existing BI tools. It also allows running complex queries against petabytes of structured data, using the technique of query optimization.
https://aws.amazon.com/redshift/?nc2=h_m1
How do data warehouses, databases, and data lakes work together?
Typically, businesses use a combination of a database, a data lake, and a data warehouse to store and analyze data. Amazon Redshift’s lake house architecture makes such an integration easy.
As the volume and variety of data increases, it’s advantageous to follow one or more common patterns for working with data across your database, data lake, and data warehouse:
A data warehouse is specially designed for data analytics, which involves reading large amounts of data to understand relationships and trends across the data. A database is used to capture and store data, such as recording details of a transaction.
Unlike a data warehouse, a data lake is a centralized repository for all data, including structured, semi-structured, and unstructured. A data warehouse requires that the data be organized in a tabular format, which is where the schema comes into play. The tabular format is needed so that SQL can be used to query the data. But not all applications require data to be in tabular format. Some applications, like big data analytics, full text search, and machine learning, can access data even if it is ‘semi-structured’ or completely unstructured.
Data warehouse vs data lake
| Characteristics | Data Warehouse | Data Lake |
|---|---|---|
| Data | Relational data from transactional systems, operational databases, and line of business applications | All data, including structured, semi-structured, and unstructured |
| Schema | Often designed prior to the data warehouse implementation but also can be written at the time of analysis
(schema-on-write or schema-on-read) |
Written at the time of analysis (schema-on-read) |
| Price/Performance | Fastest query results using local storage | Query results getting faster using low-cost storage and decoupling of compute and storage |
| Data quality | Highly curated data that serves as the central version of the truth | Any data that may or may not be curated (i.e. raw data) |
| Users | Business analysts, data scientists, and data developers | Business analysts (using curated data), data scientists, data developers, data engineers, and data architects |
| Analytics | Batch reporting, BI, and visualizations | Machine learning, exploratory analytics, data discovery, streaming, operational analytics, big data, and profiling |
Data warehouse vs database
| Characteristics | Data Warehouse | Transactional Database |
|---|---|---|
| Suitable workloads | Analytics, reporting, big data | Transaction processing |
| Data source | Data collected and normalized from many sources | Data captured as-is from a single source, such as a transactional system |
| Data capture | Bulk write operations typically on a predetermined batch schedule | Optimized for continuous write operations as new data is available to maximize transaction throughput |
| Data normalization | Denormalized schemas, such as the Star schema or Snowflake schema | Highly normalized, static schemas |
| Data storage | Optimized for simplicity of access and high-speed query performance using columnar storage | Optimized for high throughout write operations to a single row-oriented physical block |
| Data access | Optimized to minimize I/O and maximize data throughput | High volumes of small read operations |
How does a data mart compare to a data warehouse?
A data mart is a data warehouse that serves the needs of a specific team or business unit, like finance, marketing, or sales. It is smaller, more focused, and may contain summaries of data that best serve its community of users. A data mart might be a portion of a data warehouse, too.
Data warehouse vs data mart
| Characteristics | Data Warehouse | Data Mart |
|---|---|---|
| Scope | Centralized, multiple subject areas integrated together | Decentralized, specific subject area |
| Users | Organization-wide | A single community or department |
| Data source | Many sources | A single or a few sources, or a portion of data already collected in a data warehouse |
| Size | Large, can be 100’s of gigabytes to petabytes | Small, generally up to 10’s of gigabytes |
| Design | Top-down | Bottom-up |
| Data detail | Complete, detailed data | May hold summarized data |
Benefits of Data Warehouse
Wondering why businesses need data warehousing? Well, there are several benefits of data warehouse for end users.
- Improved data consistency
- Better business decisions
- Easier access to enterprise data for end-users
- Better documentation of data
- Reduced computer costs and higher productivity
- Enabling end-users to ask ad-hoc queries or reports without deterring the performance of operational systems
- Collection of related data from various sources into a place
Companies having dedicated Data Warehouse teams emerge ahead of others in key areas of product development, pricing, marketing, production time, historical analysis, forecasting, and customer satisfaction. Though data warehouses can be slightly expensive, they pay in the long run.
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Build Your Career in Data Warehousing
If you are looking to work as a Business Intelligence (BI) professional or learn data warehousing, you have many exciting career options available. Data architects, database administrators, coders, and analysts are some of the most sought-after BI professionals. Prepare yourself for a job interview with our data warehouse interview questions.
