What is Data Warehousing? Characteristics, Types & Benefits

Posted February 8, 2022

Data warehouses, as one might expect, can be quite large and expensive to build and maintain. A well-thought-out data warehousing strategy assists businesses in maximizing their return on investment (ROI) by improving accessibility, transparency, and functionality.

What is a Data Warehouse & What is Data Warehousing?

A data warehouse is a centralized repository of information. As a result, businesses can use it to help make better decisions. Data flows into a data warehouse regularly from transactional systems, relational databases, and other sources.

Business users use reports, dashboards, and analytics tools to extract insights from data, monitor business performance. These dashboards and analytical tools also support businesses in decision-making.

Data warehouses power these reports, dashboards, and analytics tools. Also, these warehouses store data efficiently to minimize data input and output (I/O). As a result, they can deliver query results quickly to hundreds of thousands of users concurrently.

Data Warehousing is the process of collecting and managing data from various sources to provide meaningful business insights. Also, it is a collection of technologies and components that aid in the strategic use of data. Furthermore, it helps in converting data into information and making it available to users for them to make a difference on time.

Characteristics Of Data Warehouse

Data warehouses are very important for growing businesses because of their commendable features. Given below are the major data warehouse features –

1. Subject-oriented – A data warehouse is a subject-oriented approach. Because, it provides information on a specific topic rather than information about an organization’s ongoing operations.

Inventory and storage are examples of such issues. It also provides a simple and succinct description of the subject by omitting details that would be useless in assisting the decision-making process.

2. Integrated – In a data warehouse, integration entails establishing a standard unit of measurement from various databases for all similar data. Within it, you must store data in a simple and universally acceptable manner.

It must also maintain consistency in naming conventions and format. This type of application aids in the analysis of large amounts of data.

3. Time-variant – In comparison to operating systems, the data warehouse has a relatively long time horizon. The data stored in a data warehouse is acknowledged over time and provides historical information. Each primary key in the data warehouse should include a time element, either implicitly or explicitly.

4. Non-volatile – The data warehouse is also non-volatile, which means that you can not erase the previous data. The data is read-only and is only updated regularly. It also aids in the analysis of historical data and understanding of what and when events occurred. You don’t need any other difficult process.

Data warehousing services in India have sparked tremendous interest in real-world applications, particularly in banking, business, healthcare, and other areas, as worries about data manageability and complexity have grown.

Types Of Data Warehouses

Enterprise Data Warehouse

An enterprise data warehouse is a database that brings together various functional areas of an organization and unifies them. Also, it is a centralized location where all business information from various sources and applications is accessible.

EDW’s goal is to provide a comprehensive overview of any particular object in the data model. You can accomplish it by identifying and manipulating data from various systems. After that, you can load the data into a consistent and conformed model.

Operational Data Store

Businesses can use an operational data store instead of an operational decision support system application. It facilitates direct access to data from the database, which also supports transaction processing.

Data Store aids in the integration of disparate data from multiple sources. Hence, making it easier to carry out business operations, analysis, and reporting. It works well for simple queries and small amounts of data. Generally it functions as a temporary memory, storing the most recent information.

Data Mart

It focuses on storing data for a specific functional area and contains a subset of data from a data warehouse. Data Marts aid in improving user responses while also reducing the volume of data required for data analysis.

Data Mart, as a subset of Datawarehouse, is simple to set up. When compared to a full data warehouse, it is less expensive. Also, it is more adaptable. As a result, you can define its structure and configuration with the help a single subject matter expert.

Benefits Of Data Warehouse

Several businesses use data warehousing because it provides numerous benefits, such as streamlining operations and increasing profits. Nevertheless look at the advantages of data warehouses to businesses –

Scalability – Data warehouse is easy to scale, making it easier for the business to stride ahead with minimum hassle.
Historical Insights – It enables businesses to access historical data with a few mouse clicks. The warehouse can store data that is months or years old.
Better Efficiency – It increases the efficiency of the business by collecting data from multiple sources and processing it to provide actionable insights.
Improved Security – By collecting data in a centralized warehouse, it becomes easier to set up a multi-level security system to prevent the data from being misused.
Increased Revenue – With access to valuable data analytics, businesses have better decision-making. This increases the revenue in the long run.
Faster Analytics – When data is available in the central data warehouse, it takes less time to perform data analysis and generate reports.

Data Warehouse Solution

Warehousing solutions are more important than ever in an era when data is the most valuable resource any business has. Data warehousing solutions frequently include a variety of useful data management and consolidation features.

You can use them to extract and curate data from a variety of sources. Also, they will help to transform data and remove duplicates, and ensure consistency in your analytics. Some data warehouses even include machine learning algorithms and artificial intelligence (AI).

Data Warehouse solutions become even more adaptable when delivered via the cloud. Business leaders, like those in other as-a-service environments, can add and remove features to meet the changing needs of their organization.

Data Warehousing Tools

Amazon Redshift

Fast, cost-effective, and easy to use
Works great with big data
Automatic scaling
Redshift spectrum runs queries against unstructured data

Thinklytics

Flexible and scalable
Integrate with multiple data sources
Works with relational databases
Easy to connect to online data analytics

IBM Infosphere

Suitable for intense projects
Reliable and scalable
Boosts business agility
Excellent ELT tool

Oracle 12c

Known for optimization and high performance
Offers advanced analytics
Scalable
High-level data compression

Teradata

Data segregation into hot and cold
Parallel processing of data & queries
Simplified data analytics
Relational database management system

Data Warehouse Implementation

Businesses can carry on data warehouse implementation under different stages, which are as follows –

1. Capacity planning: The first step is to define enterprise needs, architectures, capacity planning, and hardware and software tools.

2. Hardware integration: Once you select the hardware and software, you need to put them in by integrating the servers, the storage methods, and the user software tools.

3. Modeling: It is an important stage. Here, you have to design the warehouse schema and views. If the data warehouses are sophisticated, this may include the use of a modeling tool.

4. Physical modeling: Designing the physical data warehouse organization, data placement, data partitioning, deciding on access techniques, and indexing are all part of this.

5. Sources: The data warehouse’s information is likely to come from a variety of sources. Identifying and connecting the sources via the gateway or ODBC drives is part of this step.

6. ETL: The process of designing and implementing the ETL phase may contain defining a suitable ETL tool vendor and purchasing and implementing the tools.

7. Populate the data warehouses: Once you have agreed upon the ETL tools, you have to test them, possibly in a staging area.

8. User applications: End-user applications are required for data warehouses to be useful. This step entails designing and implementing the applications that they require.

9. Application roll-out: Once the data warehouse has been populated and the end-client applications have been tested, the warehouse system and operations can be rolled out.

How Does Data Warehouse Work?

You can use data warehousing to provide additional visibility into a company’s performance. You can do it by combining integrated data from multiple sources. A data center is intended to run historical data searches and analyses.

There is no need to change data after its integration into the system. You can keep this warehoused data in a secure, accurate, and easy-to-manage format. Also, You can take some steps toward the creation of a data warehouse.

The first step is data extraction, which involves collecting large amounts of data from multiple sources. After processing the data, it goes through data cleaning. It is the process of combing through the data for errors and removing or excluding any found errors. You can clean-up this data from a computer format to a warehouse form.

When the data is processed in the facility, it goes through processing, consolidating, summing, and other steps to make it more organized and user-friendly. Consequently, when multiple data points are modified over time, new data is added to the warehouse.

Data Warehouse Services

Strategy & Consulting

Assessment of Data Sources and existing Infrastructure
Data Integration, Accessibility & Migration Requirements
Data Warehouse Workload Estimation & Costing
On-premise, Cloud, or Hybrid DWH Platforms and Technology

DWH Development & Support

Developing a Data Model Based on Business Goals
Identifying and Extracting Information from Data Sources (E)
Cleaning and transformation of data to improve quality (T)
Data loading into a centralized repository (L)

DWH Migration & Optimization

Diagnostics Report of the existing DWH Setup
Modeling issues must be resolved to improve query response time
Enabling best practices for Master Data Management
Data Cube and Marts assessment

Data Warehouse as a Service

Assessment of Data Sources and existing Infrastructure
Data Integration, Accessibility & Migration Requirements
Data Warehouse Workload Estimation & Costing
On-premise, Cloud, or Hybrid DWH Platforms and Technology

Data Warehouse Architecture

The architecture of a data warehouse refers to the design of an organization’s data collection and storage framework. Data warehouse design focuses on determining the most efficient method of extracting knowledge from a raw collection.

It also focuses on converting it into a system that can be easily digested and provides valuable BI insights. When constructing a data warehouse for an organization, you need to consider three main types of architecture. Each of this has its own set of benefits and drawbacks.

The goal of single-tier warehouse architecture is to create a compact data set while minimizing the amount of data stored. While it is useful for removing redundancies, it is not appropriate for organizations with large data needs and multiple data streams.

Two-tier warehouses physically separate available resources from the facility itself. Although processing and organizing data is more efficient, it is not as adaptable and requires a minimum number of end-users.

But, the most common type of data warehouse architecture is three-tier architecture, which creates a more structured flow from raw sets to actionable insights. The database server itself is housed in the bottom tier, as are the data cleaning and transformation back-end tools.

The second tier employs OLAP and serves as a liaison between end-users and the warehouse. OLAPS can communicate with both relational and multidimensional databases, allowing them to collect additional data based on broader parameters.

The top tier is the front end of a company’s overall business analysis system. It is where developers can communicate results by using questions, data visualizations, and data analytics software.

Components of Data Architecture

The following are the components of data architecture a business needs to plan before beginning the data warehousing process.

Data pipeline
Cloud storage
APIs
AI & ML models
Real-time analytics
Kubernetes

What Does Data Warehousing Allow Organizations To Achieve?

A data warehouse is a facility that centralizes and consolidates massive amounts of data from various sources. Its analytical capabilities enable businesses to gain valuable business insights from their data. As a result, allowing them to make better decisions.

Also, it enables organizations to collect only data from their various databases for the current day. It reduces some of the server’s day-to-day resources. Consequently, organizations can use operating systems to replace all of their day-to-day databases.

Data Warehousing And Data Mining

Data mining, also known as data knowledge discovery, is the process of identifying patterns and other valuable information in large data sets.

Because of the advancements in data warehousing technology and the proliferation of big data, the adoption of data mining techniques has accelerated over the last few decades.

Businesses may store data for exploration and data mining, in which they seek information patterns that will assist them in improving their business processes.

A warehousing system can also allow different departments to access each other’s data. On the other hand, data mining can help businesses by converting raw data into useful knowledge.

Data warehousing and data mining both go hand in hand & work effectively to improve business performance. A data warehouse, for example, may allow a company to quickly review data from the sales team. Also, it make decisions about how to increase revenue or streamline the department.

In order to get a better position and increase sales of its products, the company may choose to focus on the spending habits of its customers. You can divide the data mining method into three steps.

First, the companies gather information and load it into data warehouses. They then store and manage the data, either on-premises or in the cloud. Finally the Business analysts, IT experts, and management teams decide how to organize it.

Integration Of Data Mining System With Data Warehouse

Data warehousing and data mining systems integration schemes include no coupling, loose coupling, semi-tight coupling, and tight coupling. Let’s examine each of these schemes:

No coupling: No coupling means that a DM system will not use any DW system functions. It may retrieve data from a specific source, process the data using data mining algorithms, and then store the mining results in a separate file.

Loose coupling: Loose coupling means that a DM system will use some DW system facilities, such as fetching data from a data repository managed by these systems, performing data mining, and then storing the mining results in a file or a designated location in a data warehouse.

Semi-tight coupling: Semitight coupling means that, in addition to connecting a DM system to a DW system, the DW system can provide efficient implementations of a few essential data mining primitives. For instance, sorting, indexing, aggregation, histogram analysis, and precomputation of some essential statistical measures.

Tight coupling: Tight coupling refers to the smooth integration of a DM system into a DW system. The data mining subsystem is one of the information system’s functional components. You can use data structures, indexing schemes, and query processing methods to optimize data mining queries and functions.

Data Warehouse Optimization

The Data Warehouse Optimization solution enables organizations to add more types of data, and more capabilities to their DW environments in a cost-effective manner. They can also store large amounts of data for longer periods to gain deeper insights.

Companies benefit from the numerous new and disparate data formats that are now available. As a result organizations gain the ability to keep up with faster incoming data speeds. Hence, enabling real-time analytics and faster response to new insights.

By utilizing the existing data warehouse for the highest-value analytical workloads, you can also avoid costly data warehouse upgrades. Also, you can reduce data and workload to lower the costs of hardware.

Extract-transform-load (ETL), extract-load-transform (ELT), and data cleansing tasks can be performed on lower-cost systems. After processing, the data can be reloaded into the data warehouse or delivered to data marts.

Benefits of Data Warehouse Optimization

Future-proofed data warehouse environment

With greater volume and velocity of data, you can gain more valuable insights
Make data more valuable by utilizing a wide range of data processing techniques
Make use of innovations from a diverse community

Increased productivity

Reduce latency of integrating, processing, and analyzing big data
Boost developer productivity for data processing by a factor of up to 5 times
Reuse existing analytical applications on the full read/ write platform

Proven production-readiness

You can rely on our extensive experience with manufacturing systems
Also ensure that strict SLAs are met
Maintain business continuity to avoid downtime costs

Data Warehouse Optimization Techniques

Data warehouses are traditionally data repositories optimized for data retrieval and analysis from systems, applications, and sources. It allows managers, executives, and other decision-makers to conduct data analysis.

Because the primary purpose of the data warehouse is to enable rapid querying of large and complex sets of data, the technology must be designed to support this purpose. As a result, four fundamental techniques are now included in data warehouse technologies to allow for extremely fast processing:

Columnar Data Storage

Unlike traditional databases, which store data in rows or records to limit the number of operations on data, cloud data warehouses frequently store data in columns or fields to improve READ query performance.

Instead of discarding unwanted data in rows, the database can more precisely access the data it needs to answer a query by storing it in columns. Columnar data storage for database tables significantly reduces the total number of I/O operations to disc storage.

It also reduces the amount of data that must be loaded from a physical disc. Columnar data storage data warehouse technologies ignore all unnecessary data in database table rows that do not apply to a READ query.

Database Compression

Database compression is used in data warehouses to save disc storage space by storing data on fewer database pages. Also, it involves reducing the number of bits required to represent data.

Because more data can be stored on a single database page, fewer database pages must be read to access the same amount of data. In general, data warehouses have large amounts of data redundancy and a large number of repeating values.

As a result, it allows for very effective database compression and very high data compression ratios. In general, the higher the database compression ratio, the greater the potential performance gains in READ queries.

Massive Parallel Processing

MPP is typically made up of independent processors that run in parallel to provide optimal query performance. This type of data warehouse optimization technique, also known as a “shared-nothing architecture,” is distinguished by a design in which each embedded processor or node is self-sufficient and controls its memory and disc operations.

READ queries are divided into smaller components, and each component is worked on independently and concurrently to produce a single combined result set.

When a query is issued, every node works at the same time to process the data contained within that node. This is known as massively parallel processing.

In-Memory Processing

Data processed in the cloud data warehouse is stored within system random-access memory rather than a conventional database management system that processes data stored on physical disks.

This allows the processing of queries from reads of system memory rather than reads from disk devices. Accessing data in memory eliminates both seek time and I/O operations when querying data.

This provides faster and more predictable performance than queries conducted from data residing on disk since retrieving data from disk storage is the slowest part of data processing. The fewer data that needs to be retrieved from the disk, the faster the data retrieval process.

Final Words

Cloud computing has transformed the business world by enabling businesses to easily retrieve and store valuable data about their customers, products, and employees.

Many multinational corporations have turned to data warehousing to organize data that flows in from corporate branches and operations centers all over the world.

Data warehouse platforms differ from operational databases in that they store historical data, making it easier for business leaders to analyze data over time.

Thinklayer provides data warehousing services under the guidance of its experts and gives your business a new dimension.

References:

Data Warehouse Development, Data Warehouse Optimization, Data Warehousing

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