The importance of data in business has become an undeniable issue. Increasingly, companies are realizing the relevance, not only of having quality data but also of having it organized and stored optimally so that it can be managed appropriately. In this regard, data storage is now a basic requirement in any company, and data integration is an essential resource. However, in many cases, companies are still unaware of the differences between a database and a data warehouse, something that is definitely important since a data warehouse does not fulfill the same functions or roles as a regular database.
A data warehouse, or DWH, is indeed a data storage process; however, unlike other databases, it is designed and prepared to enhance data analysis and data-driven decisions. A data warehouse can be hosted on a company's own server or in the cloud and receives information from various sources, typically enterprise management software, relational databases, individual files in various formats, and web platforms that collect data such as Google Analytics, Google Ads, or social media. Additionally, a data warehouse also stores metadata, which are the "parent data" used to prevent errors or ensure that data is updated correctly.
Therefore, any database is simply a place where a large amount of data is accumulated. In contrast, a data warehouse not only collects data but also prepares it to be easily worked with and promotes data analytics and business decision-making.
Most organizations work with or accumulate large amounts of data that have different structures and formats and, therefore, cannot be interpreted together, limiting their usefulness. What good is it to have data about our business activities, customers, or third parties if we cannot understand them?
The data stored in a data warehouse undergo a process of integration that normalizes and standardizes them to be compatible with each other. In other words, different formats and structures are adapted so that they can be interpreted together, as well as compared, filtered, and worked on using data analysis or data visualization tools like Power BI. Additionally, the data is verified and consolidated. In fact, the data warehouse is one of the key tools for carrying out a data consolidation process, which involves checking for discrepancies between data and ensuring that they are not duplicated or contain errors. Data consolidation also enhances data-driven decision-making, reduces operational costs, and ensures data quality.
In addition, to facilitate their analysis and interpretation, data is organized by themes and structured into processes, views, dimensions, and metrics. The rules and schemas governing the data are defined by those responsible for its management, and permissions for company users can also be administered.
Basically, a database includes raw, untreated data, whereas in a data warehouse, the data is ready to be processed or used. To create robust reports, graphs, or visualizations, having data stored in a data warehouse is essential.
Furthermore, one of the most valuable advantages of data warehouses is that they are non-volatile and time-variable data storage systems. In other words, the information stored in a data warehouse is not lost or modified. Once data is stored, it remains accessible forever and is opened in a read-only version so that no one can delete it. Additionally, the data warehouse collects new data as it is generated and adapts it to what it already contained. Therefore, it is the best way to have a historical record of all company data that is also updated automatically to perform temporal analysis, detect inefficiencies and correct them, and identify strengths and opportunities. Changes made to existing data are recorded to be reflected in new reports. Essentially, having a data warehouse promotes innovation and a company's business intelligence strategies.
The concept of data warehousing originated in the late 1980s thanks to IBM researchers Paul Murphy and Barry Devlin. Although they didn't invent data warehouses or databases, they were pioneers in developing a specific business approach to meet the information needs of organizations. Their innovative architecture was based on the flow of data from corporate operating systems to decision support environments.
Data warehousing has become a vital component for the smooth operation of businesses. It combines data storage capacity with the data-driven decision-making process.
In summary, the enterprise data warehouse arises from the integration of data warehouses and the process of informed decision-making. This combination allows organizations to access key information and base their decisions on reliable data and in-depth analysis.
The concept of a "data warehouse" has been closely linked to data-driven decision-making and the efficient use of corporate data from its inception. Essentially, it is a data storage designed specifically to meet the business intelligence and data analysis needs of organizations. It is commonly known as an Enterprise Data Warehouse (EDW).
More precisely, a data warehouse can be defined as a data storage and integration architecture that facilitates the organization, transformation, understanding, and management of data, as well as its subsequent use for making more informed business decisions. The creation and development of this architecture, along with associated operations, are known as "data warehousing," which involves collecting, integrating, and organizing data in a data warehouse.
Unlike other databases, the primary goal of a data warehouse is to streamline the transformation of data into valuable business information and provide easier access to company users. Instead of simply storing raw data, the focus of a data warehouse is on turning that data into relevant insights that drive informed decision-making.
A data warehouse generally serves as the central repository of an organization's data. After extracting data from its source systems and integrating it into the data warehouse, it undergoes a process of treatment, transformation, and organization into views, dimension tables, and fact tables. The most commonly used methodology for this purpose is the ETL (Extract, Transform, Load) process, or more recently, ELT (Extract, Load, Transform).
Once the data has been transformed and organized, users can access it through SQL queries, business intelligence tools like Power BI, customer management platforms like a CRM, among others. The data warehouse provides an abstraction layer that facilitates structured and coherent data access, enabling users to obtain relevant information for business decision-making.
A data warehouse is characterized by its architecture, which consists of various layers that interact with each other and with the data.
The classic architecture of a data warehouse consists of 3 layers:
Extraction Layer (Bronze): In this layer, also known as the Staging layer, data is extracted from its source systems, typically using SQL scripts or other extraction techniques.
Integration Layer (Silver): In this layer, data from different sources is integrated into the data warehouse. After being stored, the data is transformed and modeled using star or snowflake schemas. It is then loaded into an OLAP (Online Analytical Processing) server for further analysis and use in decision-making.
These initial two layers are often implemented through the ETL (Extract, Transform, Load) process, which involves the extraction, transformation, and loading of data.
More and more companies are choosing to store their data in cloud data warehouses for several reasons. Some of the key reasons include greater speed, scalability, lower initial investment, and significant savings in maintenance costs.
Cloud data warehousing, whether public or private, not only offers greater agility to organizations but also enables the adoption of new data flows and types of analysis that redefine the traditional concept of a data warehouse.
Furthermore, cloud data warehouses can improve query and transformation speeds by leveraging parallel processing (MPP, Massively Parallel Processing).
It's important to note that, like any technology, data warehousing is in constant evolution, and most cloud data warehouse providers now consider scalability as a basic requirement.
Having control over the data assets we possess is fundamental. Data has already become one of the primary raw materials for an organization as it contains valuable information that enables companies to optimize their strategies and strategic actions.
In this regard, data integration is a necessary process to harness the power of data
Data Integration for Data-Driven Decisions
To harness the value of data, the first step is to collect it. Data represents information that can become crucial when analyzing a company's performance and identifying the needs and behavior of our consumers, subsequently enabling data-driven decision-making. Data is now one of the most important assets for the business decision-making process. However, many companies still fail to tap into the potential of the data they have because data is often scattered across fragmented silos or not properly handled. In this regard, data integration emerges as the solution to this issue.
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What is data integration?
Data integration is a strategic process based on unifying all of a company's data, typically stored in different data sources accessible only to certain individuals within an organization. Data integration involves bringing together all business data into a single data warehouse.
Far from being a simple procedure, data integration encompasses many practices beyond moving data from one place to another. A well-crafted data integration process involves the transformation and consolidation of data assets. In stages, data is collected and stored in a provisional data source where data scientists proceed to clean it—excluding unnecessary information or data containing errors—filter it—checking its reliability and relevance according to business logic—and consolidate it. Furthermore, data is converted into a common format to make them compatible with each other and analyzable as a whole, as well as performing aggregations and combinations if necessary.
Once the data is prepared to be used, it is loaded into the data warehouse for inventory purposes. A data warehouse should have the capability to automatically update the data over time.
This process allows analysts to obtain new and reliable information about business activity, customers, processes, operations, etc., which is often unknown to the entrepreneurs.
The Importance of Data Integration in the Business Environment
Data integration is one of the fundamental processes for any company that doesn't want to fall behind. As we've already mentioned, if data is not integrated, it will be practically impossible to leverage it. Having a wealth of information is of no use if this information is inaccurate, erroneous, or cannot be analyzed as a whole.
Let's illustrate this with an example. Imagine a company is preparing to launch a new product or service. To ensure that the new product or service doesn't fail, the ability to analyze key information about previous products or services will be crucial. Additionally, the company will probably want to analyze the products of competitors, study their buyer personas, and so on. Organizations often have this type of information, but it's stored in different places because each department stores the data it needs in its own repository.
However, this is a mistake and can lead to failure due to the inability to obtain a comprehensive view of the situation and analyze all aspects at play in order to draw clear conclusions and make the right decisions.
After a data integration process, all valuable information is unified and integrated into a single place, and all departments and stakeholders in the company can access it. Furthermore, the process ensures that the information is not only accurate but also useful and contains valuable insights for the business. It is precisely for this reason that data integration promotes the generation of new business opportunities, better decision-making, and increased productivity and business performance. It is, therefore, the optimal way to generate business intelligence to reduce risks, avoid errors, and achieve optimal results.
How to Carry Out a Data Integration Process?
There are multiple ways in which the process can be performed. Bismart, as a preferred Microsoft Power BI partner in Spain, often opts for the ETL (Extract, Transform, and Load) process using Microsoft SSIS (SQL Server Integration Services) technology. However, we adapt the process to the specific needs of each business.
What are the benefits of data integration?
Data integration offers numerous business advantages, some of which have already been mentioned. However, the most prominent ones include:
Time Savings: The data gathering process consumes between 60% and 80% of the time organizations dedicate to business intelligence. Data integration automates this process, significantly reducing the amount of time data scientists or analysts spend on tasks that can be automated. One of the strengths of data integration is that information is always ready for analysis.
Cost Savings: Eliminating unproductive and unnecessary tasks translates into freeing up human, physical, and operational resources. All of this results in substantial cost savings and an increase in ROI (Return on Investment).
Reliable and Useful Information: When data integration is done correctly, it ensures that the data or information available to the company is entirely reliable and useful for achieving established business objectives.
Historical Record of Information: A data warehouse serves as a historical inventory that updates automatically and can be reviewed over periods of time.
Promotes the Creation of New Strategies and Business Opportunities: When a company invests in data integration, it becomes an unstoppable engine that drives the development of new business strategies and the generation of new business opportunities. Having more effective information translates into making better decisions, enriching strategic operations, reducing risks, and promoting more timely actions.
In addition to the ones listed here, data integration offers many other benefits, including time and money savings, the discovery of previously unknown information, data accessibility at all times, and readiness for analysis. What more could one ask for? Investing in data integration is investing in the future!
Some of the key benefits of data integration are:
Time Savings: Data collection typically takes up a significant portion of an organization's business intelligence efforts, accounting for between 60% and 80%. By automating this process through data integration, the time required is significantly reduced, eliminating unnecessary tasks. This ensures that information is ready to be used more quickly and efficiently.
Cost Savings: By eliminating unnecessary tasks and optimizing human and physical resources involved in data management, significant cost savings can be achieved. Data integration allows for process efficiency improvements, reducing unnecessary expenses and enhancing overall productivity within the organization.
More Reliable and Efficient Information: Data integration ensures that the obtained information is accurate and reliable. By systematically processing and filtering data, the quality of information is improved, leading to more informed and accurate decision-making.
Historical Data Record: Centralizing data in a single storage location creates a comprehensive historical record of the organization's information. This provides a panoramic view of the organization's development and evolution over time, facilitating retrospective analysis and the identification of relevant trends and patterns.
New Business Strategies: Data integration can drive business initiatives by providing new information and deeper insights into all aspects and processes of the company, as well as its customers. By enriching strategic operations with integrated data, decision-making can be improved, internal processes optimized, and risks associated with business actions reduced.
In summary, data integration offers numerous benefits, including time and cost savings, reliable and efficient information, a complete historical record, and the promotion of new business strategies. By leveraging these advantages, organizations can enhance their performance and competitiveness in the market.
As previously mentioned, a data warehouse offers many competitive advantages compared to a regular database. In a nutshell, a data warehouse ensures data quality and conditions it for easy data analysis. In fact, having a data warehouse significantly reduces the time required for data analysis, reporting, visualization, or dashboards.
Furthermore, data in a data warehouse is more secure and protected, more reliable and accurate, and is transformed into more valuable information and insights. Therefore, storing data in a data warehouse enhances a company's responsiveness and optimizes data-driven decision-making by providing high-quality, up-to-date, and reliable data. Making better business decisions and accurately understanding the company's situation translates to cost savings, increased revenue, a higher return on investment, and better customer service.
On the other hand, the integration, normalization, and consolidation of data promote cooperative work among different departments, which will no longer have different information sources and can work together. A data warehouse helps ensure that all business departments have access to the same information and can collaborate, compare data quickly, and access external information relevant to their department. This prevents confusion and misunderstandings. Additionally, data warehouses make data management an agile and intuitive task, designed so that non-expert users can access, query, or extract data without the risk of accidentally deleting or altering data due to lack of knowledge.
Furthermore, storing data in a data warehouse guarantees that data is protected, won't be lost, won't undergo irreversible changes, and that all information is accurate.
In today's business environment, databases play a fundamental role in information management to achieve competitiveness. However, as a company grows and seeks expansion, the question arises of whether to continue using a database system or transition to a data warehouse.
When is the right time to make this transition?
As a company accumulates large volumes of data from various sources and the need for multiple tasks and analyses with this information becomes pressing, maintaining different scattered databases can become a competitive obstacle. The need to perform independent queries in each database, without the ability to cross-analyze smoothly, is inefficient, slow, costly, and poses security risks.
When data integration becomes crucial for a company's development and expansion, leading systems analysts recommend implementing a data warehouse.
Let's look at some basic differences between a regular database and a data warehouse:
Designed to store data from an unlimited number of sources.
Efficient for analyzing and aggregating large volumes of data.
Allows for quick data visualization and generating reports from complex data.
Initial implementation is more costly and labor-intensive.
An ideal tool for studying a company's evolution and making medium to long-term projections.
In summary, as a company grows and needs to manage large volumes of data more efficiently, a data warehouse becomes a recommended solution. It provides advanced capabilities for analysis, integration, and reporting, which are essential for long-term success and strategic decision-making.
You may be wondering if your company needs a data warehouse. For all the reasons we've mentioned, the answer is yes, but let's take a closer look at specific situations that might be a signal that you should take the plunge:
My company has a large amount of disparate data stored in different places that are not interconnected.
I need to have historical records of information, perform temporal analyses, or compare information from different time periods.
It's very complicated or laborious for me to make comparisons between data of different themes, characteristics, or formats because they are stored in different places.
I'm unsure if my data is validated, if it contains errors, or if I can trust it 100%.
My company needs to perform real-time data integration from various sources.
Different departments in my company have different versions of business activity, find it difficult to make joint decisions, and do not cooperate.
The stored data is not secure, and querying or using the data for analysis could result in data loss or induce errors.
I have a large amount of data, so analyzing it, extracting clear and reliable insights, and creating data visualizations are extremely challenging tasks that require experts and a significant amount of time.
If you identify with one or more of these statements, you should consider the benefits of acquiring a data warehouse and the many problems it could help you avoid.
A data warehouse encompasses the entire data processing process, from data collection to importing it into a reporting or visualization program like Power BI. Additionally, there are multiple platforms or applications that allow for the implementation of a data warehouse, but one of the most comprehensive options is Azure Synapse, an alternative to Azure Data Bricks and SQL that enables you to complete the entire process within a single application.
A data warehouse can have different architectures depending on how it is developed, but it typically consists of four layers: staging, core, data mart, and reporting, in that respective order.
The staging or data sources layer involves the process of transferring 'raw' data from its source systems (its original places of origin) to the data warehouse. The various source systems are stored in the data warehouse within the Staging layer using a different schema, but no relationships are applied between them, and the data is not transformed.
In the transform or core layer, data integration takes place, along with cleaning, normalization, standardization, and validation. During this stage, data is also classified into concepts so that relationships and rules can be established among them.
Finally, in the data mart layer, rules and relationships are applied among the different conceptual groups defined earlier, and calculations and aggregations are performed to present the data effectively in analytics and reporting platforms.
These layers serve the functions of an ETL process (extract, transform, load).
The data exploitation or reporting layer refers to the bridge established between the data warehouse and data analysis programs such as Power BI or Excel. This bridge establishes connections from both on-premises servers and cloud-based servers.
In conclusion, a data warehouse is nowadays an indispensable tool for any expansion-oriented company that wants to stay competitive and respond to the rapid and unexpected changes in today's volatile market.
The relationship between a data warehouse and business intelligence (BI) is fundamental to an organization's information system. Although the literal translation of "data warehouse" to Spanish is "almacén de datos" (data storehouse), it is not simply a conventional database. The main difference lies in its data processing and integration capabilities. Let's explore why the data warehouse and business intelligence are closely intertwined.
Companies need spaces to store their data assets, but the relationship between a data warehouse and business intelligence goes beyond that.
One of the primary challenges preventing companies from harnessing the real value of data and turning it into intelligence is data fragmentation. This occurs when different departments or business units within a company store information in various data warehouses that are incompatible with each other, making integration and knowledge sharing difficult. Furthermore, this fragmentation creates data silos that cannot be shared.
In 2017, the American consultancy firm Gartner conducted research on why companies do not adopt a data-driven approach. Fifty-two percent of the surveyed executives mentioned that fragmented data silos hindered them from sharing information, and 33% acknowledged that their company lacked the necessary data management technologies. Fortunately, the market has rapidly responded to new business needs with more advanced technologies, such as the consolidation of Customer Data Platform (CDP) data in recent years.
In the business environment, it is common for departments to operate with legacy systems and platforms that are not integrated with each other. This makes it difficult to create a solid and comprehensive foundation for business intelligence.
The data warehouse was designed to address this issue by directing data flows from all corporate sources to a central repository, the data warehouse. This allows anyone within the organization to access data easily.
On the other hand, an efficient data warehouse can accelerate the data preparation and analysis process, promote data security, and ensure compliance with data protection regulations.
Below are the essential steps for building a solid foundation for business intelligence through a data warehouse.
Identification of Data Sources: The first step in setting up a data warehouse is to determine which data you want to collect and locate the original sources where it resides so that it can be transferred to the data warehouse or its subcategory, the data mart.
This step is crucial as it forces managers to consider what they want to achieve with the data, what data they need to achieve their objectives, and what data assets can be leveraged.
ETL: Extraction, Transformation, and Loading: Once the desired data to centralize and its storage location are identified, the process of extracting, transforming, and loading the data, known as ETL (Extract, Transform, and Load), is carried out.
ETL is an essential part of the process as it not only extracts the information to be loaded into the data warehouse but also cleanses and consolidates it to ensure data quality and consistency across all databases, regardless of the source system.
In summary, ETL is the necessary processing to turn raw data into usable data, ready to be used by data analysts, data scientists, business intelligence consultants, or other users.
Today, most ETL processes are automated and promote data quality and governance.
Like any technology, in recent years, the ETL process has evolved into a new perspective: ELT, which changes the sequence of "transform" and "load."
Business Intelligence (BI): Once data has been transformed, validated, cleansed, and consolidated and has been loaded into the data warehouse, it is ready to be converted into knowledge through business intelligence tools.
Business intelligence tools allow users to transform data into information, and information into insights or, in other words, into intelligence. These tools include reporting systems like Power BI, data visualization platforms, dashboard development, and corporate reporting, among others.
The leading business intelligence tools on the market, such as Microsoft Power BI, a leader in Gartner's 2022 Magic Quadrant for Analytics and BI platforms, have been designed with a business perspective and enable users with limited technical knowledge to work with data and turn it into more informed decisions. Ensuring that end users receive the information they need appropriately is the foundation of business intelligence and makes a difference in terms of data utilization.
Is it necessary to have a data warehouse to generate business intelligence? Short answer: Yes.
While some companies manage to generate business intelligence without a data warehouse, this approach has several disadvantages in terms of performance, time, and costs. Processing the data needed for business intelligence without a data warehouse can put pressure on transactional databases, reduce performance, and increase load times, slowing down the process of transforming data into intelligence.
Furthermore, the lack of adequate infrastructure for data and system integration poses numerous issues, as mentioned earlier.
In summary, transactional databases cannot perform the same tasks as a data warehouse, and their ability to generate business intelligence is limited. It is no coincidence that 48% of organizations consider their business intelligence environment "critical" or "very important" for their long-term productivity.
Data lakes and data warehouses are widely used for big data storage, but they are not interchangeable terms. A data lake is a vast collection of raw data that doesn't yet have a defined purpose. On the other hand, a data warehouse is a repository of structured and filtered data that has been processed for a specific purpose. While both store large amounts of data, it's important to distinguish between them as they serve different purposes and require different approaches for optimization.
Some of the key differences between a data lake and a data warehouse include data structure, processing methods, scope of use, and data purpose.
A data lake stores raw, unprocessed data without a specific purpose. End users are often data scientists, and data accessibility is high. In a data lake, due to its easy accessibility, data can be updated quickly.
On the other hand, a data warehouse contains processed data used for a specific purpose. End users of a data warehouse are typically business professionals, and making changes to its structure can be more complex.
Each type of storage offers distinct benefits. The main difference lies in the variable structure of raw data compared to processed data. Data lakes, by storing raw data, require more storage capacity than data warehouses. The advantage of having raw data is the ability to analyze it quickly and for any purpose. However, without proper data quality and governance measures in place, data lakes can become challenging and of little value to manage.
Data warehouses also offer interesting benefits. Storing only processed data saves storage space, translating into cost savings. Furthermore, processed data is more understandable and accessible to a less technical audience.
Beyond their storage purpose, these two concepts are quite different. Data lakes, due to the unstructured nature of their data, can be complex to navigate and require the involvement of data scientists. On the other hand, data warehouses are better suited for business use by less technical users. Each company should evaluate with experts which of these storage types best suits their needs based on the intended use.
In the business world, databases are fundamental tools for any organization. However, it's common for only those responsible for data to understand the differences between various types of databases. Let's explore the basic differences between a data warehouse and a data mart in a way that's understandable for non-technical individuals.
What Is a Data Mart?
A data mart is a subset of a database, typically a data warehouse, where data is stored for a specific area of the business. In other words, a data mart contains concise and specific datasets intended for analysis by a particular department or business line, such as the sales department.
The data mart focuses on specific queries, and like in a data warehouse, the data has a clear structure, often in star or snowflake dimensional models. The purpose of using a data mart is to index data to facilitate queries in specific areas of the business and meet the needs of a specific group of users within the organization, such as the sales or finance team.
The main difference between the two databases lies in their scope. While a data warehouse serves as the global database for a business and stores data related to any aspect of the company, a data mart stores a limited amount of data with a thematic focus, related to a specific department or business line. Additionally, a data warehouse collects data from various sources, while a data mart typically gathers data from the central data warehouse.
This implies that a data warehouse has much greater storage capacity than a data mart and requires a more complex and challenging design architecture. Furthermore, the implementation of a data warehouse is a costly and lengthy process, often taking several months or even a year. On the other hand, the implementation of a data mart can be completed in a few months, as it involves a much smaller amount of data and a simpler structure.
To illustrate this difference, we can use an example from the education sector. A data warehouse would be like the place where all the documents of an educational institution are stored, while a data mart would be the place where each teacher or group of teachers stores relevant documentation for their subject.
Next, we will explore in more detail the main distinctions between a data warehouse and a data mart based on different criteria.
In summary, a data warehouse is a central database with the ability to connect to various data sources and a large storage capacity. On the other hand, a data mart is a subdivision of a data warehouse, with a smaller storage capacity and focused on providing answers to user queries related to a specific area of the business.
An inadequate design of a data warehouse can lead companies to base their knowledge on incorrect data, affecting business performance analysis and potentially resulting in erroneous decision-making. In this article, we will explore the four essential steps for designing an effective enterprise data warehouse.
The initial phase of designing a data warehouse involves analyzing business requirements and needs, taking into account the fundamental tasks for the successful operation of the business. It is crucial to involve decision-makers and technical professionals in a collaborative approach. Furthermore, it is important to ensure that all departments participate in defining and establishing the business objectives that the data warehouse must meet.
During this phase, it is essential to ask important questions such as the purpose and business objectives of the data warehouse, prioritized information, data sources to integrate, and the availability of a backup system in case of failure.
A data warehouse requires three distinct physical environments: development, testing, and production. It is essential to design each environment with dedicated servers to achieve efficient workload management, allow for pre-production testing, and prevent server downtime during testing. Proper configuration of the physical environment contributes to the stable operation of the data warehouse.
Once the data warehouse environment is designed, front-end operations can be performed to allow users easy access to data. In this step, you can choose to use pre-established business intelligence kits or develop custom tools. Specific data columns should be selected, and the limitations of the OLAP provider should be considered to optimize queries and overall data warehouse performance.
After completing both back-end and front-end operations, it's time to implement the data warehouse and train users. Proper team training is essential to ensure smooth operation and the achievement of business objectives. During this phase, it is important to monitor the performance and operation of the data warehouse, identify any issues, and perform regular maintenance. Additionally, regular backups should be taken, and future upgrades should be planned to adapt to evolving business changes and requirements.
It's worth noting that each data warehouse is unique, and its design should be tailored to the specific needs of each business. Additionally, the design and implementation process may vary depending on the business environment in which the data warehouse is deployed.
Automation in the realm of data warehousing is cutting-edge technology that relies on advanced design patterns and processes to automate the planning, modeling, and integration stages throughout the entire lifecycle of the data warehouse. Its aim is to provide an efficient alternative to the traditional data warehouse design approach, reducing time-consuming tasks such as ETL code generation and implementation on a database server.
The data warehouse automation process consists of several steps:
Data Extraction: In traditional data warehouses, data is extracted from relational databases using SQL scripts. Before being transferred, data undergoes cleansing to ensure data consistency and accuracy. During this stage, data is based on a relational model of entity relationships and is primarily used for online transaction processing.
Analytical Data Storage: Transactional data is modeled into star or snowflake schemas and transferred to an OLAP (Online Analytical Processing) server through a relational OLAP or multidimensional data model. This structures and simplifies the data for use in analytical reports and queries. The data is then transformed and loaded into the data warehouse.
Analysis and Reporting: Once the ETL (Extract, Transform, Load) processes are complete, data from the data warehouse is exported to business intelligence and analytics tools to gain insights that support decision-making.
Data warehouse automation software offers a seamless, programming-free approach to aggregate and move enterprise data from various sources into a data warehouse and beyond. Unlike traditional data warehouses, this software automates the batch execution and ETL code deployment tasks required in the data storage process. Built on agile methodologies, automation software employs various functionalities such as denormalized, normalized, and multidimensional data structures, ETL and ELT data integration processes, source data modeling, and connectivity to multiple data providers.
The ETL phase plays a pivotal role in the data warehouse as it maximizes its value. While the data warehouse acts as the storage place for all data, and business intelligence tools are used for consumption and providing insights, ETL serves as an intermediary by moving data and tools from source systems to the data warehouse for analysis. The ETL stage is where much of the business's time and energy are devoted in developing a data warehouse solution.
When it comes to cloud-based ETL, it involves extracting data from various source systems, transforming it into a common format, and loading consolidated data into a cloud storage platform to meet business intelligence, reporting, and analysis needs. Working in the cloud offers a variety of benefits, such as real-time streaming and rapid integrations. Moreover, it is the preferred choice if your data warehouse is in the cloud.
The adoption of cloud-based ETL has been a transformative process linked to the evolution of internet speed and capabilities. The most notable and appreciated advantage of cloud-based ETL is its increased speed. The cloud environment allows ETL process computing tasks to be performed much faster and optimizes business intelligence activities, which, in on-premises environments, can slow down due to the constant growth of data volumes that companies work with. Additionally, implementing cloud-based ETL processes is often quick, as it can easily connect with both on-premises and cloud services.
In summary, data warehouse automation is cutting-edge technology that streamlines data integration, automates time-consuming processes and tasks, and ensures interoperability of business systems in a cloud environment that optimizes business intelligence operations.
At Bismart, we have been working on and implementing data warehousing solutions for many years. One of our strengths is that we apply data warehouse processes tailored to the characteristics and needs of each company. Not all businesses have the same type or quantity of data, and they do not use it for the same purposes. Therefore, we ensure to find the best personalized solution that adapts to our client's specific requirements. Additionally, one of our core values is to promote data-driven decision-making rather than relying on hypotheses. We work to provide the best solutions to make this a reality. Furthermore, we have other competitive advantages compared to other companies offering the same service:
Microsoft Guarantee: Bismart is one of the few Microsoft Power BI partners in Spain, and as such, Microsoft recognizes us as a leading provider of solutions related to data analysis, management, reporting, and data visualization.
Kimball Methodology: Our experts work using the Kimball methodology, which is dedicated precisely to the development of data warehouse projects.
Data Quality: Data quality is one of our primary concerns because years of experience have shown us that working with high-quality data is a guarantee of improved performance and enables expansion and innovation. Therefore, we ensure data quality in all our data collection processes.
Rigorous Data Model Creation: We pay special attention to creating simple and understandable data models. Our goal is to avoid any confusion about the meaning and purpose of the entities and fields used in reports.
Careful Report Presentation: We strive to create attractive, interactive, and user-friendly reports. We adapt themes and visual elements to the style and corporate image of each company.
Consideration of Future System Operation: In all our projects, we take into account the technological and functional evolution of the platform. We ensure that the system is scalable and manageable as needs change over time.
Continuous Updating of Our Solutions: At Bismart, we stay up to date with the latest industry trends. We have extensive experience in Big Data solutions and Modern Data Warehousing, which integrate different types of data, including structured, unstructured, and semi-structured data, using technologies such as Microsoft Azure Data Factory, Microsoft Azure Data Lake, and Azure Blob Storage. We are committed to applying the latest innovations for the benefit of our clients.
In summary, at Bismart, we focus on best practices, use robust methodologies, ensure data quality, create understandable data models, present attractive reports, and consider the future evolution of the system. Furthermore, we stay updated with the latest solutions and technologies in the market to provide our clients with the best in Business Intelligence and Big Data.