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ETL Data Warehousing for 2024 Data Management and Analysis

ETL Data Warehousing for 2024 Data Management and AnalysisETL Data Warehousing for 2024 Data Management and Analysis

Figure 1. ETL vs. data warehouse.1

Since 2004, ETL (extract, transform, and load data) and data warehouse have been frequently, similarly, and correlatively searched (Figure 1). This is because ETL and the data warehouse are interrelated terms. The ETL pipeline is a process used for integrating data from multiple sources into a central repository, while data warehousing is the process of storing and managing this data in a way that makes it easy to access and analyze. Together, ETL and data warehousing are critical components of modern data management, allowing businesses to extract valuable insights from the vast amounts of data they collect.

The relationship between ETL and the data warehouse is not always well understood. ETL and data warehouses have been regularly searched on Google since 2004 (Figure 2). To clarify the relationship between ETL and data warehouse we explain: 

  • The process of data mapping 
  • Data warehouse architecture
  • ETL and data warehouse best practices
  • Future of ETL and data warehousing 

Figure 2. Interest in ETL data warehouse.2

1. Extracting data for data warehousing

The figure illustrates the basics of ETL, which is the extraction of data from source systems and loading them into the data warehouse.

Figure 3. The data source systems to the data warehouse source system.

Extracting data is the first step in the ETL process. Data extraction often involves extracting data and pulling data from a variety of data sources, such as databases, application programming interfaces (API), and flat files. Some common techniques for data extraction include batch processing, real-time streaming, and change data capture.

It is important to handle data quality issues during the extraction process, such as ensuring that all the data used is complete and accurate. Additionally, when dealing with large volumes of data, it may be necessary to use techniques such as parallel processing and distributed computing to manage the load of data extracted well.

2. Transforming data for data warehousing

After the data has been extracted, it must be transformed into a format suitable for the data warehouse. This could entail cleaning and filtering the raw data itself, as well as aggregating it to generate more meaningful insights. During this stage, data quality issues such as removing duplicate records and standardizing data structures across different sources can also be addressed.

3. Loading data into the data warehouse

The final step in the ETL process is data loading into the data warehouse, which involves moving the transformed data into the central repository. Data can be loaded using a variety of methods, including bulk loading, incremental loading, and trickle loading. Compression and partitioning techniques, as well as optimized database schema designs, are best practices for optimizing performance loads data and minimizing data load times. Using error logs, data profiling, and data quality dashboards are some strategies for handling errors and data validation during the loading data integration process.

4. Designing the data warehouses

Designing a data warehouse requires careful consideration of data modeling and dimensional modeling techniques. The data modeling process involves designing the schema and tables for the target database or data warehouse itself. On the other hand, the dimensional modeling process involves designing the structure and relationships between dimensions and fact tables. Strategies for optimizing query performance and data access include using indexing, partitioning, and materialized views.

5. ETL tools and technologies

There are many ETL tools and technologies available in the market, both commercial and open source. Some popular open-source ETL tools include Informatica, Talend, and Apache NiFi. Choosing the right ETL tool depends on factors such as the organization’s budget, data volume, and technical requirements. Different ETL approaches include using code-based ETL frameworks, graphical ETL tools, and cloud-based ETL platforms.

6. Data warehousing architecture

Figure 4. Data warehouse architecture.3

There are several different architectures for data warehousing, including:

  • Traditional
  • Cloud-based
  • Hybrid

1. Traditional data warehousing

In a traditional data warehousing architecture, the data warehouse is hosted on-premises, meaning that the hardware and software required to manage and maintain the data warehouse are located within the data scientists’ or organization’s own data center. The data lake or organization is responsible for managing and maintaining the data warehouse infrastructure, which can be costly and require significant IT resources.

The data warehouse is a target system designed to mine data engineers’ support business intelligence (BI) activities such as data mining, reporting, and online analytical processing (OLAP). To optimize query performance, data in the data warehouse is typically organized using a dimensional modeling technique, which involves separating data into fact tables and dimension tables.

2. Cloud-based data warehousing

Cloud-based data warehousing is a modern way to build and manage data warehouses using cloud computing technology. Some popular cloud-based data warehousing solutions include Amazon Redshift, Google BigQuery, and Microsoft Azure Synapse Analytics. These solutions typically have multiple data sources that offer features such as parallel processing, columnar storage, and compression to optimize query performance, improve data quality, and reduce data storage costs. Cloud-based data warehouses can provide the following benefits:

  • Reduced costs: The cloud data warehouse itself is hosted on a cloud infrastructure, meaning hardware and software are provided by a third-party cloud service provider. This reduces the need for organizations to purchase and maintain their data warehouse admins’ own hardware and software, saving costs.
  • Scalability: Cloud-based data warehousing solutions can be easily scaled up or down based on the organization or target system’s needs, which means that it can handle fluctuations in data volume and processing power.
  • Flexibility and accessibility: Cloud-based data warehousing also provides flexibility and accessibility for organizations. Users can access the data warehouse from anywhere with an internet connection, and cloud-based data warehouse server solutions often offer advanced features such as automatic backups, disaster recovery, and data encryption.

3. Hybrid data warehousing

The hybrid data virtualization architecture combines traditional and cloud-based approaches. It utilizes on-premises and cloud infrastructure based on budget, data volume, and technical requirements. The data warehouse enables organizations to reap benefits from both approaches.

The on-premises component handles sensitive data, while the cloud-based component offers flexibility and scalability to handle fluctuations in data volume and processing power. Cloud-based data warehouses services, such as Amazon Redshift or Google BigQuery, are common, as are cloud-based ETL tools, such as Apache NiFi.

Video 1: Disadvantages of traditional data warehouses and benefits of cloud-based and hybrid data warehouses.4

7. ETL, data warehousing, and data analysis

Figure 5. ETL, data warehouse, and data analytics.5

Data and source data warehousing and the ETL processes are critical components of any data analytics strategy because they lay the groundwork for effective data analysis. Data analytics is the process of extracting insights from data using tools and techniques such as data visualization, statistical analysis, and machine learning. To effectively perform data analytics, organizations require a centralized and reliable source of data, which is where data warehousing comes in.

Organizations can store and manage enormous amounts of data in a central repository with data warehousing. ETL is used to extract data from databases and web services, transform it into a standard format, and put it into the data warehouse. Once the extracted data is in the data warehouse, it can be used for a variety of data analytics tasks, such as data mining, reporting, and predictive modeling.

8. Best practices for ETL and data warehousing

Implementing ETL and data warehousing solutions can be complicated and difficult, necessitating careful planning and execution. To ensure the success of an ETL and data warehousing project, organizations should follow best practices such as:

  • Creating a clear set of business requirements and project goals
  • Conducting extensive data analysis and modeling prior to designing the data warehouse
  • Following an ETL process that is standardized, such as the Kimball methodology
  • To ensure data accuracy, use appropriate data validation and cleansing techniques.
  • Putting in place robust error handling and logging mechanisms
  • Testing and validating the ETL and data warehousing processes on a regular basis
  • Establishing a data governance framework to ensure data quality and compliance
  • Providing appropriate end-user training and support

By adhering to these best practices, organizations can increase the likelihood of a successful ETL and data warehousing implementation while also ensuring that the data warehouse provides valuable insights that can inform business decisions.

9. Future of ETL and data warehousing

As organizations generate and collect massive amounts of data, the importance of ETL and data warehousing will only grow. Cloud-based data warehousing solutions are expected to grow in popularity as they provide greater scalability, flexibility, and cost-effectiveness. Machine learning and AI improvements are also likely to have an impact on the future of ETL and data warehousing, since they can help organizations automate tasks like data integration, data cleaning, data transformation, and data analysis. ETL and data warehousing can be critical components of any organization’s data management and analytics strategy.

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Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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