What are the differences between ETL, ELT, and EAI? A thorough explanation of the key points for optimizing data integration platform
data integration platform is essential for utilizing the wide variety of data generated both inside and outside a company. Every department generates a huge amount of data every day, and if this data is not connected effectively, it can hinder business efficiency and decision-making.
ETL, ELT, and EAI are methods for achieving data integration and linkage using different approaches. The appropriate method must be selected depending on the application and environment.
In this article, we will provide a detailed explanation of the overview of ETL, ELT, and EAI, as well as iPaaS as a related technology, and points to check when implementing them.
What is ETL? - Understanding the basic process in three steps -
Let's take a look at the mechanisms and procedures of ETL, which has long been used in data warehouses and BI tools.
ETL stands for Extract, Transform, Load, and refers to the batch processing process by which companies collect information from various data sources, transform it into the required form, and then load it into a storage location such as a data warehouse. Historically, this method has been used primarily in on-premises environments and is optimized to make it easier to handle large amounts of data.
By implementing ETL, it becomes possible to process large amounts of data at regular intervals and store it in a standardized format. Because the work to ensure the accuracy and quality of the data is done in advance, it is easy to prepare the data in a state suitable for analysis. Another advantage is that there are many established tools and frameworks, making operation stable.
On the other hand, when near real-time data processing is required, it can be difficult to achieve timely integration using ETL alone.As the number of situations where business requirements call for the quick ingestion of new data increases, there is an increasing awareness of combining ETL with more flexible mechanisms.
▼I want to know more about ETL
⇒ ETL｜Glossary
Extract: The key points of data collection
The first step, Extract, is the process of extracting the necessary data from multiple sources, such as internal systems and cloud services. For example, to aggregate data from various sources, such as sales management systems, marketing automation, and social media APIs, appropriate connectors and API controls are essential.
During the extraction stage, it is common to run batch processing periodically, taking into consideration the load on the source system. For databases with continuous transactions, using a mechanism to efficiently retrieve changes (incremental extraction) can optimize processing time and resources.
It is also important to specify the extraction range accurately. By clarifying which table and field you want to target, you can avoid including unnecessary data and improve the efficiency of the final conversion.
▼I want to know more about the API
⇒ API｜Glossary
Transform: The secret to improving data quality
The Transform process converts the data collected by Extract into a unified format and standard. By harmonizing character codes and date formats that differ between systems, correcting error values, eliminating duplicates, and cleaning data, high-quality data is generated.
When developing conversion logic, it is important to take into account internal company rules, such as referencing master tables to ensure consistency with business rules. Maintaining the internal consistency of the data will help prevent unnecessary error detection and duplication of work in the subsequent analysis stage.
High-quality data provides the foundation for decision makers to conduct more accurate analysis. Establishing a system for regularly checking for missing data or data anomalies can help prevent a decline in quality.
Load (Write): The process of incorporating into the data utilization platform
The Load process loads the data that has been processed in Transform into its final storage destination, such as a data warehouse. A system must be established to ensure that data is updated accurately after passing through an approval flow, and retry functions and log acquisition must be implemented in case of any problems that may occur when writing.
Typically, data is loaded into an on-premises data warehouse or a cloud-based data lake. Recently, an increasing number of companies are creating replicas in the cloud to handle large volumes of data and distribute analytical workloads.
Once this final step is stable, data integration can be performed continuously through periodic batch processing, keeping the latest or nearly latest data available for analysis. Careful management of logs and error reports for the Load process is also essential to make it easier to isolate system failures.
What is ELT? - Understanding the differences with ETL -
We will clarify the basic concepts of ELT, which is gaining attention in the cloud era, and the differences between it and ETL.
ELT is an approach to data processing that follows the order of Extract, Load, and Transform. Since the data is first stored and then converted and processed all at once using the processing power of the database, it is said to be well suited to large volumes of data and cloud environments. The main difference is that ETL loads after transformation, while ELT transforms after loading.
The popularity of cloud data warehouses is due to their ability to flexibly expand computing resources. ELT leverages the scalability of these platforms to efficiently execute large-scale transformations, even in batches.
However, storing a large amount of unconverted data in the database may affect existing queries, and advanced SQL and database skills may be required, so in actual operation, it is essential to consider the structure of the development and operation team.
Benefits and Use Cases of ELT
The biggest advantage of ELT is its speed of ingestion and flexibility, as it allows you to quickly load large amounts of data and then transform them all at once. Deferring the transformation process can speed up initial data entry, especially if you're not using it for real-time analysis.
Another advantage is that data scientists and analysts can use SQL to quickly access data and conduct trial and error. In environments that handle big data or when performing preprocessing for machine learning, it is efficient to be able to offload high-load conversions to the database.
For example, you can first store large amounts of log and sensor data in a cloud data warehouse, and then perform conversion and aggregation as needed, allowing for more flexible data utilization.
Key points for choosing between ETL and ELT
Both ETL and ELT are based on batch processing and are effective for large-scale data integration, but the key to choosing which to use is primarily the infrastructure environment and operational system. In on-premise data warehouses that have been in operation for a long time, the stability of ETL is often highly regarded.
On the other hand, if you are building a system that is highly compatible with the cloud, ELT may be simpler in behavior and easier to handle large volumes. It is best to choose the optimal method, taking into consideration the skill sets and available resources both inside and outside the company.
Increasingly, companies are planning hybrid or phased migration strategies in advance to anticipate changes in performance requirements and data volumes during operation. It's worth considering a flexible architecture that suits your company.
What is EAI? - A mechanism for realizing inter-application collaboration -
Let's take a look at an overview of EAI, which connects business systems within a company to create a consistent business flow.
EAI (Enterprise Application Integration) refers to the technology and architecture that integrates data and processes between different applications in real time or near real time. It connects different systems such as ERP, CRM, and core systems, enabling seamless business processing.
While ETL and ELT effectively utilize batch processing, EAI has the advantage of real-time data transfer and sharing when needed. For example, by linking an inventory management system with an order management system, it is possible to quickly reflect the latest order status in inventory planning.
In recent years, API-based integration has been promoted, and EAI itself has evolved to be more conscious of connecting to cloud systems. However, in some cases, dedicated connectors or adapters are required to bridge the gap with legacy systems, so it is important to check the version and protocol of the target application when implementing EAI.
▼I want to know more about EAI
⇒ EAI｜Glossary
Background to the introduction of EAI and its main functions
Amid calls for corporate innovation and digital transformation, EAI is attracting attention as a way to resolve issues with integrating existing systems. A major reason for its adoption is its ability to achieve a consistent data flow by linking legacy systems with new applications.
EAI tools support messaging, event-driven collaboration, and real-time processing via adapters, and are responsible for regulating communication between systems, simplifying the control of each system and reducing the burden of operational management.
Another major advantage is that error handling and retry control are provided as standard functions on the EAI platform, making it possible to promote complex system integration while minimizing modification costs.
Advantages and disadvantages of EAI
EAI enables real-time integration, and can automatically propagate changes throughout the entire business process from the moment data is generated, contributing to improved business efficiency and faster troubleshooting. Another benefit is that multiple applications can be managed in an integrated manner using a single interface, improving maintainability and scalability.
However, the implementation costs and operational complexity can be an issue in some cases. The more systems that are linked via EAI, the greater the risk of outages, and it is often necessary to check the overall operation during maintenance.
Furthermore, if the requirements for connecting to a legacy system are complex, custom development may become necessary, which could increase the schedule and cost. Therefore, it is important to clarify the details of the connection target before implementation.
Comparison of the differences between ETL, ELT, and EAI in a table
We'll compare the features of each method in a list to help you organize your basic knowledge to make the best choice for your requirements.
While ETL and ELT are primarily used to build data utilization platforms centered around data warehouses, EAI is a method aimed at integrating business applications.The basic idea is that EAI is used when the focus is on business scenarios that require real-time performance or integration with legacy systems, while ETL and ELT are used for large-volume batch processing aimed at analysis.
If you don't understand these differences, you run the risk of building a platform that doesn't suit your data utilization goals. For example, if you were considering introducing an ETL platform, but require a lot of real-time integration, you may need to consider EAI or iPaaS first.
Below, we will summarize the differences between real-time processing and batch processing, as well as the specific scope of application.
| item | ETL (Extract → Transform → Load) |
ELT (Extract → Load → Transform) |
EAI (Enterprise Application Integration) |
|---|---|---|---|
| Definition/Explanation | A method of extracting data from various sources, converting and shaping it into the desired format, and then loading it into the target. | A method in which data is extracted (Extract) and loaded into the target (Load), and then transformed (Transform) within the target or its environment. | A method of integrating and linking data transactions between multiple applications/systems, and achieving real-time/near real-time consistency and synchronization across business processes. |
| Main purpose/use | Integrating and aggregating large amounts of data into data warehouses and data marts and preparing them for analysis. | By utilizing the processing power of cloud data warehouses, etc., conversion can be performed later, enabling high-speed loading and flexible schema changes. | Real-time collaboration between applications, business process integration, and transaction processing and routing. |
| Target data/environment | It mainly targets structured data (e.g., RDB) and loads it into a predefined schema format. | It can handle structured, semi-structured, and unstructured data, and is flexible because it can be transformed after loading. | It mainly involves data exchange, events, and transactions at the application/service level, and the amount of data is not necessarily as large as ETL. |
| Execution timing/processing format | In most cases, it is batch processing, extracting, converting, and loading periodically and in bulk. | Data can be loaded instantly and transformations can be performed in parallel in the target environment. It can also be used for relatively real-time applications. | Event-driven or real-time/near real-time. Primarily application integration. |
| When/where to convert | After extraction, transformation is performed before loading. This is performed by a transformation engine or dedicated middleware. | Transformations are performed in the target environment (e.g. data warehouse) after loading. The transformation engine leverages the target. | It mainly performs real-time routing/mapping/conversion between applications, with the purpose of maintaining application integration. |
| Application scale/data volume | It is suitable for moving and integrating large amounts of data, and is often used in activities when introducing a data warehouse. | This is particularly effective when there is a large amount of data and the target environment has high processing power. | Applicable to individual application integration, business process development, real-time trading, etc. Not intended for large-volume batch processing. |
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Real-time or batch processing?
ETL and ELT are based on batch processing, where jobs are run periodically. Typically, data is extracted, transformed, and written in batches several times a day or every few hours to avoid unnecessary transaction load.
On the other hand, EAI excels at linking data in real time based on events that occur in daily business operations. It is suitable for situations that require synchronization on a transactional basis, such as when an order is received and the data is immediately reflected in inventory management.
However, because high-frequency integration places a strain on the system, the implementation design requires tuning that takes into account the priority of each task and the network bandwidth.
Differences in scope and system configuration
ETL and ELT are primarily used in analytical infrastructure, but are also widely adopted in big data and machine learning projects. The process involves aggregating large amounts of data into a large data warehouse and using it for reporting, training predictive models, and more.
EAI is often used to eliminate the barriers between core systems and business applications and realize integrated business processes. It makes it easy to ensure data consistency and create real-time monitoring mechanisms, and is expected to improve the efficiency of the entire business process.
Since development structures and tool licensing formats differ, the choice between ETL, ELT, or EAI will depend on the purpose of system integration and the technology platform used.
Comparison with iPaaS: A new data integration method for the cloud era
We will look at the background behind the growing attention on iPaaS, a cloud-based integration platform, and its relationship with ETL/ELT/EAI.
iPaaS (Integration Platform as a Service) is a cloud-based platform that enables centralized data integration and application integration. Because workflows and data flows can be built on a GUI without writing code, it is considered to be easy to implement and allows for rapid testing.
In addition, there are often many API connections between services, so you can quickly integrate systems by utilizing a huge number of connectors. Its greatest feature is that it is cloud-native while playing a role similar to ETL and EAI.
As a company grows, it becomes increasingly necessary to integrate with hybrid clouds and multiple SaaS services. iPaaS has the advantage of being flexible enough to meet such requirements and is also resilient to future expansion.
▼I want to know more about iPaaS
⇒ iPaaS | Glossary
What iPaaS can do and the benefits of implementing it
By introducing iPaaS, you can easily set up integration not only with the cloud but also with some on-premise systems via a GUI. For example, a typical use case would be to link a SaaS CRM with an on-premise ERP without coding, and synchronize transaction information in real time.
Furthermore, its high scalability makes it easy to handle increases in data volume and integration with new services. New features and updates are also added on the platform side, eliminating the risk of users being overwhelmed with version upgrades.
Another benefit is that vendors and the community have prepared a wide variety of connectors and templates. The ease of initial setup and operation, even if you have few specialized engineers in-house, is a major factor in promoting adoption.
iPaaS-based data integration platform HULFT Square
HULFT Square is a Japanese iPaaS (cloud-based data integration platform) that supports "data preparation for data utilization" and "data integration that connects business systems." It enables smooth data integration between a wide variety of systems, including various cloud services and on-premise systems.
Key points for tool selection: from requirements organization to product comparison
We will specifically review the items that should be checked when introducing tools such as ETL, ELT, and EAI.
Because there are so many different tools and services available, it is important to clearly define your requirements at the early stages of your implementation project and then compare products. Reflect the needs of the actual site where the system will be used and consider technical and cost requirements comprehensively.
Other important comparison factors include the vendor's support range, the ease of implementation, the activity of the community, and the ongoing support system after operation. Prioritizing only low initial costs may result in inefficiency in the long run.
Here we will list some typical checkpoints, so please consider them in light of your company's environment and requirements.
Links and conversion functions: Check the supported range
Clarify whether integration with a variety of sources, such as SaaS and APIs, is required, rather than just specific databases and file formats, and confirm compatibility. With ETL and ELT, the breadth of supported formats is important, while with EAI, the key is the adapters and messaging functions for each target application.
Another important thing to check is how flexibly customizable the data conversion function is. The more complex the conversion rules, the more likely it is that integration with scripts and external modules will be required in addition to the GUI.
When conducting large-scale data analysis, it is especially important to consider whether the platform supports distributed and parallel processing. Choosing a platform that can deliver maximum performance for your application is the shortcut to success.
Check costs and licensing models
Depending on the product, there are various pricing structures, such as subscription, perpetual license, pay-as-you-go, etc. In the case of cloud iPaaS, the pay-as-you-go model is common, but for tools such as ETL, licenses are generally purchased based on the number of servers or cores.
Additional costs may be incurred not only at the time of implementation but also during operation and upgrades, so it is necessary to consider the long-term TCO (total cost of ownership). Whether or not you can start small is also an important factor in reducing initial risks.
Also, be sure to check the details, such as whether a separate support contract is required and what level of troubleshooting support is available.
Determine the support system and the difficulty of implementation
Whether a tool has an easy-to-use UI and documentation, as well as an active vendor support and user community, are all factors that directly affect the stability of the operation phase. Troubleshooting can take a long time, especially with new tools, if there is little information available.
If the data handled within your company is highly confidential, you should also pay attention to security and audit compliance. Check whether the company has ISO certification or Privacy Mark certification, and if it is using the cloud, check the security of its data center.
The difficulty of implementation depends not only on the cost of learning the tool but also on the degree of organizational change. We recommend that you check whether training programs or consulting services are available to help operators smoothly transition to the new system.
summary
What did you think? As a means of utilizing data, ETL supports analytical infrastructure with stable batch processing, while ELT is an option for efficiently handling large volumes of data while taking advantage of the scalability of the cloud. On the other hand, EAI plays a role in smoothing a company's business flow with real-time integration.
In recent years, cloud-based iPaaS has become popular, and approaches that allow for rapid integration between API-driven services have also become widespread. The key to success is choosing the right combination based on each company's environment and needs, including using it in conjunction with existing tools.
When considering implementation, it is important to comprehensively consider future scalability, operational structure, costs, etc., and design a platform that can continue to generate value over the long term. After clearly outlining your company's data strategy, choose the optimal methods and tools to create a data-driven organization.
The person who wrote the article
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