ETLT (Extract, Transform, Load, and Transfer) process combines the best of the legacy ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) approaches. The ETLT process and application ensure that organizations can process voluminous data sets in real time and obtain useful insights. It also ensures speed, security, and compliance during data synchronization and processing. Below is the data integration and processing pattern for ETLT.
- Extract: ETLT can extract the unprepared and raw data from any source application. The data is then loaded in the “stage” area.
- Transform: The initial transformation is for the staging area. It may involve masking, encrypting, or removing sensitive data. Some examples of such data include PHI (Protected Health Information) and PII (Personally Identifiable Information). During transformation, the sources are not mixed. It ensures the process is fast and can maintain its integrity. The outcome ensures compliance.
- Load: Since data is prepared, it is loaded in the requisite warehouse.
- Transform: The data is integrated completely with the warehouse at the last and secondary transformation stages. At this stage, data from multiple sources is processed.
Business organizations may gather data from multiple sources today. The databases should be processed to obtain inputs for insights and decision-making. Modern organizations must use the latest data processing techniques (including ETLT) to process data quickly and improve their processes. Improvements must be made in operational efficiency, customer satisfaction, complaints/customer support, and technical support management. The real-time data processing techniques including ETLT allow organizations to achieve analytics without security issues. For instance, the application can extract data and remove/encrypt them wherever needed. Therefore, an organization can reap the benefits of data analytics while fully complying with the regulations and best practices. it may ensure customer support and trustworthiness, and a long-term relationship with their clients. The technique is also speedy.
The real-time ETLT data processing solution still faces certain challenges. These include the errors caused by the tools, bottlenecks, inconsistencies, conflicts, and security. Data checks in the real-time distribution of databases improve scalability, encryption, and access control. These are some solutions available for the ETLT application.
Challenges of Real-Time ETLT
Below are some common challenges for real-time data processing applications including ETLT.
1. Handling Voluminous Data at High Velocity
- The system may be exposed to inconsistencies in accuracy and data loss when it handles large amounts of data inflowing at high speeds.
- The application must be built on proficient architecture to maintain data quality and handle large data sets with technological confidence.
- Data corruption and loss must be avoided. The system must have an ingrained capability to recover data.
- The system must be monitored in real-time to identify and manage all issues.
2. Ensuring Data is Consistent, Accurate, and Devoid of Defects.
- The real-time ETLT system architecture is based on multiple systems with numerous sub-components. The systems must be synchronized and optimized. Their progress should be monitored carefully.
- Data synchronization and its sequencing for processing must also be monitored to maintain data accuracy.
3. Ensuring data privacy and security during processing.
- Proficient data architecture is required for ETLT and other real-time data processing systems. Secure data access must be ensured during the processing of real-time data.
- System architecture must ensure that maintaining and validating security does not cause interruption of operations and data processing. The appropriate real-time ETL tools must be integrated into the system’s architecture.
Solutions for Secure and Accurate Processing of Voluminous Data at High Velocity
Fortunately, solutions are available for the above-stated issues. These include the use of monitoring tools and other third-party services and applications.
- Real-time ETL tools can be deployed to check and validate data instantaneously. It can reduce errors and manage the quality of data.
- The architecture for RAM and CPU can be updated to use services including Amazon Dynamo DB and Apache Cassandra, among others. These applications can speedily process data, reduce downtimes, and scale and optimize operations. Being cloud services, their deployment and use is free from hassle.
- Encryption, access control, and other similar technologies must be used to ensure data security and integrity while in transit.
How does ETLT extract data?
The ETLT process begins with data extraction. This data may be accepted and obtained from files, databases, warehouses, and integrated streaming applications. Data may also be obtained from the APIs (Application Programming Interfaces). This data may be available in multiple formats including text files, audio, video, images, XML, and JSON files. The two prominent ways for real-time data extraction and capture include:
- CDC (Change Data Capture): Here the data is directly captured from its data source. Any changes to the source may also be tracked, tried, and captured.
- Event Streaming: This technique can capture data during transition or state change.
Real-Time Transformation and Loading for Extracted Data
An ETLT process and architecture must integrate certain important technologies for secure and fast transformation and loading of data. For instance, Talend or Apache NiFi applications can clean the extracted data. They can also format and enrich the data, and improve its quality in real-time. The application must also integrate the data integration pipeline to connect the different data sources and manage the data flow. Some important guidelines or best practices to follow for proficient ETLT architecture include:
- Integrative system that uses a failover mechanism. It can mitigate impactful issues including, network problems, human error, or system failure.
- A well-formulated data government policy and its effective implementation can maintain the security and integrity of data.
- Effective use of a monitoring tool is necessary. These tools must be allotted for issues, tracking performance, maintaining scalability, and denoting alerts.
ETLT ensures that organizations can extract and transform data in real time and exploit its benefits. However, there are challenges due to the volume of data, speed involved, architecture, system compliance, and security. Well-planned architecture, use of effective services, applications (in-house or third party), and constant monitoring can ensure challenges can be overcome effectively.
Conclusion
The future is bright for real-time data processing applications and processes including ETLT. The security and efficiency features are bound to improve in the future. We can see more effective and agile third-party services that would be integrated easily. Businesses can improve their performance, customer support, service quality, and scalability. They must adopt a wise and well-thought-out approach to real-time data processing through ETLT and similar applications.