The demand for effective real time data processing has reached a point of no return in the 21st century. Real-time data processing is the constant, uninterrupted handling and delivery of messages and events. Examples of real-time data processing include live traffic updates, stock market feeds, online video games, etc., where data is processed and acted upon as it is received. It is absolutely paramount that companies and service providers process data in real time (live) as effectively and as quickly (nanoseconds) as possible to ensure a better user experience on their products and services. You might be taken aback by the number of industries that require real time processing to stay afloat in the fast-paced software ecosystem today. This document will provide you with context on the ETLT data processing systems, its advantages and drawbacks, you will then decide if this process will be favourable to your organization.

What is Real time ETLT?

To fully understand ETLT, you must first learn about ELT.
ELT (Extract, Load and Transform) is a well-known data architecture technique. It involves extracting data from a variety of sources and storing them in one target data warehouse, where the data is then decoupled, denormalized, combined, and transformed in other possible ways before they are loaded into another data source. For example, you can perform ELT when you collect sales data from a store and customer data from a loyalty program, combine and transform data, and then load it into a dashboard to track trends. Companies perform ELT so they can handle large volumes of data in a manner that mirrors real time data processing.

ETLT (Extract, Transform, Load, and Transfer) adds another layer to the traditional setup. The “transfer” part represents the final step where data is moved in real time to another system. ETLT is crucial for companies aiming to derive valuable insights and take real-time action. In video games, for instance, ETLT can update player stats and leaderboards instantly as they play, which illustrates one of the key processes involved in modern data pipelines.

What are the Challenges of real-time ETLT?

Real-time ETLT comes with a few hiccups here and there that can do more harm than good to any organization looking to implement it. Some of the challenges you might face when implementing ETLT include:

• Overburdened data loads: Processing a large volume of data in real-time can overwhelm infrastructure and cause system failures.

• Multiple data access: Access to different streams of data demands rigorous data formatting that can prove to be detrimental to the quality of the data extracted and its competency.

• Data Security and Privacy: Data Security and Privacy cannot really be accounted for because data is being transferred in real time to another data source, where it is being utilized at the same time.

Suggested Solutions

• Implement a Robust Cleaning Mechanism: Companies can reduce errors by cleaning and standardizing data before it moves downstream. This ensures consistency and improves data quality across the pipeline.

• Deploy Secure Data Transfer Protocols and Encryption: Organizations must apply secure data transfer protocols to protect against data breaches while maintaining compliance with regulations for data transfer of sensitive data.

• Use ETLT Tools and best practices: Tools like Apache Kafka, Flink, or Talend help automate and optimize data science workflows and support business intelligence teams with real-time integration and visibility

Extracting Data in Real Time

Data extraction is the process of remotely collecting various data types from different locations and storing them in a data lake all at the same time. This data lake is an intermediate storage area for temporarily storing extracted data. The data lakes can be transient in nature i.e. data can be deleted when the extraction process is done.

Data Extraction Methods and Best Practices

You can extract data from external sources using the following methods:

1. Set Up Notification Update

• Configure the source system to send notifications when data changes.
• Use these notifications to trigger the extraction of modified records instantly.

2. Implement Incremental Extraction

• For systems without update notifications, schedule periodic checks for recently modified data.
• Extract only the changes identified within the specified time window.

3. Use Full Extraction as a Backup

• If the source system lacks update mechanisms, extract all data periodically.
• Compare the new extract with the previous one to identify changes.
• Limit this approach to small datasets to minimize transfer overhead.

Transforming and loading Data in Real Time

After extracting data from their sources, the next step is to organize that data and structure it in a format that is suitable for analysis, then store it in the data warehouse. Once transferred, You then load this data into the target systems where it is readily available for usage.

Real-Time Data Transformation and Loading Methods and Best Practices

Follow the steps below to effectively transform and load extracted data in your system:

1. Transform Data in Real-Time

• Automatically remove errors, fill missing fields, and standardize values as data streams in (e.g., map “Parent” to “P”).
• Link real-time data from different sources to create a cohesive data set, allowing for up-to-date insights.
• Convert data formats as they flow in (e.g., standardize measurements to a common unit or unify date formats).
• Apply encryption in real-time to protect sensitive information before it reaches the target system, ensuring compliance with data protection standards.

2. Choose the Loading Method

• Streaming Load: Stream real-time data changes directly into the target data warehouse as they occur, ensuring the target system is always up-to-date.
• Incremental Load: Load only the changes (deltas) since the last update. Use real-time data streams to synchronize the target system with the most current data.

3. Automate the Process

• Use ETLT tools with real-time data processing capabilities (e.g., Apache Kafka, Flink) to automate the data transformation and loading steps.
• This ensures continuous real-time integration between source and target systems.

Conclusion

In this article, we explored the critical role of real time data processing in modern organizations. We examined the differences between ELT and ETLT, explained the processes involved, and addressed key challenges such as data security and privacy and real-time data transfer. When dealing with big data or a specialized processing system, make sure to implement real-time ETLT, this can significantly enhance your ability to make timely decisions and stay competitive.