What is the difference between batch ETL and streaming ETL?
ETL stands for Extract, Transform, Load. It is a data integration process that involves extracting data from multiple sources, transforming it into a functional format, and loading it into the system for analysis, reporting, or several other applications. Nowadays, ETL is significant for gaining valuable insights and making informed and accurate decisions. But choosing the right ETL approach can be challenging, especially when streaming data is hyped-up so much now. There are two main types of ETL that we will discuss here:
Batch ETL is the traditional workhorse. It processes data in large chunks at
predefined intervals Predefined intervals could be hourly, daily, or weekly, etc.
Streaming ETL follows a real-time approach and deals with data as it arrives. It works like a continuous stream flowing into a processing pipeline. We can think of it as filtering and cleaning water as it flows through a river.
Following are some of the key differences between batch ETL and streaming ETL:
Key differences between batch ETL and streaming ETL
Feature | Batch ETL | Streaming ETL |
Data Processing Model | Processes large datasets in batches | Processes data continuously as it arrives |
Latency | Higher latency (minutes to hours) due to batch processing | Focuses on near real-time processing |
Scalability | Can be challenging to scale for high-velocity data | Highly scalable for handling large data streams |
Data Freshness | Provides historical data insights | Provides up-to-date insights on current data |
Use Cases | Data warehousing, historical analysis, reporting | Fraud detection, anomaly detection, real-time analytics |
Complexity | Relatively simpler to implement and maintain | More complex due to real-time requirements |
When to choose batch ETL
Batch ETL is used in the following cases:
Large data volumes: Batch processing is efficient for handling big datasets accumulated over time.
Historical analysis: Batch ETL excels at providing historical insights and trends.
Cost-effectiveness: It can be more cost-effective for static, predictable data workloads.
When to choose streaming ETL
Real-time insights: Streaming ETL is needed for applications requiring immediate action based on data, like fraud detection or stock trading.
High-velocity data: It efficiently handles continuous streams of data from sensors, social media, or IoT devices.
Freshness and agility: It enables quick adjustments and decisions based on up-to-the-minute data.
There’s no strict rule for when to choose which model. Both batch and streaming ETL have their strengths and weaknesses. Choose the method that aligns with the data volume, velocity, and desired level of data freshness and insights.
Note: Learn more about ETL testing for further understanding.
Test your understanding
Solve the following quiz to test your understanding:
When should you choose batch ETL?
When real-time insights are needed
When handling continuous streams of data
When historical analysis and trends are important
When immediate action based on data is required
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