ELT

Introduction

In today’s data-driven world, organizations deal with vast amounts of raw data from various sources. To understand this data and gain useful insights, you need to organize and convert it into a usable format. This is where ELT comes in.

ELT is a process called Extract, Load, Transform. It helps businesses manage lots of data efficiently. In this article, we’ll dive into the basics of ELT, explore its advantages, and see how open-source tools can streamline the process.

What is ELT?

ELT is a data integration approach that involves three key steps:

1. Extracting data from source systems
2. Loading the raw data into a target system
3. Transforming the data within the target system

ELT process loads raw data into target system first, then transforms it. Different from traditional ETL process. This allows for faster loading and leverages the processing power of the target system.

Advantages of ELT

Faster Data Loading

ELT simplifies the extraction process by loading raw data directly into the target system without the need for complex transformations. This leads to faster data loading times, especially for large datasets.

Flexibility in Transformations

ELT causes transformations to happen after loading the data. This allows for easier changes to transformations to meet new business needs, without affecting data extraction.

Scalability

ELT leverages the processing capabilities of the target system, making it highly scalable. It can handle growing data volumes and accommodate new data sources with ease.

ELT in Action: An Example

Imagine an online store that wants to combine data from different places, like sales, customer details, and product listings. Here’s how ELT can be applied:

1. Extraction: Data is extracted from source systems like the sales database, CRM, and product management system. The raw data is collected without any transformations.
2. Loading: The extracted data is loaded into a target system, such as a data warehouse or a big data platform like Hadoop. The data retains its original format during the loading process.
3. Transformation: Once the data is loaded, transformations are applied within the target system. This may include data cleansing, aggregation, joining tables, and applying business logic. For instance:

• Cleaning up inconsistent customer names
• Calculating total sales per product category
• Merging customer data with sales transactions

The transformed data is then ready for analysis and reporting.

Open-Source Tools for ELT

Several open-source tools can streamline the ELT process. Here are a few popular options:

Apache Spark

Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs for data processing and supports various data sources. Spark’s in-memory computation capabilities make it ideal for handling large-scale data integration tasks.

Example using PySpark:

from pyspark.sql import SparkSession
# Create a SparkSession
spark = SparkSession.builder \
.appName("ELTExample") \
.getOrCreate()
# Extract data from CSV files
sales_data = spark.read.csv("sales.csv", header=True)
customer_data = spark.read.csv("customers.csv", header=True)
# Load data into a target table
sales_data.write.mode("overwrite").saveAsTable("sales_raw")
customer_data.write.mode("overwrite").saveAsTable("customers_raw")
# Transform data using SQL
transformed_data = spark.sql("""
SELECT
s.transaction_id,
s.customer_id,
c.name,
s.amount
FROM sales_raw s
JOIN customers_raw c ON s.customer_id = c.customer_id
""")
# Store transformed data
transformed_data.write.mode("overwrite").saveAsTable("sales_transformed")

In this example, we extract data from CSV files. We then load the data into target tables. Finally, we use SQL JOIN to combine sales and customer data.

Apache NiFi

Apache NiFi is a powerful system for automating data flows between systems. It provides a web-based UI for designing, controlling, and monitoring data pipelines. NiFi supports a wide range of data formats and protocols, making it suitable for ELT workflows.

Example NiFi data flow:

1. Use a GetFile processor to extract data from a source directory.
2. Use a PutHDFS processor to load the data into Hadoop Distributed File System (HDFS).
3. Use a ExecuteSparkInteractive processor to run Spark transformations on the loaded data.
4. Use a PutHiveQL processor to store the transformed data in Apache Hive tables.

Talend Open Studio

Talend Open Studio (free version discontinued from January 31, 2024) was an open-source data integration platform that provides a graphical interface for designing ELT jobs. It supported various data sources and targets, and offered a wide range of built-in components for data processing and transformation.

Example Talend job:

1. Use a tFileInputDelimited component to extract data from a CSV file.
2. Use a tMap component to apply transformations and mappings.
3. Use a tOracleOutput component to load the transformed data into an Oracle database table.

Best Practices for ELT

To ensure a successful ELT implementation, consider the following best practices:

1. Data Quality: Establish data quality checks and validations during the extraction and transformation stages to maintain data integrity.
2. Incremental Loading: Implement incremental loading techniques to process only the changed or new data, reducing the overall processing time.
3. Monitoring and Logging: Set up robust monitoring and logging mechanisms to track the progress of ELT jobs and identify any issues or errors.
4. Data Security: Implement proper security measures, such as encryption and access controls, to protect sensitive data during the ELT process.

Conclusion

ELT is a powerful approach for data integration that enables organizations to efficiently handle large volumes of raw data. ELT is a process that involves extracting data from source systems, loading it into a target system, and applying transformations. This method offers faster loading times, flexibility, and scalability.

Open-source tools like Apache Spark, Apache NiFi, and Talend Open Studio offer robust capabilities for implementing ELT workflows. Businesses can improve their data integration processes and maximize their data’s potential by using best practices and tools.

As data continues to grow and evolve, ELT will remain a crucial component of modern data architectures, empowering organizations to make data-driven decisions and stay ahead in the competitive landscape.