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Modern Data Pipelines: Architecting ETL and ELT Workflows for Big Data

  • October 28, 2024
Evermethod

Author : Evermethod | October 28, 2024

Modern data pipelines are the lifeblood of today's data-driven business, where businesses ingest, transform, and store massive volumes of data.
 
With increasing volume, variety, and velocity of data, businesses tend to adopt strict workflows to effectively process and unleash big data effectively.
 
The two most common methodologies to process data are ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform).
 
This blog explores the architecture of modern data pipelines, their workflows, and best practices for processing large-scale data. 

What are Modern Data Pipelines?

data Pipeline
A modern data pipeline automatically moves data from various sources, such as APIs, databases, or IoT devices, to a destination, which might be a data lake or warehouse.
 
These pipelines often form the fundamental infrastructure that supports business intelligence dashboards, machine learning models, and other data-intensive applications.
 
Handling raw, unstructured, and structured data from different environments (e.g., on-premises, cloud) enables the business to turn the data into working insights.

Understanding ETL and ELT Workflows

ETL (Extract, Transform, Load)

ETL
ETL is a traditional data integration process in which data is initially extracted from the source system, transformed to meet certain needs, and then loaded into the target system, such as an OLAP data warehouse.
 
This process is highly effective for structured environments but can be very time-consuming as the transformation happens before the loading of data.
 
ELT (Extract, Load, Transform)
ELT (1)
In contrast, ELT will extract and load raw data to the target system, where the transformation will be done.
 
This method uses the computing power of the most advanced cloud-based data warehousing engines, like Snowflake and Google BigQuery, to make the process faster and scalable.

ETL vs ELT: Side-by-Side Comparison

Category ETL ELT
Definition Data is extracted, transformed, and then loaded. Data is extracted, loaded, and then transformed.
Transform Transformed on a separate server before loading. Transformed inside the destination system.
Load Loads transformed data into the destination system. Loads raw data into the destination system for later transformation.
Speed Time-intensive due to early transformation. Faster, as raw data is loaded directly.
Data Output Ideal for structured data. Supports structured, semi-structured, and unstructured data.
Scalability Suited for smaller datasets with complex transformations. Optimized for large datasets with simpler transformations.
Maintenance Requires maintenance of a separate transformation server. Simplified, with fewer systems to maintain.

Architecture of Modern Data Pipelines
There are three stages for a modern data pipeline:

DATA TAR
1. Data Ingestion

Raw data is pulled from various sources. This may include data from SaaS applications, mobile devices, and IoT sensors. It might be either structured or unstructured. It is often stored in the cloud warehouse, such as Amazon Redshift or Azure Synapse, for better flexibility and scalability. Hence, it is updatable and ready to implement real-time processing.

2. Data Transformation

It then undergoes numerous data transformations at ingestion; it cleanses, filters, and enriches the data. Automation comes into play at this stage because aggregating data or converting formats are repetitive processes. This transformation stage is very important for consistency and to prepare the data for analysis.

3. Data Storage

The transformed data is kept in the repository, allowing end users to access it. The processed data is delivered to the subscribers or consumers in a streaming context, making it available for real-time processing or batch processing.

Best Practices for Handling Large-Scale Data Processing

Effectively handling large volumes of data requires implementing best practices to ensure performance, accuracy, and scalability:

 

large scale data
  • Automate Data Workflows
    Automation removes human error and optimizes repetitive data processing tasks.
  • Optimize Data Storage
    Using a combination of data lakes and warehouses to balance the storage needs for structured and unstructured data.
  • Monitor Data Lineage
    Understand how data evolves using data lineage. This activity ensures compliance with regulatory requirements.
  • Cloud Scalability
    Leverage cloud-native scalable solutions that can be optimized based on performance.
Ensuring Data Quality

Data quality is critical to the success of modern data pipelines. Without clean, accurate data, insights drawn from the analysis may be flawed. To ensure high-quality data:

 

Group 5556
  • Set Up Validation Rules:
    Use automated checks to flag and correct inconsistencies during ingestion and transformation.
  • Embed Data Governance:
    There should be governance frameworks for the entire organization to ensure data is processed safely in accordance with privacy standards such as GDPR or HIPAA.
  • Monitor in Real Time:
    This kind of tool manages to track issues in a timely manner so that they can be resolved as soon as possible.
Conclusion

Modern data pipelines are necessary for processing large amounts of business data today. In most situations, the selection of the right data workflow—whether ETL or ELT—can help architects ensure efficiency, scalability, and accuracy in their data systems.

Evermethod understands the challenges involved in building and maintaining modern pipelines. We provide tailored solutions that are scalable, secure, and designed to address businesses' exact data demands.

Whether you're dealing with structured, unstructured, or streaming data, Evermethod's expertise ensures that your data pipelines run smoothly, driving actionable insights and improved decision-making.

Streamline your data processes now! Contact Evermethod to discover modern pipeline solutions tailored to your company.

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