From ETL to ELT: Modernising Pipelines for High-Volume Metrics

This article explores how the ELT (Extract, Load, Transform) approach modernizes data pipelines, offering greater scalability, flexibility, and speed for today's demanding analytics workloads.

Data pipelines are the most crucial part of systems. Integrating the consumption layer with source systems involves classic steps, just the difference is, they are often in different orders. A well-structured pipeline handling big data efficiently, especially when based on higher volume, velocity, variety, veracity, and value. However, the landscape is changing from more than a decade and with a variety of options emerging, choosing the best for your business is often ambiguous and highly crucial. Reports say that integrating ETL and ELT with understanding your requirements boosts pipeline development 37% faster and 30% improved developer productivity.

What is ELT & How it Works

ELT means extracting, loading, & transforming data processing methodology that helps in reversing the traditional sequence of data integration steps. Generally, data originates from the source systems, is loaded directly into the target system, and is finally transformed within the target system.

ELT leverages the computational power using cloud data platforms, performing transformations after data is loaded not before.

How Does ELT work?

ELT processes data with these 3 steps:

Extraction

Extraction is about pulling data from a source using various databases, applications, APIs, and files. Real-time streaming, batch processing, and change data capture (CDC) are the modern extraction methods for collecting both structured and non structured data efficiently.

Loading

Implementing raw data directly into data platforms or data lakes without any major modification. Here, high-speed ingestion tools & cloud storage quickly make data available for analytics boosting cloud efficiency.

Transformations

Target systems are where transformations happen. They use processing power to clean, enrich, and aggregate data by modeling it efficiently. On-demand transformations happen within SQL, Python, and specialized tools working directly within your data warehousing.

What are the Benefits of ELT

Scalable

ELT is highly scalable due to its cloud infrastructure or cloud data platforms handling massive data volumes, parallel processing as well as making ELT significantly better than traditional approaches.

Affordable

ELT focuses the most on cost optimization helping separate servers for optimization of resources properly. It improves the efficiency and acts as a computational power of modern data warehouses.

Flexible

ELT boosts the flexibility by modifying the transformations without relocating data from the original source. Here, teams adapt to changing business requirements faster.

Maintainable

Data pipelines need less maintenance due to fewer components. ETL systems are complex and have more failures as compared to ELT.

Supportive

ELT-based marketing reporting tools support agile products working directly with raw data, transforming without waiting for IT to modify processes.

Preserves Raw Data

With ELT, Raw Data stays intact targeting the different transformations without eliminating raw data from scratch. A clear historical record is available and maintainable.

A clear historical record is available and maintainable. Organisations often complement this process with a reputable records management company like RecordPoint, which helps ensure compliance, governance, and secure lifecycle management of data stored across platforms.

Evolution of ELT

2011-2017: The Era of ETL

In this decade, the organizations highly supported keeping data hosted on premises with limited or non-scalable frameworks. The storage is costly and every byte needs to be mindfully accounted for. Keeping the system efficient and teams backed up by ETL model, modeling data before it reaches the analytics database is really important.

Basically, the raw data is generally collected, cleaned, organised, and then loaded into the data warehouse for further reporting. ETL model’s goal is to keep things that are highly important since adding more space is always expensive. Here every pipeline sure did work but was typically too much to change every now and then as there were strict rules and specific data formats. For adding new data, coders need to redo their codes every time.

Not only this but processing also happens quite at intervals, especially at nights. Hence, fresh data always take too long to appear in reports leading to slow innovation and impractical quick changes.

2017 Onward: Shift to ELT

Around 2017, the dynamics started to shift when cloud data platforms came to rise. More storage and scaling independently at affordable costing is also something that ELT brought to existence. Also, cloud storage is cheap and highly scalable.

Streaming

Insights within seconds is what the era demands, not hours. Streamlining data pipelines run along the traditional batch pipelines with continuous data processing and reporting live as events happen. From user clicks to sensor readings, raw data flowing directly from source into the platform acts like a high-speed messaging system.

Zero ETL Integration

This is the new way emerging in the market. Despite the name extraction and transformation is still a part of the process. The objective is to eliminate the need for separate ETL or ELT. The traditional and analytical blend offered by vendors these days provide link syncing in real-time without custom code requirement.

ELT vs. ETL: Know All About Usage

Using ELT isn't always a wise choice. You can use it when

● Using a cloud data platform with impeccable processing capabilities.

● ELT handles workloads efficiently and makes things simpler while transformations.

● Data volumes are large & growing rapidly and ELT helps in processing massive datasets without awkward transformational issues.

● ELT facilitates introducing flexibility and keeps the data intact.

● Where real-time or near-real-time analytics is required, ELT helps the most.

● Lastly, when you have diverse data types, unstructured or semi-structured, ELT boosts transformations.

Right Architecture for Marketing Reporting Tool

ELT the backbone or marketing reporting tool requires to sort the data to avoid messy piles targeting the wrong audience and costing way more than actually required.

Decoding the right architecture layer by layer for marketing Reporting tools to follow as a marketing expert:

Layer 1: Source Layer - Extraction

Data mining is the starting of the marketing strategies. Extraction of appropriate data from various channels such as Google Ads, LinkedIn, Meta, CRM, website analytics, etc. facilitates better integration and sound fault tolerance.

Layer 2: Storage Layer - Load

Dump raw & unaltered data straight into a cloud data platform where it stays intact and can be filtered out as well as used again when the KPI changes. Furthermore, using ELT eliminates the need to rebuild upstream logic, which is a core practice in big data engineering.

Layer 3: Transformation Layer - Changes Inside the Warehouse

The next layer is where the marketing logic lives. This layer facilitates cleaning, standardizing, and revisiting the campaign names, currencies, and time zones without creating confusion.

Also, it accounts for ad spends featuring the focus on conversion rates & revenue tracking. This layer further facilitates calculation of the basic and specialized metrics like CAC, ROAS, or multi-touch attribution.

Layer 4: Serving Layer - Analytics & Reporting

Segregation of data by campaigns, geographies, demographics, funnel stage and more is another critical aspect to look for while it is simply exposed using various AI tools such as BI, Metabase, and Looker. Therefore, in other words, marketing reporting tools make it easier to decide your next campaign effortlessly.

Best Practices to Follow

Understand Requirements

Start with learning about the user case and understand their requirements. Before starting on a new architecture, learn what the businesses need and analyse using daily updates. Identify and consider streaming wisely.

Study Patterns to Mix & Match

One solution is not a fit for all. Use various methods to create the perfect fit for you. Streaming is when the portions of data require it, invest in Zero ETL integration when you need a marketing reporting tool along with the existing pipelines.

Move Forward with Automation

Break the work in smaller pieces and use automation for data lineage and impact analysis. Automation helps in keeping track of every change and transformation. Also, modular designs when combined with tools handle dependency management without rewriting code.

Testing & Validation of Data

Automation testing and observation saves you by catching issues proactively. Data lineage shows how data flows across your pipeline where the context is invaluable and impact goes wrong.

Idempotent and Resilient Pipeline

Adding various checkpoints for safeguarding data is another great approach. These temporary staging tables only swap to production after success and partial failures don't corrupt your output. Get notified with alerts related to failure and anomalies.

In The End

The movement of modern data demands have shifted from traditional offering scalability, flexibility, and speed. Especially when it comes to marketing reporting tools, real time insights are what you need the most. Moreover, extracting from multiple sources, choosing a secure cloud platform and transforming the data reduces complexity and preserves raw data as it is for changes to be made in future.

Disclaimer: The views expressed herein are for reference only and don't necessarily represent the official views of Alibaba Cloud.