ELT has emerged as a critical process for managing massive, diverse data pipelines to empower advanced analytics. This comprehensive guide will explore what ELT entails, key benefits, use cases, leading tools, best practices for implementation, and the future outlook.
Introduction
With data volumes expanding exponentially across organizations, the limitations of traditional ETL (extract, transform, load) have become more pronounced. ETL often relies on complex, on-premises middleware that creates bottlenecks for ingesting large, streaming, and varied data sources.
ELT (extract, load, transform) addresses these challenges by simplifying data integration architecture and leveraging the scalability of cloud data platforms. As an experienced data engineer, I‘ve witnessed firsthand how ELT can accelerate time-to-insight across many analytics use cases.
In this guide, I‘ll share my insights on when ELT makes sense, how to execute it effectively, and key developments on the horizon that could shape the next evolution of ELT.
Demystifying ELT
So what exactly is ELT and how does it work?
ELT stands for extract, load, and transform—the three stages comprising the process:
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Extract – Data is pulled from sources like databases, SaaS apps, social feeds, and sensors in its raw form.
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Load – The extracted data is moved into the target database or data warehouse without any transformation.
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Transform – Once loaded, the data is cleansed, aggregated, joined, and otherwise prepared based on the desired analysis.
Unlike ETL, which transforms data after extraction but before loading, ELT flips the sequence to load first and transform later. This reversal provides greater flexibility, simplifies the pipeline, and facilitates near real-time analytics.
ETL vs. ELT
While ETL and ELT are both viable options, here‘s an at-a-glance comparison of some key differences:
| Criteria | ETL | ELT |
|---|---|---|
| Order of Steps | Extract, Transform, Load | Extract, Load, Transform |
| Target Data Store | Data warehouse | Data lake or cloud warehouse |
| Transformation Process | Limited flexibility | Customizable |
| Latency | Higher | Lower |
| Architecture Complexity | Higher | Lower |
Analyzing these differences helps illustrate the advantages ELT provides, which I‘ll explore in the next section.
The Benefits of ELT
Based on my experience, ELT can enhance data integration across several dimensions:
1. Faster access to data
By loading source data immediately without lengthy staging and transformations, ELT minimizes the time delay between data extraction and availability for analysis. This enables closer to real-time analytics.
2. Simplified dataflow
ELT eliminates the need for a separate ETL environment to transform data before loading. This reduces overall pipeline complexity.
3. Flexible transformations
All transformations occur within the data warehouse itself based on the specific analytics needs rather than via an ETL tool. This facilitates more customization.
4. Scalability
Modern cloud data warehouses can scale elastically to accommodate massive raw data volumes, overcoming bottlenecks.
5. Data lake optimization
The ability to land raw data in a data lake and transform it later aligns perfectly with the data lake ingestion model.
A survey by Gartner found that 41% of organizations adopting ELT did so to increase flexibility while 24% aimed to improve performance. [1]
Challenges and Considerations
Despite its advantages, ELT comes with a few caveats to consider:
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The target data warehouse must support heavy raw data loads and transformations after loading—not all do.
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Data security and privacy risks may rise with increased data movement to the cloud.
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Data engineers may require retraining since ELT represents a shift from longtime ETL norms.
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Transforming data after loading into the warehouse can create more load for the warehouse to handle.
Accounting for these challenges in areas like security, governance, and capacity planning is imperative for ELT success.
Use Cases Where ELT Shines
Based on my consulting experience, ELT delivers the most value in these types of scenarios:
Streaming and real-time analytics – The low latency of ELT allows data to be queried immediately upon landing in the warehouse, enabling real-time reporting and dashboards.
Data lake ingestion – ELT aligns perfectly with the schema-on-read approach of data lakes, wherein data is transformed at consumption rather than ingestion.
Cloud migration – For organizations moving on-premises data warehouses to the cloud, ELT simplifies the transition and takes advantage of cloud scalability.
Machine learning – The flexibility of transforming data within the data warehouse is ideal for the feature engineering and dataset preparation needs of machine learning projects.
Internet of Things data – The massive streams of sensor and telemetry data generated from IoT devices can be ingested faster via ELT.
Now let‘s explore the leading ELT solutions available to undertake such initiatives.
Top ELT Tools
A range of commercial and open-source ELT tools exist, each with their own strengths and weaknesses. Here I highlight some popular options:
Informatica Intelligent Data Management Cloud – Informatica‘s end-to-end intelligent platform covers data integration, quality, governance, and more. It provides a unified interface to manage ELT workflows across on-prem and cloud sources and targets.
Matillion ETL – As a cloud-native ETL/ELT tool designed for Snowflake, Matillion simplifies building transformation jobs that run natively within Snowflake for optimal performance.
Skyvia – This SaaS platform focuses on ELT specifically for cloud data warehouses. It uses an intuitive visual interface to model ELT pipelines and includes pre-built connectors.
Hevo – Hevo shines at incremental data replication and sync across many data sources with automatic schema evolution as upstream source schemas change.
Amazon Glue – Fully managed ETL service native to AWS that simplifies ETL/ELT with serverless Apache Spark jobs. Integrates data catalogs and schemas.
Apache Airflow – Open-source workflow management platform to programmatically author, schedule, and monitor ELT pipelines. Integrates with databases like BigQuery and Snowflake.
I generally recommend evaluating commercial tools first for their ease of use and support, with open-source options for more customization.
Best Practices for Implementation
Follow these tips for executing ELT successfully based on my experience:
Choose the right data warehouse – Select a modern cloud data warehouse able to handle heavy raw data loading like Snowflake, BigQuery, Redshift, or Azure Synapse Analytics.
Make data immediately available – Load new data as it arrives before transforming so it can be leveraged right away if needed.
Transform data incrementally – When possible, transform data in smaller batches through incremental ELT jobs rather than bulk updates.
Implement governance – Apply strong data security, access controls, and governance practices, especially with increased data movement to the cloud.
Monitor data quality – Assess data quality before and after loading into the warehouse to catch any issues early.
Use workload isolation – Isolate ELT workloads from analytics and reporting workloads for optimal performance.
Automate workflows – Automate recurring ELT jobs through workflow schedulers like Apache Airflow for efficiency.
Retrain developers – Train developers on ELT-specific design patterns as the paradigm shift can take adjustment.
With the right planning and preparation, organizations can smoothly transition to ELT and realize the performance and agility benefits.
The Future of ELT
ELT adoption is growing rapidly as organizations recognize its advantages for modern data integration. Look for ELT to continue evolving with these emerging trends:
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Closer couplings between ELT tools and cloud data platforms – Tighter integration via native connectors and optimization.
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Automation and machine learning – Automated job tuning, intelligent optimizations, and smart data cataloging.
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More focus on data quality and governance – Greater capabilities for data profiling, cleansing, and privacy built into ELT tools.
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Support for diverse data types – Handling new data sources like IoT, social media, and multimedia.
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Continued shift from ETL – Transition from legacy on-premises ETL platforms to cloud-native ELT alternatives.
Conclusion
ELT offers a flexible, scalable approach to managing growing data volumes and varieties required by modern analytics. By loading data immediately and transforming later within the data warehouse itself, ELT improves latency, reduces complexity, and drives faster insights.
As organizations recognize these benefits, they will continue embracing ELT, particularly as they shift data pipelines to the cloud. ELT represents a next-generation paradigm that promises to unlock the true value of data.
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