The Ultimate Guide to ELT in 2024: Extracting Value from Data

Hello there! If your organization is exploring how to effectively manage large volumes of data from diverse sources, you‘ll be excited to learn about ELT. Keep reading this guide to understand how ELT works, its benefits compared to traditional ETL, use cases where it shines, and how to implement ELT successfully.

What is ELT and Why Does it Matter?

ELT stands for extract, load, transform and is a process for ingesting data from multiple sources into a target database or data warehouse. It differs from ETL in that it loads data first then transforms it, enabling faster access for analysis.

With data volumes and variety accelerating, ELT provides a flexible, scalable architecture for modern analytics. According to Gartner, "Through 2022, ELT will be a path to broader and faster analytics adoption for 80% of organizations."

ELT overview

Organizations need to make data available quickly to drive real-time decision making. ELT supports this agility while also handling massive datasets.

Now let‘s look at how ELT works…

How ELT Works: Key Steps

ELT involves three main phases:

1. Data Extraction

ELT starts by extracting data from various sources, both on-premises and cloud-based. This includes databases, CRM and ERP systems, IoT sensors, mobile apps, social media, and websites.

Data extraction has gotten more complex as the volume and variety of sources grows. Just some examples of data types to integrate include:

  • Relational data from transactional systems
  • semi-structured data like JSON and XML
  • Unstructured data from documents, audio, video
  • Streaming data from mobile and IoT devices

Specialist tools like Bright Data help automate data extraction with pre-built connectors, spiders, and scrapers. This enables those with less technical expertise to integrate disparate sources.

2. Loading Data

Next, the raw extracted data is loaded directly into the target database such as a data warehouse or data lake. Loading methods include:

  • Bulk loading – Efficient for large historical datasets
  • Incremental loading – Fast updates for recent data
  • Stream loading – Continuous inserts for real-time data

Modern cloud warehouses like Snowflake provide flexibility to load using multiple techniques.

3. Transforming Data

Finally, within the data warehouse, the loaded data is transformed to prepare it for analysis and usage in applications.

Data transformations may involve:

  • Cleansing – fixing missing values, formatting, deduplication
  • Joining together data from multiple sources
  • Aggregating for reporting
  • Enriching data by adding new attributes

Data engineers can customize transformations for different needs rather than force data through one ETL flow.

Evolving from ETL to ELT

ELT has been gaining adoption as businesses shift from legacy on-prem data warehouses to cloud-based "data lakes" that can handle greater volume, velocity, and variety of data.

Let‘s compare some key differences between the traditional ETL approach and modern ELT:

ETLELT
Order of StepsTransform then LoadLoad then Transform
Target SystemOn-prem data warehouseCloud data lake
TransformationFixed early in pipelineFlexible after loading
LatencySlower access to dataFaster data availability
ArchitectureComplex separate ETL toolsSimplified built into data lake

According to industry surveys, ELT adoption has tripled in the past 5 years as firms embrace cloud data platforms:

ELT adoption increasing

"By 2025, 50% of traditional on-premises ETL will have transitioned to ELT architectures leveraging the elasticity of the cloud." – Gartner

However, this shift can introduce challenges which we‘ll discuss later. First, let‘s dive deeper into why ELT delivers value.

5 Benefits of ELT Over ETL

ELT simplifies data integration and provides other advantages including:

1. Faster Access to Raw Data

By loading source data immediately into the target system, ELT makes it available quicker for downstream uses. Waiting for lengthy ETL transformations slows time to insight.

2. Simplified Pipeline

ELT eliminates the need for moving data to an intermediate staging area and managing complex ETL tool administration. Data lands in its raw form in the data lake.

3. Scalability

Cloud data platforms like Snowflake easily scale storage and compute for increasing data volumes. On-prem warehouses had more rigid resources.

4. Transformation Flexibility

Data teams can customize data for their specific analytical needs rather than conform to a predefined ETL flow.

5. Agility for Changing Requirements

Schema on read means structure can be applied at query run time versus ingest. This facilitates adapting to new data and requirements.

Now let‘s compare popular ELT tools…

Top ELT Tools

Many tools exist to help build and manage ELT pipelines. Here are some top options:

  • Pentaho – Visual data integration + ELT with big data support
  • Talend – Pre-built components to create ELT jobs and data flows
  • Hevo – Fully managed ELT with 1500+ data source connectors
  • Apache Spark – Unified analytics engine for large-scale ELT
  • Matillion – Cloud-native ELT on AWS, Snowflake, Databricks
  • Fivetran – Fully managed connectors and dbt for ELT

These tools help simplify ELT configuration vs hand-coding. Many also provide connectors to easily integrate sources and destinations.

ELT Architecture Overview

A reference architecture for ELT pipelines typically includes:

ELT architecture

  • Data sources – On-prem, cloud apps, websites, etc.
  • Extraction – Connectors, scripts, and tools to pull data
  • Cloud data lake – Stores raw data and handles transformations
  • Orchestrator – Schedules and manages ELT jobs
  • BI tools – Connect to transformed data for analysis

ELT Best Practices

Here are some best practices to follow for ELT success:

  • Choose right loading methods for data volumes and use case
  • Secure credentials and connections during extraction
  • Implement partitioning for historical loads
  • Profile data thoroughly – know your sources!
  • Adopt schema on read for transformation flexibility
  • Monitor and tune ELT job performance
  • Leverage native database transformations when possible

When to Use ELT?

The following are good use cases for ELT:

  • Streaming or real-time analytics on large data volumes
  • Frequent loading from many disparate sources
  • Ad hoc analytics with flexible transformations
  • Optimizing cloud data warehouse performance
  • Agile BI when business needs change often

For example, internet companies like Facebook use ELT heavily to analyze user activity data at massive scale and offer real-time experiences.

ELT Implementation Challenges

While promising, ELT also introduces new challenges including:

  • Storage capacity required for raw data in cloud data lake
  • Securing data during transfer and access control
  • Handling variety of data formats and schemas
  • Orchestrating complex workflows and dependencies
  • Reprocessing all data when logic changes

Mitigation strategies involve thorough planning, security controls, top ELT tools, and workload optimization.

The Future of ELT

What does the future hold for ELT? Here are some innovations expected:

  • More cloud-native ELT services embedded in platforms
  • ML-driven optimization of ELT performance
  • Automated insights into data profiles
  • Low/no-code self-service ELT for business users
  • Support for real-time streaming analytics at scale

As analytics becomes increasingly democratized, ELT will need to further simplify data integration.

Key Takeaways and Next Steps

To wrap up, remember these key points about ELT:

  • Loads data first, transforms later enabling faster analytics
  • Simplifies pipelines by consolidating inside cloud data lake
  • Scales for growing data volumes on cloud infrastructure
  • Delivers flexibility missing from rigid ETL
  • Best for streaming, ad hoc analytics and frequent data loads

Migrating from ETL can introduce challenges to overcome. But done right, ELT can future-proof data environments to drive innovation and growth.

I hope this guide has provided a comprehensive overview of ELT and how it powers modern data analytics. Please reach out if you need any help on your ELT journey!

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