We will generate an estimated 181 zettabytes of data annually in 2025. This staggering volume demands a highly efficient data integration approach like no other.
While ETL (Extract, Transform, Load) has worked wonderfully for a long time, the sheer scale and diversity of modern datasets necessitate a paradigm shift to ELT (Extract, Load, Transform).
With over 80% of data projected to be unstructured by 2025, ELT is the need of the hour. It incorporates its ability to utilize its innate cloud data warehouse power for in-database transformation to help your organization realize the full potential of big data.
Do you want to tap into ELT’s ability to reduce data latency and streamline workflows?
But what is ELT? Let’s explore the answer by understanding its working, benefits, tools, and best practices.
| TL;DR: The article argues that the massive volume of data generated today makes the traditional ETL (Extract, Transform, Load) method obsolete. It champions ELT (Extract, Load, Transform) as a more efficient, modern approach. The core of ELT involves loading raw data directly into a cloud data warehouse before transforming it, which simplifies architecture, increases agility, and reduces costs. Get a detailed view of how ELT works, its key benefits, best practices for implementation, and a tool guide. |
What is ELT – Extract, Load, Transform
ELT (extract, load, transform) is a modern data integration approach to load extracted raw data into a repository, usually a cloud-based data lake or data warehouse. After loading, this data is transformed for analysis. It has become a global phenomenon in recent years, owing to the growth of powerful data lakes and data warehousing technologies.
94% of businesses note improved security after moving to the cloud.
Most traditional approaches prioritized the transformation of data over loading it. ELT, however, interchanges the latter two steps with another for a more efficient data ingestion.
Let’s explore its core processes!
How ELT Works: Core Process
Businesses with heavy and diverse data inflows are turning towards ELT. If you are in a similar situation, this approach can empower you to tap into your data’s true potential with unrivalled efficiency and speed.
Each aspect of this acronym is a step that might seem simple but critical to its working.
Let’s understand how ELT works by exploring its core process:
Extract in ELT
The extraction step is a common point between this approach and its predecessor. It remains the same and includes extracting data from various sources.
1. Identifying Data Source
Teams begin by identifying and connecting to multiple systems consisting of data. There is no end to the number of these sources, and they may include:
- NoSQL Databases: MongoDB, Neo4j, Cassandra, Redis, etc.
- Relational Databases: Oracle, MySQL, PostgreSQL, SQL Server, etc.
- Flat Files – JSON, XML, CSV, etc.
- SaaS Applications: Google Analytics, Salesforce, Zendesk, and Marketo, often accessed through APIs.
- Streaming Data: Real-time data from application events, weblogs, IoT devices, etc.
2. Data Retrieval
Since the step is about extracting, it does not stop at source identification. The extraction process includes retrieving all the necessary data without much initial manipulation. Data retrieval captures the information in its current format for minimal premature structural changes.
Load in ELT
Loading is the step that distinguishes ELT from the traditional ones. All the extracted data is loaded directly into the target system without any transformation or manipulation. Here are a few of its features:
1. Direct Ingestion
All the data extracted from the prior step is directly loaded into the system without changes to its format. This system is usually either a modern data warehouse or a data lake that can manage gigantic data volumes.
Top examples are:
- Modern Data Warehouses: Google BigQuery, Snowflake, Azure Synapse Analytics, Amazon Redshift, etc.
- Data Lakes: Google Cloud Storage, Azure Data Lake Storage, AWS S3, etc. These are usually used alongside query engines like Presto or Apache Spark.
2. Schema-on-Read
Traditional systems follow a predefined schema before writing data, also known as schema-on-write. ELT, however, works on a schema-on-read principle.
Here, the data’s format and structure are defined and interpreted as the data is queried and analyzed, not during the loading phase. It increases the system’s flexibility to handle scaling data structures.
3. Scalability and Performance
Data lakes and cloud-based DWHs support parallel processing for their scalability and high performance. The loading step uses these capabilities for efficiently ingesting humongous datasets.
Transform in ELT
After loading the data into the target environment, the transformation stage becomes imperative to maximize the benefit of adopting and implementing this approach.
1. Data Transformation
The raw data is prepared in the right format for analysis and reporting. The key data tasks here are cleaning, restructuring, integrating, enriching, filtering, and type conversion.
2. Using Target System Capabilities
ELT uses the processing functionalities and powers of the chosen data lake or DWH for transformations. Most of these systems have parallel processing capabilities, built-in SQL engines, and specialized functions for efficient large-scale transformations.
3. On-Demand Transformation
ELT states that transformations are to be done on a need-only basis and according to the company’s specific needs. This is unlike the ETL approach in data warehousing, wherein all the transformations are done before loading the data.
Key Benefits of Implementing ELT
If used correctly, all this data holds unprecedented growth potential. ELT is turning it into actionable intelligence by restructuring how your business approaches data pipelines. Rewiring this procedure brings many advantages.
Some of the top key benefits of implementing ELT in your organization are:
1. Simplified Architecture
The ELT approach eliminates the need for a separate transformation layer. Raw data is sought directly from various source systems and then loaded into the intended data lake or DWH in its existing form. Data transformation happens within the target system itself.
It saves a lot of infrastructure and skilled personnel costs for your organization. The development cost is low, and so are the overhead expenses. Simple architecture also means fewer failure points, leading to better legibility and iteration.
2. Agility & Faster Time-to-Insight
One of the most immediate benefits of ELT is the shorter time needed to get the data available for analysis. Since the architecture is simple and time-saving, raw data is swiftly loaded into the system.
Better agility allows business users, data scientists, and analysts to commence investigations and quickly generate important insights.
Faster time-to-insights are useful in faster iteration on analytical projects and quicker responses to any market changes. Your organization will ultimately become more data-driven.
3. Flexibility and Adaptability
Constant change is a fixed feature of the modern data ecosystem. New data sources are emerging left and right, current schemas are evolving, and analytical requirements are shifting rapidly.
ELT takes a ‘load-first, transform-later’ approach for better flexibility and adaptability. Any change in the source system’s structure or new analytical questions seeking changed data aggregations or transformations make the raw system data highly adaptable and processable.
Your development overhead will reduce because complex ETL pipelines are no longer re-engineered for every schema modification.
4. Cost-Effectiveness
ELT is a cost-effective path because it uses the on-demand feature of cloud-based data platforms. The processing needs dictate whether the transformation resources are scaled up or down. It is to ensure computing power is used only when data is being actively transformed.
5. Synchronization with Modern Data Warehouses
ELT is an approach that stems from the need to optimize modern data warehouse features. Cloud-based warehouses like Google BigQuery, Azure Synapse Analytics, Snowflake, and Amazon Redshift have distinct features for strategically using ELT. It also fully utilizes the Massively Parallel Processing (MPP) architectures of modern DWHs.
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ELT vs ETL – Unlocking Their Differences
Modern enterprises must pick the right data integration strategy. While both ETL and ELT are vital for consolidating data, they are based on fundamentally different architectural philosophies. The one you choose will impact your data warehouse’s scalability and flexibility to handle evolving analytical demands in a cloud-first world.
Here is a tabular representation of ELT vs ETL.
| FEATURE | ELT (Extract, Load, Transform) | ETL (Extract, Transform, Load) |
| Data Types Handled | Structured, semi-structured, and unstructured data. | Structured data. |
| Transformation Timing | After loading into the system. | Before loading into the system. |
| System Suitability | Modern DWHs and data lakes with flexible schemas. | Traditional DWHs with rigid schemas. |
| Staging Area | Usually eliminates the necessity for a distinct staging area. | Often needs a distinct staging environment. |
| Cost | Lower upfront costs with varying cloud processing costs. | Higher upfront costs for ETL infrastructure and tools. |
Build Your ELT Pipeline with the Right Tools and Technologies
It is important to understand that the effectiveness of your ELT pipeline primarily depends on the tools and technologies you choose. Every stage of the process can yield the best outcomes only when given the right means to work with.
Let’s understand the stages of building your ELT pipeline and the right tools and technologies for each.
1. Data Extraction
At the first stage, you connect to different sources and retrieve data from them. The tool choice relies on the type of sources.
ELT/ETL Platforms with Pre-built Connectors
Many all-inclusive data integration platforms come with diverse pre-built connectors. These simplify connecting to other sources as the coding is minimized.
Top examples: Talend, Fivetran, Informatica Cloud Data Integration, and Airbyte
Built-in Connectors and Drivers
Most modern data integration platforms and DWHs come with native drivers and connectors, including SaaS applications (Google Analytics, Salesforce), databases (NoSQL, SQL), and file formats (JSON, CSV).
Top examples: Google Cloud Dataflow Connectors, Snowflake Connectors, and AWS Glue Connectors
Custom Connectors and APIs
Source system’s APIs help build custom connectors for less common or proprietary data sources.
Top programming languages: Java, Python
Change Data Capture (CDC) Tools
CDC tools are best used for ingesting real-time or near-real-time data. These can capture all the changes from source databases to ensure the DWH stays up-to-date.
Top tools: AWS DMS, Apache Kafka Connect with CDC connectors, and Debezium
2. Data Loading
Involves efficient transfer of data into a data lake or a DWH.
ELT Platforms
Such platforms usually make the most of the existing warehouse capabilities.
Most often chosen ELT platforms are Snowflake, Google BigQuery, and Amazon Redshift
Native Bulk Load Utilities
Modern DWH optimizes bulk loading utilities to rapidly ingest data cost-effectively.
Top examples: Snowflake's COPY INTO, Google BigQuery's bq load, Amazon Redshift's COPY, and Azure Synapse Analytics' COPY INTO
Data Lake Storage
Data lakes like Azure Data Lake Storage, Amazon Simple Storage Service, and Google Cloud Storage need a more efficient tool for moving data files into the storage layer.
More efficient tools can handle immense scale and diverse data types. They are useful for handling complex data pipelines for consistent and fast data flow into the storage layer.
Top tools: AWS CLI, AWS DataSync, Azure CLI, Azure Data Factory Copy Activity, gsutil, and Google Cloud Data Transfer Service.
Data Streaming Platforms
Streaming platforms handle real-time data by continuously ingesting high-velocity data streams directly into a data lake or warehouse. It direct ingestion enables immediate analysis and processing for up-to-the-minute insights.
Top streaming platforms: Amazon Kinesis, Google Cloud Pub/Sub, and Apache Kafka
3. Data Transformation
The system’s power is optimally used to transform your data to the desired format.
Data Transformation Services
Cloud platforms offer powerful data transformation services. These highly flexible services can efficiently execute complex transformations directly within or atop your warehouse to optimize data preparation workflows.
Top cloud platforms: Google Cloud Dataflow, AWS Glue, and Azure Data Factory Data Flows
SQL
Most relational data warehouses have Structured Query Language (SQL) as the fundamental language for data manipulation. It’s an integral language that can define, transform, and query your data.
Procedural Languages and Extensions
Beyond standard SQL, many warehouses offer procedural language extensions for more complex and logic-driven transformations. It enables conditional logic, error handling, and iterative processes directly within the DWH environment.
Top procedural extensions: PL/pgSQL in PostgreSQL
dbt (Data Build Tool)
dbt is a popular open-source tool that revolutionizes data transformation by building sophisticated data models directly within the warehouse. Data engineers and analysts use familiar SQL and Jinja templating for code reusability and version control.
Python and Other Scripting Languages
For data transformation flexibility, you can integrate scripting languages (like Python) within the warehouse or even use external compute engines (like Spark). It will allow you to customize data manipulations for advanced analytics or ML pipelines.
4 Orchestration and Workflow Management
Automating and monitoring the ELT pipeline is necessary to maintain reliability and efficiency. Robust workflow management and orchestration tools move the data smoothly to identify issues.
Open-Source Orchestration Tools
Powerful open-source orchestration tools have comprehensive capabilities for defining, monitoring, and scheduling complex data workflows. They are great at handling failures, offering insights into your ELT pipelines, and managing dependencies.
Top orchestration tools: Apache Airflow, Dagster, and Prefect
Cloud Workflow Services
Major cloud providers offer managed workflow orchestration services fully integrated into their respective ecosystems. They thus offer seamless automation and management of data pipelines and other cloud resources.
Top workflow orchestration services: Google Cloud Workflows, AWS Step Functions, and Azure Data Factory Pipelines
ELT Platform Schedulers
Many dedicated ELT platforms have their own built-in scheduling and monitoring features. These integrated schedulers can simplify data ingestion and transformation jobs for convenient and streamlined operation.
ELT platforms: Airbyte and Stitch
Our seasoned professionals for data warehouse consulting can transform your raw data into actionable insights and drive informed business growth.
Best Practices for Successful ELT Implementation
Effective ELT implementation is not just about choosing tools but rather involves strategic planning and execution. You must adhere to established best practices to maximize data pipeline efficiency, drive accurate analytics, and ensure data quality.
Here are a few best practices for successful ELT implementation for optimized performance, minimal errors, and real business value.
1. Pick the Right System
Pick a data lake or DWH that fits your data volume, velocity, type, and processing requirements. Consider their scalability and transformation capabilities.
2. Prioritize Data Quality
Implement basic validation processes to ensure good data quality during the phases of extraction and loading. This will help you catch major issues upfront before transformation, saving time and money.
3. Embrace Schema-on-Read
The target system and transformations should revolve around flexibility. They should be able to accommodate your changing analytical needs and data sources.
4. Optimize Loading for Performance
The system’s bulk loading capabilities should be used for efficient data ingestion.
5. Design Reusable Transformations
Segregate complex transformations into smaller components that are both manageable and reusable.
6. Implement Robust Data Governance
Set clear procedures and policies for accessing data, tracking lineage, and monitoring quality and security within the environment.
7. Automate & Orchestrate
Workflow management tools are a great way of automating the ELT pipeline for higher efficiency and reliability.
8. Monitor & Alert Proactively
Track pipeline performance, set up alerts, and identify errors through comprehensive monitoring.
9. Iterate & Adapt
Leave scope to iterate on the pipeline design and transformations with the changing understanding of business requirements and data.
10. Maintain Documents
Focus on maintaining clear documentation regarding your data sources, transformations, pipeline orchestration, and loading processes.
ELT for Data-Driven Success with Aegis Softtech
Adopting an ELT strategy is decisive for truly achieving data-driven success. ELT streamlines data pipelines by using the colossal processing power of modern cloud data warehouses. Consequently, your organization experiences significantly enhanced scalability and drastically reduced time-to-insight.
With a well-executed ELT implementation, your business can tap into the full potential of your vast datasets for agility, deeper insights, and a substantial competitive advantage.
Are you ready to use your data for unprecedented growth?
We, at Aegis Softtech, are here to help your organization harness the actual power of ELT. Our team of seasoned experts designs, builds, and manages efficient pipelines tailored to your unique business needs.
We not only aid in choosing the right cloud data warehouse but also automate data loading and transformation.
FAQs
Q1. What is ELT used for?
This data integration process moves and prepares data for analysis, particularly in a cloud-based environment.
Q2. What is ELT vs ETL?
While there are many differences, the main difference lies in the order in which data transformation happens. ETL transforms data before loading it into the system, while ELT transforms data after the loading process.
Q3. What is the main purpose of ELT?
Its main purpose is to enable companies to integrate and process gigantic data from multiple servers.
