Skip to main content

When to Choose ETL vs. ELT for Maximum Efficiency


ETL has been the traditional approach, where data is extracted, transformed, and then loaded into the target database. ELT flips this process - extracting data and loading it directly into the system, before transforming it.

While ETL has been the go-to for many years, ELT is emerging as the preferred choice for modern data pipelines. This is largely due to ELT's speed, scalability, and suitability for large, diverse datasets generated by multiple different tools and systems, think about CRM, ERP datasets, log files, edge computing or IoT. List goes on, of course.

Data Engineering Landscape

Data engineering is the new kind of DevOps. With the exponential growth in data volume and sources, the need for efficient and scalable data pipelines and therefore data engineers has become the new standard.

In the past, limitations in compute power, storage capacity, and network bandwidth made the famous 3-word "let's move data round" phrase Extract, Transform, Load (ETL) the default choice for data processing. ETL allowed data engineers to shape and clean data before loading it into warehouses and databases. This minimized infrastructure costs.

Cloud data warehouses such as Snowflake, BigQuery, and Redshift are changed the game in past years. Modern data platforms offer virtually unlimited storage and compute, along with flexibility to scale up and down on demand. But they also come with a cost factor, plus the problem of ETL.

As a result, Extract, Load, Transform (ELT) is now the preferred approach for building data pipelines. ELT focuses on fast ingestion of raw data into data lakes and warehouses, deferring transformations to later stages. This unlocks quicker insights, greater agility, and lower costs for organizations, plus accelerates the move from DevOps (ETL) to DataOps (ELT) setups. And with data continuing to grow exponentially, data engineers now require scalable and flexible architectures centered around ELT to create future-proof pipelines. The ability to efficiently store, process, and analyze vast amounts of raw data is becoming critical.

ETL Explained

ETL (Extract, Transform, Load) is a data integration process that involves extracting data from source systems, transforming it to fit analytical needs, and loading it into a data warehouse or other target system for analysis.

The key steps in ETL are:

Extract - Data is extracted from homogeneous or heterogeneous sources like databases, CRM systems, social media, etc. The data can be structured, semi-structured or unstructured.

Transform - The extracted data is transformed to meet the requirements of the target system. This involves data cleaning, filtering, aggregation, splitting, joining, formatting, validating, and applying business rules.

Load - The transformed data is loaded into the data warehouse or other target database. This makes the data available for data mining, analytics, reporting and dashboards.

Some of the pros of ETL include:

  • Mature technology with many tools and expertise available
  • Handles complex transformations efficiently, especially for smaller datasets
  • Allows for data cleaning and preparation before loading into target
  • Facilitates data integration across disparate sources and formats
Some of the cons are:
  • Batch-oriented process, can't handle real-time data
  • Requires separate environment for transformations increasing complexity
  • Difficult to modify pipelines for new requirements
  • Not ideal for large volumes of data

ETL is commonly used in data warehousing and business intelligence to prepare integrated, consistent and cleansed data for analytics and reporting. It continues to be relevant today, especially when complex transformations are needed before loading data into relational data warehouses.

ELT Explained

ELT stands for Extract, Load, Transform. It is a process for moving data into a data warehouse or other target system.

The key steps in ELT are:

Extract - Data is extracted from various sources such as databases, APIs, files, etc.

Load - The extracted raw data is loaded directly into the target system such as a data warehouse or data lake, without any transformations.

Transform - Once the data is loaded, transformations and cleansing happen within the target system to prepare the data for analysis and reporting.

Pros of ELT:

  • Faster loading since no time spent on transformations beforehand. This improves overall processing speed.
  • Flexibility to transform data on an as-needed basis depending on downstream requirements.
  • Scales well with large datasets as loading is not bottlenecked by transformations.
  • Cost-effective as less processing power needed upfront.
  • Works well with unstructured and semi-structured data.

Cons of ELT:
  • Security and compliance issues as raw data is loaded which may contain sensitive information. 
  • Requires availability of powerful target system to handle transformations after loading.
  • May be challenging to find experts with ELT skills since it is a relatively new approach.

Use cases:
  • Loading data into a data lake where schema-on-read is applied after loading. 
  • Ingesting unstructured or semi-structured web, social media, IoT data. 
  • Quickly pre-loading raw datasets before applying complex transformations.
  • Frequent loading of streaming data from sources like sensors, mobile devices etc.

Key Differences Between ETL and ELT

When deciding between ETL and ELT, it is important to understand the key differences between the two approaches:




Less efficient for large datasets. Transformation before loading adds time.

More efficient for large datasets. Faster loading, transformation happens later.


Can be more costly (hardware needed for upfront transformations).

Lower costs (less upfront processing power needed).


Less flexible. If new uses emerge, re-extraction and transformation is required.

More flexible. Raw data allows adapting transformations as needed.


Difficult to scale with large, growing datasets. Transformations can bottleneck.

Scales well as loading is not slowed by transformations.

Big Data

Not ideal for large, unstructured datasets.

Better suited for unstructured data. Transformations easier after loading.

Data Quality

May provide higher quality data (transformations happen upfront).

Lower quality initially as raw data is loaded without adjustments.

Security & Compliance

Sensitive data can be transformed prior to warehouse loading.

Raw data loaded first, extra care needed for security and compliance.

Skill Set

ETL experts widely available. Mature tools.

Newer, so finding ELT skilled resources may be harder. Tools evolving.

In summary, while ETL is made for small, structured data that requires complex transformations (old data warehouses typically have only structured data, pressed into a schema), ELT is the better choice for large, diverse big data sets due to its flexibility, scalability and efficiency. 

Why Should You Use ELT Now?

1. Increased Speed and Efficiency

ELT allows for much faster data ingestion and processing compared to traditional ETL pipelines. Since transformations are done after loading the raw data into the data warehouse, the initial data intake is streamlined. This difference is especially impactful when working with massive datasets, where ETL can become bottlenecked. With ELT, you can load terabytes of raw data quickly into cloud data warehouses like Snowflake, then transform it later.

2. Flexibility

Storing the raw data directly in the warehouse provides more flexibility. Data can be transformed differently depending on the specific analytical needs, without having to repeatedly extract data from the source systems. ELT facilitates easy integration of new data sources and types into the pipeline. The raw data acts as a central source, which can then be transformed and structured as needed.

3. Performance and Cost-Effectiveness

ELT reduces the need for heavy transformation processing on the frontend, lowering the infrastructure costs. The raw data intake is fast and lightweight, while leveraging the scalable processing power of cloud data warehouses for transformations afterwards. This makes ELT a very cost-effective model, particularly when dealing with massive datasets. The pay-as-you-go nature of cloud data warehouses complements this nicely.

ETL vs ELT In Your Project

The choice between ETL and ELT depends on the specific data infrastructure, data types, and use cases. Here are some guidelines on when to choose one over the other:
ETL is a good choice:

  • The data requires complex transformations before analysis. ETL allows cleaning and transforming data before loading into the warehouse.
  • Compliance and data privacy are critical. ETL enables transforming sensitive data to ensure compliance before making it available for analytics.
  • The existing infrastructure relies on a traditional data warehouse. ETL is optimized for loading data into relational database systems.
  • The dataset is relatively small. ETL can efficiently handle small, complex datasets.
  • Data quality is a high priority. ETL allows thoroughly validating, cleaning, and transforming data for consistency before loading.

ELT is a better choice when:
  • Working with big data from diverse sources. ELT efficiently loads high volumes of structured, semi-structured, and unstructured data.
  • Flexibility in analysis is needed. Storing raw data allows analysts to transform it differently for various needs.
  • The infrastructure relies on a data lake. ELT integrates well with data lake architectures.
  • Real-time analytics is required. Loading data first enables faster queries for real-time insights.
  • Scalability is important as data volumes grow. ELT scales seamlessly with increasing data.
  • Cost needs to be minimized. ELT requires less processing power and is cost-effective.
So in summary, ETL adds more value when data quality and complex transformation are critical before analysis. ELT provides advantages when working with diverse big data sources and flexibility in analytics is important.

Some key points:

  • ETL involves extracting, transforming and then loading data into the target system. It works well for handling complex transformations with smaller, structured datasets.
  • ELT prioritizes loading data first, then transforming after. It is ideal for large, diverse datasets including unstructured data.
  • ETL offers benefits like data compliance, efficiency with complex transformations, and mature technology.
  • ELT benefits include speed, flexibility, scalability, cost-effectiveness and suitability for big data.

Factors like data volume and variety, infrastructure, compliance needs, and transformation complexity can dictate the best approach. And don't forget talent and integration costs. Investing into better and faster data management tools makes you fit for the next years, and reduces technical debt. Data pipelines are the underlaying working horse for data analytics, ML and AI. Setting on the older horse doesn't makes you win ;) 


Popular posts from this blog

Deal with corrupted messages in Apache Kafka

Under some strange circumstances it can happen that a message in a Kafka topic is corrupted. This happens often by using 3rd party frameworks together with Kafka. Additionally, Kafka < 0.9 has no lock at at the consumer read level, but has a lock on Log.write(). This can cause a rare race condition, as described in KAKFA-2477 [1]. Probably a log entry looks like: ERROR Error processing message, stopping consumer: ($) kafka.message.InvalidMessageException: Message is corrupt (stored crc = xxxxxxxxxx, computed crc = yyyyyyyyyy Kafka-Tools Kafka stores the offset of every consumer in Zookeeper. To read out the offsets, Kafka provides handy tools [2]. But also can be used, at least to display the consumer and the stored offsets. First we need to find the consumer for a topic (> Kafka 0.9): bin/ --zookeeper management01:2181 --describe --group test Prior to Kafka 0.9 the only possibility to get this inform

Hive query shows ERROR "too many counters"

A hive job face the odd " Too many counters:"  like Ended Job = job_xxxxxx with exception 'org.apache.hadoop.mapreduce.counters.LimitExceededException(Too many counters: 201 max=200)' FAILED: Execution Error, return code 1 from org.apache.hadoop.hive.ql.exec.MapRedTask Intercepting System.exit(1) These happens when operators are used in queries ( Hive Operators ). Hive creates 4 counters per operator, max upto 1000, plus a few additional counters like file read/write, partitions and tables. Hence the number of counter required is going to be dependent upon the query.  To avoid such exception, configure " mapreduce.job.counters.max " in mapreduce-site.xml to a value above 1000. Hive will fail when he is hitting the 1k counts, but other MR jobs not. A number around 1120 should be a good choice. Using " EXPLAIN EXTENDED " and " grep -ri operators | wc -l " print out the used numbers of operators. Use this value to tweak the MR s

GPT & GenAI for Startup Storytelling

OpenAI and Bard   are the most used GenAI tools today; the first one has a massive Microsoft investment, and the other one is an experiment from Google. But did you know that you can also use them to optimize and hack your startup?  For startups, creating pitch scripts, sales emails, and elevator pitches with generative AI (GenAI) can help you not only save time but also validate your marketing and wording. Curious? Here are a few prompt hacks for startups to create,improve, and validate buyer personas, your startup's mission/vision statements, and unique selling proposition (USP) definitions. First Step: Introduce yourself and your startup Introduce yourself, your startup, your website, your idea, your position, and in a few words what you are doing to the chatbot: Prompt : I'm NAME and our startup NAME, with website URL, is doing WHATEVER. With PRODUCT NAME, we aim to change or disrupt INDUSTRY. Bard is able to pull information from your website. I'm not sure if ChatGPT