ETL/ELT Development Services for Data-Driven Decisions

Reliable data for easy decision-making often needs to be improved. However, this issue can be resolved with proper ETL/ELT practices. At CHUDOVO, our effective data prep approach enables fast and solid data-driven decision-making.

What is ETL\ ELT?

ETL, which stands for extraction, transformation, and loading, refers to gathering raw data from different sources, transferring it to an intermediate database for transformation, and ultimately loading the prepared data into a single target system. To integrate data and meet the requirements of relational database management systems and traditional data warehouses with OLAP support (online analytical processing), ETL tools are commonly used.

  • These tools ensure that data sets are structured and standardized through a series of transformations before entering the repository, allowing for more efficient utilization of OLAP tools and SQL queries.

    However, as data volumes and types expand, the ETL process has become increasingly inefficient, expensive, and time-consuming.

    It is where ELT comes into play, offering a solution to these challenges.
  • Due to the rapid growth of data sources and the increasing demand for processing large data sets for business intelligence and big data analytics purposes, ELT has gained popularity as an alternative to traditional data integration methods.ELT, which stands for extraction, loading, and transformation, reverses the order of the last two steps in the ETL process. After the data is extracted from databases, it is directly loaded into a central repository where all transformations occur without needing an intermediate database.

  • Modern technology makes This approach possible, enabling the storage and processing of massive amounts of data in any format. Apache Hadoop, open-source software originally designed for continuous data retrieval from diverse sources, regardless of their type, has played a significant role in enabling this methodology. Cloud data warehouses like Snowflake, Redshift, and BigQuery also support ELT by leveraging shared storage and computation resources, leading to high scalability.
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Applications for ETL and ELT

The decision between ETL and ELT is mainly based on a company's requirements for integrating data in cloud data warehouses, which have brought about new possibilities.

It's better to use ETL if...

ETL/ELT Development Services for Data-Driven Decisions

Extract data from multiple sources as needed
Transform the data to make the most out of it
Safe load of loads of data
Solid base to derive the insights from
Your single source of truth starts here
Extract data from multiple sources as needed
Our clients have no limitations on the number of data sources they can use, as we understand the power of modern technologies and how to optimize them for your benefit.
Transform the data to make the most out of it
After extracting or loading the data, make the most of it by transforming it. We support ETL and ELT methods, ensuring you can achieve high-quality transformations quickly and reliably (for ETL) or overcome database limitations for ELT.
Safe load of loads of data
Regardless of whether it is batch, micro-batch, or stream processing, your data will continuously flow towards your Data Warehouse (DWH) or Database (DB). We will diligently monitor its progress until every last piece of information reaches its intended destination.
Solid base to derive the insights from
Data is a game changer. Envision the transformative impact that clean, verified data from numerous pertinent sources can have on your business. Take action and partner with us to turn this vision into reality.
Your single source of truth starts here
Let the most crucial decisions made by your team be supported by the reliable ETL/ELT processes offered by a reputable vendor.

Check out the ETL/ELT Development Services that we offer

Hire ETL developers and experts in ETL migration to foster a culture of data within your organization. Entrust us with the tasks we excel in, allowing your talents to flourish in their areas.
ETL Migrations & Maintenance
If you need more than your software's performance and scalability, or it could be more user-friendly, consider switching to a better option. Our engineering teams excel at migrating from legacy to new ETL platforms like Oracle Data Integrator to Apache Airflow/AWS Glue or SSIS to Data Factory. Let's make the switch together.
ETL Automation
If you are dissatisfied with your current software's performance and scalability, or if it needs to meet your expectations in terms of user-friendliness, consider a transition to a superior alternative.

Our engineering teams are highly skilled in migrating from older ETL platforms like Oracle Data Integrator or SSIS to more advanced options like Apache Airflow/AWS Glue or Data Factory. Let us facilitate the switch for you.
DW Maintenance & Management
If you choose ELT, which will gain more momentum over time, investing in your DWH/DB capabilities is important. ETL is also crucial to a successful process, even though transformations don't happen within DWH. We have extensive knowledge in data visualization, in-database analytics, massively parallel processing, in-memory computing, and more for both ETL and ELT approaches. We're excited to share our insights with you.
Data Warehouse Automation
Implementing advanced DWH automation practices allows you to enhance your analytics capabilities in a shorter time frame significantly. DWH automation empowers you to conduct more comprehensive explorations of your source data and efficiently manage metadata, scheduling, deployment, and other related tasks.

Our experienced teams are adept at leveraging test automation and simplified DWH maintenance, which is guaranteed by system automation. By reducing the workload on our end, we can deliver greater value to you.

See how our ETL development and data warehousing specialists will blend into your project

We have created an onboarding framework that seamlessly integrates with your existing processes, allowing us to become an extension of your in-house team smoothly and effortlessly.

Key phases of ETL and ELT processes

ELT and ETL: a detailed comparison

The most important criteria for comparing ETL and ELT approaches to data integration have been highlighted to help you understand their advantages and limitations.
Maturity of Technology
ELT, being a new methodology, needs more development and professional competencies. Tools and systems for ELT are still in the early stages of development, making it more difficult to find professionals knowledgeable in the ELT process.

In contrast, ETL practice is already well-established and mature. With its long history of use, many well-developed tools, experienced professionals, and expertise are available.

• The key conclusion is that ETL is more reliable and mature.
Data type and size
ELT is a new methodology with limited development and professional competencies. Tools and systems for ELT are still in the early stages of development, and finding professionals who understand the ELT process can be challenging.ELT and structured data enable handling large volumes of unstructured and nonrelational data, which is crucial for big data analytics and business intelligence.

In cases where raw data originates from relational databases or requires extensive cleaning before being loaded into the target system, ETL is often the preferred choice.

• The main point is that the flexible and scalable ELT surpasses its predecessor by efficiently handling diverse data types in large quantities.
Storage/Data Warehouse Support
ETL is utilized in conjunction with OLAP data warehouses, legacy systems, and relational databases, but it does not have any provisions for data lakes. On the other hand, ELT is a contemporary approach that is compatible with cloud repositories and data lakes.

• Different use cases require different methods to be used.
ELT is not only more cost-effective than high-capacity in-house solutions, but it also has lower initial costs. Numerous cloud providers offer flexible pricing plans, which make them more favourable for users. Traditional ETL processes can be expensive due to the initial investment in hardware and the costs associated with the transformation engine's power. On the other hand, modern cloud-based ETL services offer flexible pricing plans based on the scale of usage requirements.

• ELT is more cost-effective than in-house ETL.
ELT is a cloud-based solution with automated features that require minimal maintenance. It contains pre-processed data that can be partially transformed for analysis. In-house ETL solutions with physical servers require a higher level of maintenance. However, cloud-based ETL solutions with automated processes are similar to ELT regarding maintenance requirements.

• The main finding suggests that ELT is better than in-house ETL regarding maintenance.
Load time
ELT, made possible by the processing capabilities of cloud solutions, allows for the direct uploading of data in its original format, resulting in faster load times compared to ETL. On the other hand, ETL requires the data to be transformed on a separate processing server before being delivered to the target system, causing slower data loading.

• ELT results in quicker loading times.
Conversion time
When using ETL, conversions are done on a different server and are much slower, particularly when dealing with large amounts of data. On the other hand, with ELT, the target system handles the conversions. By separating storage and computation, data can be stored in its original format and transformed as required, regardless of the data size, without impacting the speed.

• Conversions are faster with ELT.
ELT involves loading data as it is, without pre-processing or encryption, which can expose the data to tampering and result in non-compliance with standards. On the other hand, ETL enables editing, encryption, and deletion of vulnerable data before transferring it to the data warehouse. As a result, companies find it easier to safeguard their data and adhere to different compliance standards such as HIPAA, CCPA, and GDPR.

• The main point to remember is that traditional ETL has a compliance advantage.
Tools and competencies
Implementing both processes necessitates extensive knowledge of existing tools and advanced skills. Finding specialists with competencies in ELT methodology is challenging due to its immaturity. However, various programs, like Kafka, Hevo Data, Talend, etc., offer comprehensive ELT and ETL capabilities. To carry out tasks such as data transfer, export, transformation, and migration, experienced ETL specialists are required. Fortunately, it is relatively easier to find competent professionals in this field. Informatica, Cognos, and Oracle are some examples of traditional ETL tools.

• ETL tools and competencies are now more accessible in the market.


Are there any similarities between ETL and ELT? Answer
ETL and ELT share many similarities regarding their essential functionality and purpose. Both processes are used for data integration, transforming and loading data into a data warehouse or another target system. They involve extracting data from different sources, transforming it into a suitable format for easy loading, and then loading it into the target system.

ETL and ELT can handle diverse data types, including structured and unstructured data. They are also compatible with various data sources, such as cloud-based and on-site data warehouses. Both processes require careful data quality and governance consideration to ensure accuracy and compliance.
How to Choose Between ETL and ELT? Answer
When deciding between ETL and ELT, it's crucial to have a thorough understanding of your business needs, data sources and targets, data processing requirements, technical limitations, and ROI/TCO. Start by identifying the specific business needs and use cases for data integration, such as business intelligence or analytics. Next, assess the data sources and targets, considering factors like data types, volume, and location. Analyze the complexity of data transformation and the processing speed required. Consider technical constraints, such as processing power, data latency, and security concerns. Finally, evaluate the ROI and TCO by considering the costs of license, hardware, maintenance, and resource requirements. By carefully considering these factors, you can select the most suitable approach, ETL or ELT, that aligns with your business requirements and technical constraints.
Which is better, ETL or ELT? Answer
The choice between ETL and ELT is not straightforward, as it relies on the unique requirements and objectives of the organization. Each approach has pros and cons, and the optimal decision relies on factors like data size and complexity, processing speed, data quality, technical limitations, and considerations of return on investment and total cost of ownership (ROI and TCO).
What are some technical considerations for implementing ETL or ELT? Answer
Technical considerations for implementing ETL or ELT involve the processing power and complexity of data pipelines, data flows, and transformation processes. The security of sensitive data during loading and storage should also be considered. Additionally, it is vital to assess the scalability and adaptability of the data integration method to accommodate future growth and changing business needs.
Is ELT replacing ETL? Answer
ELT is gaining popularity as a data integration approach because of the advancements in cloud computing and the demand for quicker data processing. Choosing between ELT and ETL depends on a project's specific integration requirements and technical limitations.
Is ETL outdated? Answer
ETL is still widely used in data integration because of its capability to handle complex transformations and data quality management tasks. However, ELT has become a viable alternative for managing large amounts of data with faster processing times, thanks to the growing prominence of big data and cloud-based data warehouses.