ETL/ELT Development Services for Data-Driven Decisions

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.

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...

You need compliance with established standards for protecting sensitive customer data is crucial. Healthcare organizations, for instance, must comply with the HIPAA Security Rule. Therefore, they may opt for ETL to mask, encrypt, or delete vulnerable data before uploading it to the cloud.
You only specialize in working with structured data and small pieces of information.
In real-world scenarios, the extraction, transformation, and loading (ETL) process is commonly used when upgrading legacy systems, specifically relational databases, managed internally by your company. One example is extracting Electronic Health Record (EHR) data while upgrading a legacy EHR system. In this case, patient data must be carefully selected and extracted from the existing system and transformed into the appropriate format to integrate with the new system seamlessly.
Real-time decision-making is crucial in your strategy. The advantage of ELT lies in its fast data integration, enabling the target system to convert and download data simultaneously. As a result, you can generate analytics that are close to real-time.
Your company specializes in handling large volumes of structured and unstructured data. A prime example is working with a transportation company that utilizes telematics devices in its vehicles. This technology generates substantial data from sensors, video recorders, and GPS trackers. Processing such extensive data necessitates significant resources and investments. However, with ELT, you can save costs while achieving enhanced performance.
Working with cloud projects or hybrid architectures often involves using modern ETL processes. While these processes have evolved to support cloud data stores, they typically require a separate engine to perform transformations before uploading the data to the cloud. However, ELT (Extract, Load, Transform) eliminates the need for intermediate processing engines, making it a more suitable choice for cloud and hybrid systems.
ELT is specifically designed to achieve the primary objectives of Big Data, which include handling large volumes of data, managing diverse data types, processing data at high speeds, and ensuring data reliability.
Your data science team requires access to all the "raw" data for machine learning projects.
Your project aims to leverage the scalability of cloud storage and data lakes to support its growth.

ETL/ELT Development Services for Data-Driven Decisions

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.

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.

Step 1

Our architect begins the onboarding process by conducting a thorough system overview. Collaborating with your team, they assess your current tech stack, provide alternative options, and determine the ideal team composition.

Step 2

Following that, our ETL/ELT engineers come into play. Whether or not you already have QA engineers, PM, or BA in your team, we can start the project with or without them. If additional support is required, we have a pool of over 250 talented professionals ready to assist you.

Step 3

We ensure a seamless transition by preparing acceptance criteria and test cases. It guarantees that existing functionality remains intact as we introduce changes.

Step 4

Prioritizing test cases allows us to verify critical business parameters effectively.

Step 5

At this stage, active knowledge transfer takes place. Our team develops a deep understanding of your business, enabling them to see the bigger picture behind your ETL/ELT project.

Step 6

We dive into system modernization and optimization with a comprehensive understanding of your system. Our engineers strictly adhere to your specifications and can suggest system improvements if desired.

Key phases of ETL and ELT processes

01

Extract

The first step in both processes
The process always begins by extracting and duplicating data from various sources such as ERP and CRM systems, SQL and NoSQL databases, SaaS applications, web pages, unstructured files, emails, mobile applications, etc. Due to the intricacies of each source system, the initial phase can be quite intricate.
Data is typically extracted in one of three ways.
• Full extraction is necessary for systems that cannot differentiate between new or changed records. In these cases, extracting all records, including old and new ones, is the only way to retrieve data from the system.
• Partial extraction with update notifications is more convenient for extracting data from source systems. This approach is possible if the systems send notifications about record changes. With this method, there is no need to download all the data, as only the relevant updates can be retrieved.
• Incremental retrieval, or partial retrieval without update notifications, allows retrieving only the changed records. This method is useful when update notifications are unavailable but still provides an efficient way to extract the necessary data.
Users must plan when using the ETL method to determine which data elements should be extracted for subsequent conversion and loading. In contrast, ELT enables the rapid extraction of all data. Users have the flexibility to decide later which data to transform and analyze.
02

Transform

The second step in ETL / third step in ELT
The transformation phase involves a series of actions to prepare the data for change, either to align it with the requirements of another system or to achieve a specific outcome.
Transformations may include the following actions:
• Sort and filter data to remove irrelevant items
• Remove duplicates and clean up the data
• Translate and convert data as needed
• Delete or encrypt sensitive information for protection
• Merge or split tables as required, and more.
These operations occur outside the target system's preparation phase in the ETL process. Data engineers are responsible for implementing these processes. For instance, in OLAP data warehouses, only relational data structures can be stored, so data needs to be converted to SQL-readable format first. Once the conversions are done, they cannot be changed, making ETL inflexible. If you want to apply a new type of analysis to the already converted data, you may have to rebuild the entire data pipeline.
On the other hand, the ELT method provides flexibility and convenience for transformations. Data is directly transferred to the data warehouse, data lake, or data lakehouse, where it can be validated, structured, and transformed in various ways and at any time. Additionally, since "raw" data is stored indefinitely, it can undergo countless transformations. Data analysts can assist data engineers in performing SQL transformations because everything happens within the target system.
03

Load

The second step in ELT/third step in ETL.
This step involves transferring the data to the target storage system for user access. The ETL process flow imports prepared data from an intermediate database into the target data store or database. It can be done through SQL commands for individual records or by batch loading with a script. On the other hand, ELT sends the raw data directly to the target storage location without using an intermediate layer.
It saves time in the extraction-delivery cycle. The data can be loaded completely or partially.
The ELT method is flexible and convenient for transformations because the data is transferred directly to the data warehouse, data lake, or data lakehouse. It can be validated, structured, and transformed in various ways. Additionally, the "raw" data can undergo numerous transformations as it is stored indefinitely. Data analysts can assist data engineers in performing transformations using SQL within the target system.

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.

Costs

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.

Maintenance

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.

Compliance

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.

FAQ

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.

ETL/ELT Development Services for Data-Driven Decisions

What is ETL\ ELT?

Applications for ETL and ELT

ETL/ELT Development Services for Data-Driven Decisions

Check out the ETL/ELT Development Services that we offer

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

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Key phases of ETL and ELT processes

ELT and ETL: a detailed comparison

FAQ