ETL Modeling Process Made Easy: The Ultimate Guide 101

• April 27th, 2022

ETL Modeling Process FI

Companies in today’s environment collect data from a variety of sources for analysis. This data can be further processed with BI Tools to extract useful business insights, or it can be saved in a Data Warehouse for later use.

ETL stands for Extract, Transform, and Load, and it is the most common method used by Data Extraction Tools and Business Intelligence Tools to extract data from a data source, transform it into a common format suitable for further analysis, and then load it into a common storage location, usually a Data Warehouse.

In this article, you will learn about ETL Modeling Process in detail and the benefits of using ETL.

Table Of Contents

What is ETL?

Extract, Transform, and Load (ETL) is the process of combining data from numerous sources, translating it into a common format, and delivering it to a destination, typically a Data Warehouse, to gain important business insights.

Many organizations are attempting to move their data from legacy source systems to Cloud environments using ETL tools as a result of the introduction of Cloud technology. Legacy data sources such as RDBMS, DW (Data Warehouse), and others may lack performance and scalability. As a result, enterprises are migrating to cloud technologies such as Amazon Web Services, Google Cloud Platform, Microsoft Azure, Private Clouds, and others to improve performance, scalability, fault tolerance, and recovery systems.

ETL takes data from sources using settings and connectors, then changes it using computations such as filtering, aggregation, ranking, business transformation, and so on, all based on business requirements.

Need for Implementing an ETL Modeling Process

Companies use ETL technologies to streamline their data transformation processes for a variety of reasons. The following are some of the most common reasons for implementing ETL Modeling Process:

  • Making data easier to understand for management and external stakeholders.
  • More data from more sources can be handled than manual processes can.
  • The Data Aggregation method can be customized and automated.
  • As you collect more data and conduct more campaigns, you’ll be able to scale.
  • Improving efficiency, saving money, and reducing the number of hours spent on data transformation are all benefits.
  • Get the final data formatted precisely the way you want it.
  • Uploading data into a warehouse to make it easier to create reports and dashboards.
  • Human error margins are being reduced.

Difference between ETL and ELT

Before data is loaded into the Data Warehouse, traditional ETL operations extract and transform it. Instead of building up their own On-premise Data Warehouse, most firms today use Cloud-based Data Warehouses to store all of their operational data for analytical purposes. Although traditional ETL can still be used for Cloud-based systems, it is no longer regarded as desirable, and ELT Modeling Process is now favored over ETL Modeling Process.

Cloud-based systems are far more scalable in terms of processing and storage than traditional on-premise Data Warehouses when it comes to workload and data management. The ETL procedure is unlikely to benefit from the many advantages that a Cloud-based Data Warehouse provides. Cloud-based Data Warehouses are treated similarly to traditional Data Warehouses in the ETL process. As a result, the same set of performance bottlenecks exists, and switching to Cloud-based systems adds little value.

ELT (Extract, Load, Transform) is designed to make use of all of the benefits of a Cloud-based Data Warehouse, including massively parallel processing, elastic scalability, and the ability to swiftly fire up and pull down operations. This implies that all relevant data is retrieved from the sources and loaded directly into the Cloud. The Cloud’s enormous computing capacity is then used to perform the necessary data transformations as and when they are needed.

ELT operations, on the other hand, would necessitate significantly more storage space than ETL because raw data would have to be kept without any transformations. This means that the essential data would have to be taken and processed from the raw data storage every time it was needed for analysis.

Use Cases of ETL

The following are some of the most typical ETL Modeling Process use cases:

  • Cloud Migration: Since its introduction, Cloud Computing has aided businesses in transferring data from on-premises to the cloud to extract meaningful and valuable insights. Cloud-native solutions take advantage of the cloud’s advantages by allowing users to load data directly into the cloud and alter it within the cloud architecture. Data professionals might save money and time as a result of this.
  • Machine Learning and Artificial Intelligence: Many businesses have begun to investigate the influence of AI and machine learning on Data Science and Analytics. For large-scale AI and Machine Learning activities, the cloud is now the only viable option. For analytical training and model construction, as well as for automated Data Analysis, both methodologies require massive data stores. The key to transferring massive volumes of data to the cloud is to use cloud-based solutions.
  • Data Warehousing: ETL has traditionally been used to gather data from diverse sources, transform it into an analytics-ready, consistent format, and put it into a Data Warehouse. This enables business teams to examine it for commercial interests.
  • Marketing Data Integration: Customers interact with businesses across various channels today, making it challenging for marketers to track their activity across all channels to understand their behavior. These tools are crucial for integrating and gathering customer data from platforms such as eCommerce, websites, social media, mobile apps, and others. Other contextual data can also be integrated, allowing marketers to leverage hyper-personalization, offer incentives, improve user experience, and more.

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How does ETL Work?

Traditionally, data is extracted from one or more OLTP (Online Transactional Processing) databases throughout this process. OLTP applications have a lot of transactional data that needs to be combined and transformed into operational data. This is necessary since it can aid in data analysis and business intelligence.

This information is extracted and stored in a staging area, which is located between the data source and the data target. These tools can alter data in that staging region by connecting, cleansing, and otherwise optimizing it for analysis.

This tool may then put the data into a Decision Support System (DSS) database, where BI teams can perform queries and present reports and results to business users to aid in decision-making and strategy development.

Traditional tools, on the other hand, still necessitate a significant amount of effort from data experts, which is where new tools come in. With ELT, the Cloud has permanently changed. To perform transformations, sophisticated Cloud Data Warehouses like Google BigQuery, Snowflake, and Amazon Redshift no longer require external resources.

Data may be simply analyzed using pre-calculated OLAP summaries, making the process easier and faster.

ETL Modeling Process Stages

Here’s a breakdown of each stage of the ETL Modeling Process to help you better understand how it works.

ETL Modeling Process: Extraction

The extraction stage is the initial step of the ETL Modeling Process. If you have a lot of data sources, such as files, databases, spreadsheets, and so on, that you wish to convert into a new format, an ETL tool will aggregate it all for you. This data is placed in a “staging area,” which is a temporary storage location for the information.

Extraction methods are divided into two categories: logical and physical.

Logical Extraction

There are two types of logical extraction in the ETL Modeling Process:

  • Full Extraction: When extracting data for the first time, full extraction is used to extract all of the data at the same time.
  • Incremental Extraction: This method is used to extract data from the most recent successful extraction. You’ll be able to check the timestamp of each data extraction in an ETL tool, as well as examine recent modifications in a table.

Physical Extraction

Physical extractions are divided into two categories in ETL Modeling Process:

  • Online Extraction: When the ETL tool has a direct link to the data sources, it is called online extraction. 
  • Offline Extraction: When data isn’t extracted directly from the source, it’s called offline extraction. Instead, it is compiled into a flat file that can be used to manually generate charts and examine the data.

What Makes Hevo’s ETL Process Unique

Performing ETL can be a mammoth task without the right set of tools. Hevo’s automated platform empowers you with everything you need to have a smooth Data Collection, Processing, and ETL experience. Our platform has the following in store for you!

  • Exceptional Security: A Fault-tolerant Architecture that ensures Zero Data Loss.
  • Built to Scale: Exceptional Horizontal Scalability with Minimal Latency for Modern-data Needs.
  • Built-in Connectors: Support for 100+ Data Sources, including Databases, SaaS Platforms, Files & More. Native Webhooks & REST API Connector available for Custom Sources.
  • Data Transformations: Best-in-class & Native Support for Complex Data Transformation at fingertips. Code & No-code Fexibilty designed for everyone.
  • Smooth Schema Mapping: Fully-managed Automated Schema Management for incoming data with the desired destination.
  • Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
  • Quick Setup: Hevo with its automated features, can be set up in minimal time. Moreover, with its simple and interactive UI, it is extremely easy for new customers to work on and perform operations.

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ETL Modeling Process: Transformation

The second step in ETL Modeling Process is Transformation. The process of turning data gathered into a standard format that can be interpreted by the Data Warehouse or any BI tool is known as transformation. It “cleans” the data to make it more readable for the consumers. Sorting, cleaning, deleting extraneous information, and confirming the data from these data sources are some of the transformation processes.

The transform stage is when the data is transformed. This is where you apply your filters, functions, and other criteria. You’ll have clear goals and aspirations for how you want the data to be displayed once it’s completed as the user. Because ETL methods are very flexible, you can tailor them to your specific requirements.

For example, you might wish to merge several data sets to provide all of the information consistently. Alternatively, present sales data in a style that makes it simple to assess and detect strengths and weaknesses for geographic areas, sales teams, products, and other factors.

ETL Modeling Process: Loading

The final stage of the ETL Modeling Process is importing the data into a data warehouse. Loading is the process of storing converted data on a target, usually a Data Warehouse, but it also includes loading any unstructured data into data lakes, which may be used by various BI (Business Intelligence) tools to acquire important insights. Regardless of how many various types of data were processed as part of the ETL process, the result is a single clean collection of data that is ready to use.

Hevo as an ETL Tool

ETL Modeling Process: hevo logo
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Hevo is a No-code Data Pipeline that offers a fully managed solution to set up data integration from 100+ data sources (including 30+ free data sources) and will let you directly load data to a Data Warehouse or the destination of your choice. It will automate your data flow in minutes without writing any line of code.

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Its fault-tolerant architecture makes sure that your data is secure and consistent. Hevo provides you with a truly efficient and fully automated solution to manage data in real-time and always have analysis-ready data. Hevo also gives users the ability to set up an ETL modeling process that allows them to load data from a Data Warehouse of their choice to applications such as HubSpot, Salesforce, etc. using its Activate offering.

Let’s Look at Some Salient Features of Hevo:

  • Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
  • Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
  • Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
  • Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
  • Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
  • Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
  • Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.

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Hevo Pricing

Hevo offers two paid tiers, i.e., Starter and Business, along with its Free tier. The pricing for each paid tier depends on the number of events a user is expected to integrate. The Starter tier offers 20 Million events at $299/month, 50 Million events at $499/month, 100 Million events at $749/month, 200 Million events at $999/month, and 300 Million events at $1249/month. The Business tier is a custom tier for large Enterprises with complex requirements. Users can schedule a call with the Hevo team to create a tailor-made plan in the Business tier based on their unique requirements.

More details on Hevo can be found here, and our pricing can be found here.

Benefits of ETL Modeling Process

  • Time-Saving: When done manually, ETL Modeling Process takes a long time. It takes a lot of time and effort to write portions of code for each operation, handle data transformations, and establish internal processes. A well-designed ETL system allows you to take a more “hands-off” approach to process management, reducing the amount of time you spend on it.
  • Improved Accuracy: Many businesses hire a point person to oversee their many source data kinds. One person can be in charge of email marketing data, while another would be in control of Google Adwords data. When acquiring data, this might lead to discrepancies and inaccuracies. As a result, many businesses employ ETL solutions since they know the data they’re working with will be consistent and accurate. It lowers the chances of human or processing errors greatly.
  • No Developer Expertise Required: One of the most significant advantages of employing an ETL Modeling Process is that you won’t need to hire a developer. You don’t need to know any code, custom scripts, or languages. The best ETL tools on the market provide all of the features and tools you’ll need to set up and run data transformations on your own.
  • Increased Efficiency: Time is money, and time is saved by using efficient processes. By accelerating data transformation operations, ETL Modeling Process can save enterprises a significant amount of time each week. It’s just as crucial to implement ETL Modeling Process early on as it is to bring them in when your data processing responsibilities become too onerous to manage. The program allows you to scale up your processes without having to rewrite any of your existing techniques.

Conclusion

To be competitive, today’s businesses must make use of their data. However, you don’t have to rely on time-consuming manual methods to extract useful information from your data. You may save time, and money, and lessen the risk of a human mistake by using an ETL.

However, as a Developer, extracting complex data from a diverse set of data sources like Databases, CRMs, Project management Tools, Streaming Services, and Marketing Platforms to your Database can seem to be quite challenging. If you are from non-technical background or are new in the game of data warehouse and analytics, Hevo Data can help!

Visit our Website to Explore Hevo

Hevo Data will automate your data transfer process, hence allowing you to focus on other aspects of your business like Analytics, Customer Management, etc. This platform allows you to transfer data from 100+ multiple sources to Cloud-based Data Warehouses like Snowflake, Google BigQuery, Amazon Redshift, etc. It will provide you with a hassle-free experience and make your work life much easier.

Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand.

You can also have a look at our unbeatable pricing that will help you choose the right plan for your business needs!

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