With all modern-day businesses using multiple applications across their functions, data remains siloed away in their databases. To get a complete view of the ETL Meaning and your business, you would need to integrate data from all these sources into a single place for further analysis. Manually combining and cleaning data is ineffective as it is time-consuming and leads to errors. To remedy that, firms across the world use ETL(Extract, Transform & Load) process to perform data replication tasks more efficiently.
Ultimate ETL Guide: 10 Comprehensive Aspects
But how to implement that in my firm? Is it beneficial for my business? Are there any guidelines to follow while designing data replication architecture? No worries! We have prepared a well-researched and step-by-step ETL guide to answer all your queries.
ETL can be defined as a data integration process divided into three steps, i.e., extract, transform and load. It is primarily used to integrate data from multiple sources and load it in a centralized location, typically a Data Warehouse, for analytical purposes. During this process, necessary data is extracted from all data sources, transformed into a form suitable for analysis, and loaded into a destination based on the requirement. ETL enables organizations to quickly and efficiently gain insights from this readily available data, which can be used to make informed business decisions.
How does ETL work?
A well-designed data replication process allows for streamlining the process of extracting data from multiple sources, applying the data transformation, and loading it to the desired data warehouse. It consists of the following 3 stages:
The first stage in this process is to extract data from heterogeneous sources such as SaaS applications, APIs (Application Programming Interfaces), Transactional Databases, etc. Some of the data types are semi-structured JSON server logs, while others are structured outputs of widely used systems. The extraction can be done in a variety of ways such as:
- Partial Extraction: This is the simplest method of extracting data from multiple sources. In Partial Extraction, the sources notify the user when the record(s) have been updated or added.
- Complete Extraction: Some systems are unable to determine which data has been modified at all. In this instance, the only way to get the data out of the system is to do a thorough extract. This method necessitates having a copy of the previous extract in the same format so that the changes may be identified.
In this stage, the raw data gathered from the sources is transformed into a specific (analyzable) format that can be used by various applications. Data is cleansed, mapped, and converted to a specified schema, to meet operational requirements. Several types of transformations are used in this process to assure data quality and integrity. Data is generally loaded into a Staging Database which ensures a quick rollback in case something goes wrong. Moreover, you can prepare audit reports for regulatory compliance during this stage. Typically, transformation procedures are hosted in a staging environment where data may be maintained and processed through the many phases of:
- standardizing (converting all data kinds to the same format),
- cleaning (resolving errors and inaccuracies),
- de-duplication (identifying and deleting duplicate data records),
- mapping (combining data pieces from two or more data models),
- augmenting (bringing in data from other sources),
- validating (conducting audits to check data compliance and quality),
- verifying (encrypting or safeguarding information under the control of authorities such as the government or industry),
- masking (creating synthetic yet structurally similar data),
- filtering, ranking, and others.
In this stage, data is transferred from the Staging Database to a targeted destination (a Data Warehouse in most cases). This method might be straightforward or complex, depending on the application’s requirements. ETL tools or custom code can be used to complete each of these processes.
Instead of manually writing scripts from scratch, you can use ETL Tools already available in the market today, such as Hevo Data, Airbyte, and Stitch Data. You can classify them as follows:
- Enterprise Tools: Appealing to enterprises with old legacy systems, they are highly efficient and can effectively scale as the data workload increases. Example: Microsoft SQL server ETL Tool.
- Custom Tools: Providing better flexibility in terms of customization, these tools are written from scratch in Python or SQL, often requiring regular maintenance,
- Cloud-Based Tools: Connected to proprietary data sources, web apps, or on-premises sources, these tools copy, transform, and enrich data before writing it to data warehouses or data lakes. Example, Google Cloud ETL tools, and AWS ETL Tools.
- Open Source ETL Tools: The source code for these tools is freely available for users, though it might not be user-friendly for non-tech-savvy professionals, i.e., business users.
ETL Versus ELT
When comparing ETL vs ELT, the major difference between them is that ETL is a process where the data is transformed into a separate system before it is loaded into the target system, while ELT is a process where the data is transformed within the target system. ELT may require more powerful and expensive systems as the target system needs to handle both loading and transforming the data.
However, ELT allows for more efficient data processing by utilizing the power of the on-demand scalability and best-in-class performance of the data warehouse. Compared to ETL, ELT also offers lower data latency as the transformation step happens as soon as the data is loaded into the target system.
5 Common ETL Challenges
ETL process can become complicated if you need to replicate massive volumes of data from several sources into your data warehouse. Here’s a glimpse of the 5 common ETL obstacles that you find while implementing ETL for your business:
- Long-Term Maintenance: As your business grows, your replication process needs to be modified to handle the increasing data volume and speed effectively. This also includes monitoring, troubleshooting, and updating the ETL processes as needed, as well as maintaining the system’s documentation and knowledge base.
- End User Needs: Your architecture should be designed considering the end-user requirements, such as the data formats, data quality, and data replication frequency. You might also need to offer training and support to the end user to ensure that they can effectively use the data that is generated by the ETL process.
- Data Transformations: With raw data being present in multiple formats and structures, data transformations become complex and resource expensive. Inaccurate or incomplete transformations may lead to additional costs and resource demands.
- Tightly Coupled Components: When you need to scale your ETL pipelines, it is often difficult to make changes in particular components of the architecture without impacting the others.
- Potential Warnings: To ensure smooth data flow across various pipelines, you have continuously monitor and look out for warnings, such as unexpected changes in data quality or structure, increased errors or failure rates in the pipeline, performance degradation in the pipeline, etc.
Benefits of ETL
By executing a well-designed architecture in your firm, you can reap the following benefits:
- With data coming in from all your desired sources into a centralized repository, you can quickly get started by analyzing data and generating reports from a single source of truth.
- As there are multiple data quality and validation checks and transformations throughout the ETL process, you get consistent, complete, and accurate data for analysis.
- Manually extracting data from different sources takes a lot of time and effort and can often introduce errors in the data. An automated replication process eliminates manual intervention and efficiently replicates data using the resources optimally.
- The data replication processes enforce data governance policies, such as data security and compliance, thereby controlling access to and movement of data.
Role of ETL in Various Industries
ETL is widely used across different organizations worldwide to quench their data and provides them with fresh and accurate data ready for analysis. Some common examples are:
- Retail: Combining data from point-of-sale systems and inventory management systems allows users to generate a complete view of the sales, inventory, and customer behavior.
- Healthcare: You can get a comprehensive view of a patient’s health by using integrating data from electronic health records and medical devices.
- Finance: To know about the business’s financial health and cash flow, firms integrate data from banking systems and trading systems.
- Manufacturing: For better control and visibility over various components of your inventory, production, and logistics, you can integrate data from manufacturing systems and supply chain systems.
- Telecommunication: To analyze your customer’s network usage and the overall network performance, you can integrate data from call detail records and network devices.
- Government: To make effective public policies, governments often use ETL to integrate data from various government agencies to provide a complete view of the population and census.
- Energy: To get a better estimate of energy consumption and required production, firms integrate data from various sources, such as smart meters and weather data.
ETL Use Cases
In any organization, ETL can be used for various purposes, such as:
- Data Migration: It is often used in businesses that need to move their data from legacy or old databases to modern data warehouses in the correct format.
- Business Intelligence: Data replicated in the data warehouse is in an analysis-ready form, allowing business users to extract the relevant data to their BI tools for further analysis and reporting needs.
- Real-time Streaming Data: For Real-time analytics, businesses often need to extract data from various systems, transform it to fit a common format, and load it into a real-time streaming data platform.
- Master Data Management and Governance: With data security, regulations, and policies in place as part of the data replication process, firms can better protect and manage their data.
ETL Best Practices
You can follow a set of ETL best practices to ensure an effective data flow across your organization. Let’s look at the top 5 practices you can include white designing and implementing ETL for your business:
- Auditing: Including data auditing capabilities in your ETL process allows for continuous monitoring of data lineage from the source to the target and checking for any potential data abnormalities.
- Understand your Data: A clear understanding of the data, i.e., the format and structures in both the source and target schema, as well as the transformations required to be done.
- ETL Logging: By documenting all events occurring before, during, and after the data replication process, you can effectively pinpoint the point of major issues and tailor it to your needs.
- Data Cleaning: A data cleansing architecture should be in place to handle multiple cases, such as repairing mismatched data, enriching data by integrating from Purchasing, Sales, and Marketing databases, or getting rid of any serious data inaccuracies.
- Automation: With minimal human intervention, an automated replication process provides more accurate and fresh data from all your sources efficiently.
What does the future hold for ETL?
Here is what the future holds for ETL:
- Data Democratization: In the future, data will become more ubiquitous. Businesses want and need their employees to make data-driven decisions, so data tools that reduce manual processes to increase time for insight will gain steam. This also means that businesses would require different kinds of tools for different use cases. Pipeline tools for business users, streaming and batch capabilities based on the demand for real-time information, and full data transformation capabilities in IT. As organizations become more self-service based, they will continue to gain a competitive edge over the competitors who refuse to adapt.
- Exponential Data Growth: IoT data will continue to grow and play a crucial role in our lives. Based on recent statistics, we will continue to outgrow traditional Data Warehouses and need to move to the cloud. This increases the importance of cloud-native tools to manage, transform, and integrate the data in the cloud.
- More Artificial Intelligence and Machine Learning: Preparing the data for artificial intelligence and machine learning will become a more critical use case since digital assistance and the next-best-action technologies continue to expand on a very large scale.
Here’s a list of the most common ETL FAQs:
How is ETL used in big data?
It is used in big data to extract, transform and load large volumes of data into a centralized big data platform for analysis. Big data ETL may involve additional steps such as data sampling, filtering, and partitioning to optimize performance and storage requirements.
How can ETL improve the data quality?
ETL includes multiple data checks in all 3 stages, such as identifying invalid data, i.e., incorrect data types or missing values, converting all dates to a specific format or converting all names to uppercase, matching records based on a common identifier, etc.
How can ETL be integrated with data governance and security?
While executing ETL in your business, you can mask and encrypt your sensitive data while extracting, during transit, and during the loading process to prevent any unauthorized access. You can also assign roles and permissions to better control the user’s access to specific data.
Open Source ETL tools have gained more popularity than other tools. They are available for free with no upfront cost, come with a simple and accurate UI, and offer similar functionalities as other ETL Tools.
You now have a clear understanding of how ETL works and how to set up the architecture for your organization effectively. Based on your business requirements, you can select the right method to implement ETL for all your data needs. If you rarely need to replicate data from a few sources, then opting for custom scripts is a good choice.
However, if you need data every few hours from multiple sources with complex data transformations, then this can be a time and effort-intensive approach. Your engineering team would need to continuously monitor for any data leaks and fix them on priority. A more efficient and economical choice would be hoping onto a smooth ride with automated ETL tools like Hevo Data which offers 150+ plug-and-play integrations.
Visit our Website to Explore Hevo
Saving countless hours of manual data cleaning & standardizing, Hevo Data’s pre-load data transformations get it done in minutes via a simple drag n-drop interface or your custom python scripts. No need to go to your data warehouse for post-load transformations. You can run complex SQL transformations from the comfort of Hevo’s interface and get your data in the final analysis-ready form.
Want to take Hevo for a spin? Sign Up for a 14-day free trial and simplify your data integration process. Check out the pricing details to understand which plan fulfills all your business needs.
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