In today's digital world, data gets generated across different places and touchpoints, and most organizations use a data warehouse to unify it. The data warehouse pretty much serves as a single source of information for business intelligence and analytical processes.
But that said, moving the data from multiple pipelines into a unified repository isn't super straight. If proper processes are not followed, the data transfer might be incomplete, inaccurate, or even prone to security breaches.
To prevent this, companies follow a process called ETL. In ETL, data is extracted from the different sources, then transformed for consistency and loaded into the target data warehouse.
ETL isn't a one-time operation; it needs to run continuously and be robust and agile to keep the data warehouse in tune with business requirements.
In this guide, we tell you all about ETL processes in data warehouses, their importance, best practices, and commonly used tools for the process.
Why do you need ETL?
Adopting the ETL process has numerous benefits for companies, including creating a single source of truth or ensuring seamless data transfer.
For example, consider an e-commerce fashion store that wants to analyze product performance. The data store that customer feedback data is scattered across multiple channels, including social media pages, brand websites, customer data platforms, and email channels. The brand can unify all this data using the ETL process to analyze and understand its product performance.
While ETL created a unified database in this case, the process has several other advantages, such as:
- ETL integrates data from all data pipelines. This includes structured data sources like CRM and CDP and unstructured data sources like user feedback and social media interactions.
- ETL ensures that data quality is maintained throughout the extraction and loading processes.
- The process ensures that data transfer is clean, accurate, and complete without any duplication or errors.
- The process is a scalable data transfer system that can handle small or large data volumes equally efficiently.
- ETL is also used to migrate data to a new data warehouse when organizations switch from one system to another.
- ETL creates a unified, analytical data system that can be readily used for business intelligence, data mining, and interpretation. This is ideal for answering complex queries which cannot be solved by referring data in individual pipelines.
- Through the ETL process, organizations can securely migrate sensitive and encrypted data.
But why is an effective ETL process essential to data warehousing? To understand that, you first need to know the meaning of the ETL process and its three main components.
What is ETL Process in Data Warehouses
ETL stands for extract, transform, and load. These are the three processes through which data is retrieved from multiple pipelines and seamlessly transferred to a data warehouse. The ETL process steps are explained in detail below.
Extraction plays a crucial role in the ETL process. In this step, data is extracted from different sources such as ERPs, CDPs, or multimedia platforms and stored in an initial staging area before using it in the data warehouse system.
The source systems may contain inaccurately documented or duplicate information. Such data needs to be filtered out during the extraction process, which makes it a time-consuming step in the ETL process.
As data is constantly updated in the source system, organizations can follow two extraction data analysis methods:
- Full extraction
- Partial or incremental extraction
In the full extraction method, the complete data is extracted from all source systems and stored in the staging area. This method is suitable for the initial phases of ETL or for small amounts of data.
After full extraction, the updated source system data also needs to be loaded into the data warehouse. This ETL process can be started through partial extraction. In this method, only the updated data is extracted and loaded into the target system by referring to the timestamp of the last extraction.
After the data is extracted from the source systems, it is converted to a standard format through some rules and formulas. This step is called transformation. During this process:
- Data is filtered, cleansed, and validated to eliminate duplication and check for the authenticity of the data.
- Certain rules are applied to raw data to standardize the format, maintain consistency in units, and fill out the missing information.
- Data is sorted into groups based on certain attributes.
- Sensitive data under regulations is encrypted to protect the safety of the information.
- Data is audited to check for compliance of the schema with that of the data warehouse.
While these are standard transformation processes, you can also perform other operations according to your business requirement. For example, if you want to consolidate the revenue from all your products into a single datasheet, you can do that through a custom operation.
In the final step of the ETL process, the transformed data is loaded into the target warehouse. As data is constantly updated and extracted, the loading process also needs to be periodically repeated. This can be done through two methods:
- Full refresh: The complete data warehouse is refreshed to include new data. The older data is deleted and replaced along with updated data.
- Incremental loading: Only the updated data is loaded into the data warehouse, while the previously loaded data stays intact.
After loading, the process should be verified to ensure the data transfer is complete and accurate.
Most businesses have source systems that are frequently updated with new data. This means the ETL process must continue and run at a similar scale. While manually conducting the process is time-consuming, there are a number of ETL tools that can simplify the ETL process.
The Top ETL Tools Used By Companies
ETL tool help you automate the process and save time, effort, and resources. They provide a comprehensive and easy-to-use platform that can be customized for your business requirements. Some of the popular and highly useful ETL tools are:
1. Sprinkle Data
Sprinkle Data is a data transformation solution that simplifies the ETL process without the complications of coding. The no-code platform supports almost every data source and replicates them in near real-time. With Sprinkle Data, you can:
- Control and monitor the ETL process through statistics, dashboards, and real-time alerts.
- Automate schema mapping without requiring manual inputs.
- Ensure data security by storing data within your infrastructure and encrypting sensitive information.
- Enable seamless workflow between teams through Sprinkle Data's collaborative features.
Sprinkle Data creates a comprehensive ETL workflow that keeps you in control of every process while smoothly moving data to the target warehouse.
Hevo Data is another no-code data integration platform that helps you extract and move information from any data pipeline. Supporting more than 150 sources, Hevo completes the ETL process almost instantly and creates an analysis-ready data platform.
Hevo also lets you control the complete ETL process, from choosing sources for data extraction to determining how the extracted data is loaded in the target warehouse. You can analyze the process through real-time dashboards and customized data flow to seamlessly complete the ETL process.
Sybase is an ETL tool developed by SAP and is useful in the data virtualization and automating the data integration process. The tool is a synergy of two platforms:
- Sybase ETL Server: Used for the mainstream extraction and loading processes.
- Sybase ETL Development: Used to design data transformation projects.
One of the main advantages of Sybase is how easy it is to use. With ETL automation, simple GUI, and no training requirements, Sybase can be used by any team to complete their data integration processes.
4. Oracle Warehouse Builder
Oracle Warehouse Builder (OWB) is an ETL tool used to build data integration and replication processes. If you want a simple tool to manage data replication through graphical representations, then OWB is an excellent option.
With OWB, you can profile, transform, and cleanse the data to create integrated data models. You can also design target schemas and implement them to simplify ETL processes at different scales. OBW supports more than 40 vendors and file types, helping you replicate data from any source into the desired warehouse.
A data warehousing and integration platform, CloverDX helps you create data pipelines and automate them according to specific business rules and requirements. The platform supports almost all data sources and formats so you can move any kind of data to the target warehouse.
CloverDX is an excellent solution for enterprises that continually run their ETL processes. It helps you monitor the data flow, automatically handles errors, and alerts you of any complications to minimize downtime. It is also a scalable solution that allows you to code when required but can also automate processes on its own.
6. Mark Logic
MarkLogic enables you to eliminate data silos, integrate data as is, and monitor the data flow with its enterprise-grade Data Platform. With MarkLogic, companies have seen up to 4 times faster data integration and a 10-time increase in productivity.
The data platform creates a unified data warehouse to integrate data from all sources without compromising on quality. It also assists you in interpreting metadata through machine-learning knowledge models in its no-code engine.
With MarkLogic, you can leverage standard APIs to replicate data in various use cases, making it a complete solution for your ETL process.
While these tools simplify the ETL process in a data warehouse, you can leverage them to their full potential by following a set of rules for the data loading and automation processes. These rules will help you make the ETL process more efficient and minimize any errors.
Best Practices for the ETL process
Below are some ETL best practices that help you seamlessly extract data from and load data into your preferred data warehouse:
1. Only upload what you need to
You might have data in several pipelines. But you don't need to load all the same data streams together in the target warehouse. Cleanse the data, filter for duplication, and load data only from those sources that you require. This will make your ETL process more effective and keep your data warehouse clean.
2. Switch to incremental loading
Are you refreshing the entire data warehouse for every ETL cycle? That only makes the loading process lengthier and doesn't have any tangible benefits. Instead, only load the entire database for your first ETL process and then switch to an incremental load for data uploads. This enables you to load only updated data, making the ETL process quicker.
3. Ensure data quality at every step
Your data warehouse is the single source of truth for all business and analytical decisions. If you don't input quality data in the warehouse, you won't get quality insights to act upon. But ensuring data quality doesn't mean just cleansing the data but having a systematic quality assurance process at every step.
4. Automate the process
ETL automation makes the process faster and more accurate and helps you build custom solutions to specific requirements. For example, manually extracting and loading data from legacy systems to a modern data warehouse might be a challenge. But with the available ETL tools, legacy system data integrations can be handled smoothly.
To implement automation for ETL, you need to choose the right tool that allows you flexibility while also handling the process on its own whenever required. A great tool to do that and also follow the other best practices is SprinkleData.
How SprinkleData can help
SprinkleData seamlessly replicates data from multiple sources and loads it into the preferred data warehouse. Through its ETL automation processes and live monitoring features, it gives you regular updates and complete control over the ETL process.
The platform's features, like data security, automatic schema mapping, and data transformation, enable you to do much more with your data integration process through effective data flows.
ETL process in data warehouse is a necessity for every organization. We can help you simplify this process through our products. To know how to visit our website.
Frequently Asked Questions (FAQs)
- What is the ETL process in a data warehouse?
The ETL (Extract, Transform, Load) process involves extracting data from multiple sources, transforming it for consistency, and loading it into a unified repository for analysis and reporting.
- Why is the ETL process important for organizations?
Adopting the ETL process helps create a single source of truth, ensures seamless data transfer, and enables unified analysis from scattered data sources.
- What are the key advantages of using ETL in data warehousing?
ETL integrates diverse data pipelines, maintains data quality, ensures clean and accurate transfers, facilitates scalability, aids in data migration, and enables advanced analytics for complex queries.
- What are the main stages of the ETL process?
The ETL process consists of three main stages: extraction, transformation, and loading. Data is gathered, converted to a standard format, and then loaded into the target data warehouse.
- How does the extraction phase of ETL work?
Extraction involves retrieving data from varied sources like ERPs and multimedia platforms, filtering out inaccuracies or duplicates, and storing it in a staging area.
- What happens during the transformation stage of ETL?
In the transformation phase, extracted data is filtered, cleansed, standardized, sorted, encrypted (if necessary), and checked for schema compliance.
- What is the loading phase in the ETL process?
Loading is the final step where transformed data is loaded into the target data warehouse, either through full refresh or incremental loading methods.
- What are some popular ETL tools used by companies?
Commonly used ETL tools include Sprinkle Data, Hevo, Sybase, Oracle Warehouse Builder, CloverDX, and MarkLogic, each offering unique features for data integration and management.
- What are some best practices for an efficient ETL process?
Best practices include selective data uploading, transitioning to incremental loading, maintaining data quality at every step, and implementing ETL automation for speed and accuracy.
- How can SprinkleData help in simplifying the ETL process?
SprinkleData facilitates seamless data replication, ETL automation, live monitoring, data security, schema mapping, and transformation, making the ETL process more efficient and effective.