Most businesses store data on numerous platforms and applications. To enable the efficient analysis of all data, it is sensible to consolidate these data sets in one place in a standardized format. Companies use data warehouses to store their business data from various data sources in a single storage pool so that they can rapidly analyze the data and produce reports. Managing this data properly is crucial and can assist in realizing the warehouse's full potential.
DATAFOREST are specialists in the creation and maintenance of high-performance data warehouses and in the implementation of Extract – Transform – Load (ETL) strategies to accomplish this effectively.
You may be wondering: "What is ETL?" Well, now we'll tell you. In this article, we will explore:
- the ETL process
- Data Warehouses
- ETL Tools
- ETL vs ELT
- and many others
So, let’s dive in!
Introduction to ETL Tools
Businesses generate a huge amount of data every day. As well as supporting the day-to-day business process (e.g Inventory management), there is inherent value and insight to be drawn from this data and used to drive efficiency or performance improvements in the business.
To gain meaningful insights from this data, it is essential to export, process, transform and load it into a database or data warehouse. This is where ETL (Extract, Transform, Load) tools come in.
ETL tools are software applications designed to facilitate the integration of data from multiple sources, such as databases, flat files, and web services, and transform it into a format that can be analyzed and utilized by a business intelligence (BI) system.
How to Use Big Data
For years, Executives have warned that failing to embrace big data could cripple businesses and put them at a major disadvantage. Yet, 97% of organizations today invest in big data. Companies are already utilizing data warehouses in increasing numbers, with 54% of enterprises utilizing data solutions.
90% of business executives believe that data literacy is critical to their company's success and growth. They want to utilize their data to make better business judgments on the effectiveness of their marketing efforts and customer behaviors.
To truly exploit big data, brands have three primary tools:
- Data warehouses
- ETL tools
- BI tools
So how does ETL function, and do you need to employ an ETL tool to properly transfer information from one system to the next? We will discuss the significance of ETL and data warehouses, you will gain a better understanding of how to evaluate and use your data.
What is ETL?
So let's explain what ETL is.
ETL is a process that gathers data from various source systems, transforms it (using operations like addition, subtraction, and concatenation), and then loads it into the data warehouse system. It stands for Extract, Transform, and Load. In essence:
- Extract data from the source
- Transform data by combining, ensuring its quality, and removing duplicates.
- Load data into the target database (The data warehouse)
Data Warehouse ETL Procedure
Knowing what happens at each stage of the ETL process will make it simpler to understand how ETL functions.
In this step of the ETL architecture, data is extracted from the source system and staged. The staging area is like a temporary storage area before the data warehouse.
A staging area is necessary because this is where data transformation processing takes place. If the ETL Data Warehouse fails, the staging area also enables simple reversal and version restorations.
The ETL data warehouse extraction process uses pipelines to extract data from source systems. It links the staging area and the data source.
There are many different types of data sources, such as SaaS applications, mobile apps, sensors, APIs, legacy systems, transactional databases, ERP systems, CRMs, spreadsheets, etc.
There are three techniques for extracting data:
- Full Extraction
- Partial Extraction - without update notification.
- Partial Extraction - with update notification
Regardless of the technique employed, extraction shouldn't impair the source systems' responsiveness or performance. These database sources are active production systems so any lag or locking could have an impact on revenue.
During Extraction, some validation processing also takes place
- Data type accuracy
- Eliminate data duplication
- Key integrity - properly positioned and connected to the appropriate data
- Check for unwanted data
Data retrieved from the source server is unusable until it has been cleaned up, mapped, and transformed.
Transformation is the most important action, the ETL process adds value and transforms the data to enable insightful BI reports.
Filtering, de-duplicating, cleaning, validating, and authenticating the raw data are all aspects of transformation. Various rules and functions are applied to the data to eliminate redundancy, enforce consistency and convert it to a different format.
Data integrity problems can be resolved and multiple formats converted into a single consistent form. For instance, conversion of single string full names to first and last names, and changing all the dates to a single DateTime format.
Some important items to verify during data transformation include:
- Ensure sensitive data has been properly masked
- Missing values should be filled in using a sensible technique
- No required fields should be left blank
- Data filtered based on requirements, only necessary columns should be loaded
- Harmonize measuring units to a common unit, such as converting all the money to USD, lengths to meters, weights to kilograms, etc
- Transposing tables as necessary
The final step of the ETL procedure is data loading from staging into the target data warehouse. Often, huge volumes of data must be loaded into a data warehouse in a short period (nights). Therefore, performance optimization of the load process is necessary.
Checkpoint recovery mechanisms should be set up to recover from a load failure without losing data integrity. Data warehouse administrators must monitor, resume, or cancel loads based on server performance.
Three main types of loading exist:
- Initial Load
- Incremental Load
- Full Refresh
ETL vs ELT
ETL and ELT are subtle variations in the approach of different ETL tools.
ETL transforms data before loading it on the server, whereas ELT transforms it after. This is the main distinction between the two.
Each of these data transmission techniques has distinctive qualities and is appropriate for different data requirements.
ETL, an older technique, is the best choice for intricate transformations of smaller data sets. It's excellent for people who place a high priority on data security.
ELT, on the other hand, is a more recent technology that gives experts more flexibility and is ideal for processing both structured and unstructured data.
The choice of ETL or ELT will affect how you store, analyze, and process data. Therefore, it's crucial to take into account those factors before choosing between the two approaches.
Technical ETL and ELT Factors
It is critical to pay special attention to understanding how ETL and ELT work.
ETL/ELT systems should scale to meet your organization's data demands. Choosing a highly scalable system can reduce the cost of cloud-server processing and storage expenses.
Assure proper logging
Your data system must enable "accurate logging" of fresh data. Audit data after loading to check for missing or damaged files. When data integrity issues develop, ETL/ELT troubleshooting processes depend upon using the correct logging and auditing processes.
Dependability and steadiness
Overloaded ETL/ELT pipelines frequently crash and cause difficulties. Systems need to be fault-tolerant and recover from shutdowns, allowing your data to travel without being lost or damaged in the event of unanticipated problems.
Agility with many sources of structured and unstructured data
The data warehouse may need to combine data from disparate sources such as PostgreSQL, Salesforce, Cassandra, and internal finance systems. Some of this data may be missing the necessary data structures for analysis. ETL/ELT processing needs to handle both structured and unstructured data.
Increase the speed of data flow
When data warehouses and BI systems have access to current information, they can provide better, more accurate real-time insights. As a result, it's critical to focus on lowering end-to-end data latency.
ETL Tools: To Use or Not to Use?
The use of ETL tools in data warehousing is a complex issue since it is dependent on a variety of parameters such as the size of the company, the volume of data generated, and the complexity of the data sources. Yet, there are some advantages to employing ETL tools in the data warehousing process.
- ETL solutions aid automation, saving time while also reducing human error, which can be considerable when dealing with enormous amounts of data. Organizations can also process data in real time, ensuring that the data in the warehouse is up-to-date and accurate.
- ETL technologies help to assure data consistency and quality. ETL solutions can standardize data from many sources by transforming it into a structured format, making it easier to analyze and report on. This standardization also helps to assure data accuracy by identifying and correcting discrepancies during the ETL process.
- ETL tools contribute to lower data warehousing costs. Organizations can use ETL solutions to process massive amounts of data fast and efficiently, avoiding the need for extra infrastructure or software.
If you have any further queries about your ETL strategy, DATAFOREST can assist you.
Which ETL Tools are Better: Cloud or Open-Source?
When it comes to selecting an ETL tool, there are two basic options: cloud-based ETL programs and open-source ETL tools. But which ETL solutions are best for your company? Let's find out.
Cloud-based ETL tools are often very user-friendly, need no technical knowledge, and can be accessed from any location with an internet connection. Often subscription-based, using these tools means you only pay for what you use and there are no upfront hardware or software expenditures required. Cloud-based ETL technologies can also provide benefits including scalability, built-in security, and ease of interaction with other cloud-based services.
Open-source ETL solutions, on the other hand, are often free, making them an attractive alternative for enterprises with fewer resources. Open-source ETL systems are also more adaptable, allowing for customization and modification to meet unique business requirements.
If you still have questions about ETL tool selection, we can assist you to make the right choice. For additional information, please contact DATAFOREST.
ETL Tools Benefits
We have seen that ETL tools are really valuable. Yet, what precisely can they provide your company? Let us investigate this question.
Hand-coding and maintaining the ETL process might be advantageous in the near term, but as the number of data sources, data volumes, and other complexities grow, scaling and maintaining this architecture becomes increasingly difficult. ETL systems, especially cloud-based ETL tools, remove this impediment as they scale as your needs develop.
Having some of the processes on-site, some remote, and some in the cloud can make integration a headache. With cloud-based ETL technologies, one tool may manage the entire process, reducing superfluous levels of dependence.
Instead of your development team continually addressing problems and errors, by using an ETL tool maintenance is handled automatically as patches and upgrades propagate without intervention. ETL testing tools can assure data completeness, accuracy, and integrity.
Data storage and use is no longer the wild west. With, often complex, legislation such as GDPR and HIPAA, ETL tools help ensure that you're on the right side of compliance.
ETL in Data Warehousing
Organizations require data warehousing because it provides a single, consolidated source of truth for historical and present data. Data from multiple sources is aggregated into a single, uniform format, allowing data analysis to support data-driven decision-making.
One of the primary advantages of ETL in data warehousing is that it reduces data redundancy and inconsistency across many sources.
For example, data may be kept in different forms in separate systems, making consolidation for analysis problematic. ETL guarantees that data is converted and standardized before being loaded into the data warehouse, simplifying the analytical process.
Additionally, ETL in data warehousing provides data quality and consistency by cleaning, filtering, and enriching data. Duplicates, missing values, and inconsistent formats of data can all have a substantial influence on data analysis accuracy.
The ETL process guarantees that the data warehouse has clean, accurate, and trustworthy data, allowing enterprises to make solid data-driven choices. DATAFOREST can assist you in delving further into this question.
Pros & Cons of ETL process in data warehousing
While ETL offers numerous advantages, it also has certain disadvantages. Here are some advantages and disadvantages of ETL in data warehouse environments:
- Integration of data. ETL aids in the integration of data from several sources into a single data warehouse, allowing users to receive a comprehensive picture of their data.
- Data Accuracy. ETL operations can enhance data quality by cleaning, deduplicating, and verifying data as it enters the data warehouse.
- Faster Queries. ETL can assist to improve query speed by pre-aggregating data and building indexes, allowing users to run complicated queries faster.
- Historical Examination. Historical data may be captured and loaded via ETL, allowing users to study patterns over time and make better-educated business decisions.
- Scalability. ETL operations may be readily scaled up or down to match changes in data quantities or processing needs, allowing firms to manage more data as they grow.
- Complexity. ETL procedures can be difficult to develop and maintain, necessitating a substantial amount of knowledge and resources to create and administer.
- Cost. ETL methods may be costly to create and maintain, particularly when dealing with huge amounts of data, complex interfaces or sophisticated processing needs.
What Are the Different Types of ETL Tools?
ETL tools have been available for more than 30 years. As technology advanced, several sorts of solutions joined the market. Various pure-play ETL suppliers specialize in ETL, like Informatica.
We will go through many types of ETL tools in this section.
Open-source ETL solutions are free software that may be customized to meet your individual needs. Because the source code of open-source ETL solutions is publicly available, data scientists and data analysts may readily evaluate, edit, and improve their businesses' ETL processes.
Numerous open-source ETL technologies on the market ease the data management process. Here are a few examples:
- Pentaho Data Integration (PDI). PDI is an open-source ETL program known for its Spoon graphical user interface. It can build XML files to represent data pipelines and use the ETL engine to run these pipelines.
- Apache Kafka. An open-source, distributed event streaming technology that enterprises use to run high-performance ETL pipelines for streaming analytics. Because Kafka can analyze billions of events each day, it has grown in popularity among businesses that rely on real-time analytics to make swift decisions
Cloud-based ETL technologies enable enterprises to execute ETL processes in a cloud computing environment rapidly and efficiently. ETL cloud services are available from Microsoft Azure, Google Cloud Platform, and Amazon AWS. Certain ETL cloud services are extremely proprietary and can only be used inside the cloud vendor's framework.
Here are a few examples:
- Azure Data Factory. Azure Data Factory is a completely managed service that really can connect to a variety of cloud and on-premise data sources. It has the capability of copying, manipulating, and enhancing data before writing it to Azure data services. Data Factory can help with machine learning, Hadoop, and Spark transformations.
- Hevo Data. This is an end-to-end ETL solution that helps organizations extract data from many sources, conduct transformations, and store it in a centralized repository. Companies may receive a 360-degree perspective of their consumers by pulling data from over 150 sources, including SaaS, databases, apps, data warehouses, and more, using Hevo Data.
Why do you need ETL?
Extract, Transform, and Load (ETL) is a fundamental procedure in data warehousing that helps combine data from diverse sources into a single, uniform format that can be readily examined. ETL ensures that data is accurate, full, and dependable, laying the groundwork for informed business decisions.
What are the best practices for the ETL process?
Best practices for the ETL process include:
- Establishing clear data requirements
- Documenting ETL processes
- Using data profiling to identify data quality issues
- Designing for scalability and performance
- Using automation tools to reduce manual effort
- Creating a data quality assurance process
- Testing the ETL process thoroughly before deploying
What are the challenges of ETL in Data Warehousing?
Some of the challenges of ETL in Data Warehousing include:
- Ensuring data consistency across multiple sources
- Handling large volumes of data
- Dealing with complex data structures and formats
- Addressing data quality issues
- Managing the ETL process efficiently
- Ensuring scalability and performance of the ETL process
- Keeping up with changing data sources and business requirements
What are the applications of ETL in a data warehouse?
An enterprise may utilize ETL to:
- Extract data from older systems.
- Cleanse the data to increase data quality and consistency.
- Import data into the desired database.
What is the purpose of ETL?
ETL (Extract, Transform, Load) is used to combine data from numerous sources, transform it into a format appropriate for analysis and reporting, and load it into a destination system, which is generally a data warehouse or data mart. ETL is used to guarantee that the data in the target system is correct, consistent, and complete and that business intelligence and reporting tools may readily access and analyze it.
What factors should you consider when choosing an ETL tool for your organization?
Consider the complexity of your data integration requirements, the volume of data you need to handle, your budget, and the expertise level of your personnel when selecting an ETL solution. You should also analyze each ETL tool's features and capabilities, such as support for various data sources, data transformation capabilities, performance, and scalability.