One of the hallmarks of the Information Age is that data exists everywhere and, over the years, has become ubiquitous. Companies rely on data to create insights, set goals, optimize processes and improve decision making. But, as data complexity increases and organizations leverage data on a much larger scale, the importance of data quality has never been greater. Delivering trusted data in this context requires continuous monitoring of data and standardized data suitable for sharing across various teams and systems, supported by open source ETL tools.
Data management and integration has been a challenging task for many organizations. Not many years ago, data was collected and stored in data silos, making it difficult for data scientists to reconcile and integrate data from multiple sources and acquire accurate results from data analysis. The manual process of extracting data from different sources, transforming it into a standardized format, and loading the data into a centralized location is time-consuming, high-priced, and prone to errors.
To achieve highly scaled information sharing and avoid data silos, organizations employ the ETL practice to extract data, transform data, and load data and thus format, pass, and store data between systems. Considering the large volumes and still growing data sources that companies are handling between all business processes, ETL tools, especially open source ETL tools, effectively standardize and efficiently scale the data pipelines. Moreover, many organizations need their raw data to be transformed into usable formats because otherwise, the end result will be poor data availability, which ultimately can hinder the development of data culture.
Businesses today are data-driven, and 94% of enterprises agree that data is essential to business growth, yet, less than 40% of companies can gather insights from big data. While ETL processes are efficacious, it is only worthwhile with proper ETL tools. Still, finding the ETL and data integration system that works right for the company is exigent. On the other hand, open source ETL tools exist to halt such issues.
What Is Data Integration?
Data integration is a process of consolidating data from multiple source systems. The result is a unified set of information with consistent access for both operational and analytical uses that meets the information needs of all applications and businesses. It is considered one of the core elements of the overall data management process and the main objective – producing integrated (merged) data sets that are clean and consistent and meet all users’ information needs.
Integrated data is fed into transaction processing systems for a variety of business applications, data warehousing and data lakes to support business intelligence, enterprise reporting, and advanced analytics such as review analytics. Depending on the types of uses, various data integration methods have been developed, including batch integration jobs running at scheduled intervals and real-time integration completed on a continuous basis.
Data engineers develop data integration software programs and cloud platforms with data integration capabilities to facilitate an automated data integration process connecting data from data sources. This can be achieved through different data integration techniques.
Data Integration Techniques
Extract, Transform, and Load (ETL): copies of datasets from diverse sources are gathered together, systemized, and loaded into a data warehouse or database.
Extract, Load, and Transform (ELT): the data is loaded in its original form into a big data system and later transformed for particular analytics uses.
Change Data Capture (CDC): a method that identifies data changes in databases in real-time and applies them to a data warehouse or other repositories.
Data Replication: a technique in which multiple copies of data are created and stored in different databases for backup purposes, fault tolerance, and improved accessibility.
Data Virtualization: the data from different systems is virtually combined, retrieved, and manipulated without requiring technical details about the various data formats, and a unified view is created.
Streaming Data Integration: a real-time data integration and verification method in which different data streams are continuously integrated and fed into analytics systems and data stores, including in-stream processing and pipeline monitoring.
The Importance and Business Benefits of Data Integration
Most organizations have numerous data sources, commonly including internal and external ones. the Data integration tool enables businesses to combine data existing in different sources and provides a real-time view of business performance by reforming the data into valuable and meaningful information.
By integrating data as a strategic function, businesses obtain the benefits of advanced analytics processes and create multi-dimensional views of all organization’s aspects and key features such as customers, employees, filed that need improvement, and much more. Data integration improves decision-making capabilities by providing access to real-time data in a simple format to explore growth opportunities and discover potential bottlenecks before they happen.
From the aspect of customer experience, data integration enables analysis of real-time customer data and historical data, as the opposite of siloed structured and unstructured data sets that cannot provide a complete view. This enables organizations to reach out to customers at the right time with the right products and services, improve customer experience and satisfaction, and increase revenue.
Access to real-time business data is effective in terms of improving processes, reducing costs, and increasing production across various business units and business operations. Additionally, data integration increases productivity by simplifying the process of checking data from multiple sources.
In the long run, data integration helps organizations predict the future and analyze trends using chronological data. For instance, data analysis of historical along with real-time data can help process data and forecast customer needs and requirements demands. Organizations can also evaluate products and services quality and the necessity for improvements or new products, thus remaining ahead of the competitors.
What Is ETL?
ETL stands for Extract, Transform, and Load. The ETL process is a common approach to data integration and organization of data stacks. Typically, ETL processes comprise the following stages:
- Extracting data from data sources
- Transforming data into data models
- Loading data into data warehouses
ETL processes of integrating data combine data from multiple data sources into a single, consistent data storage that is loaded into a data warehouse or other targeted system. With the growth of database popularity in the 1970s, ETL was introduced as a process for data integration and data loading for computation and analysis. Eventually, ETL tools and ETL processes have become the primary method for processing data for data warehousing projects.
ETL is the bedrock of data analytics and machine learning workstreams. ETL tool utilized through a series of business rules puts forward data cleaning processes and organizes the data in a specific way that enables addressing of particular business intelligence needs, and at the same time, tackles the advanced analytics purposes that improve back-end processes within the organization.
Open source ETL tools have gained popularity because they allow organizations to reduce the size of their data warehouses, which, in turn, subsides the computation, storage, and bandwidth costs.
Process of the ETL Tools
The three-phase process of the ETL tool includes:
Data extraction refers to unifying structured and unstructured data from raw data to draw essential information critical to decision-making processes. ETL tools enable users to extract data and withdraw necessary details from collected data with just a few clicks.
Data transformation is the second phase of the ETL process and includes transforming the extracted information into a format understandable by the users, data warehouses, or business intelligence (BI). Commonly used transformation techniques include data sorting, data processing and cleaning, data migration, data aggregation, and verifying procedures. With this data preparation and cleansing, ETL software ensures improved quality of the data and establishes consistency.
Data loading is the third phase of the ETL process, in which transformed data is saved in a data warehouse. Proper data loading with a data integration tool into the target database is an essential step because the BI tools utilize the information to produce reports and provide insights for users at all levels, starting from management up to the business stakeholders.
Types of ETL Tools
Based on the infrastructure and supporting organization or vendor, ETL tools can be grouped into four categories. The four types of ETL tools available are:
Enterprise Software ETL Tools
This type of ETL tool is developed and supported by commercial organizations. The enterprise software ETL solutions tend to be more robust and mature compared to other tools available in the marketplace, and the reason behind it is that enterprises were the first to promote ETL tools. The enterprise software ETL tools offer graphical user interfaces (GUIs) for architecting ETL pipelines while supporting most relational and non-relational databases and extensive documentation for different user groups.
In addition to the functionality, the enterprise software ETL tools typically have a more significant price tag and require employee training and integration services due to the complexity.
Open Source ETL Tools
The open-source movement has risen, so it’s not surprising that open-source ETL tools have expansively entered the marketplace. Many source ETL tools available on the market are free and offer GUIs for data-sharing processes and information and data flow monitoring. The most easily recognizable feature and a distinct advantage of open source solutions is that companies can access the source code to study the tool’s infrastructure and extended capabilities.
It is essential to mention that the open source ETL tools can considerably vary in maintenance, documentation, ease of use, and functionality because they are often not supported by commercial organizations.
Cloud-Based ETL Tools
With the widespread adoption of cloud services and integration platform-as-a-service technologies, cloud service providers (CSPs) started offering ETL tools built on their infrastructure. The upper hand of these ETL tools is efficiency. Namely, cloud technology provides high latency, availability, and elasticity, making the computing resources scalable, ultimately meeting data processing demands. Organizations storing their data using the same CSP have extensively optimized the ETL pipeline since all processes occur within a shared infrastructure.
Cloud based ETL tools solely work within the CSP’s environment, making it the main downside of this ETL tool. Cloud based tools don’t support data storage in other clouds or on-premise data centers and don’t have the option to use the data without being shifted into the provider’s cloud storage.
Custom ETL Tools
Companies investing resources in development often opt for proprietary ETL tools using general programming languages (SQL, Python, and Java). The critical advantage of custom ETL tools is the flexibility to build a solution customized to the organization’s priorities and adjusted to workflows.
The stumbling block of this approach is the high prices required to build out a custom ETL tool. The process includes testing, maintenance, and updates, requiring time and money. Training of employees and additional documentation for onboarding users and developers is another aspect that increases the internal resources needed.
Key Consideration of ETLTools
When choosing an ETL solution to automate the data pipelines, several categories must be considered and evaluated. Depending on the data practices, use cases, and unique business models, standard criteria reflect the company’s needs.
How to Choose the Best Free and Open-Source ETL Tool?
Use Case and Target Audience
The critical factor for consideration for ETL tools is the use case. Depending on whether the organization is small and data requirements are minor, the ETL tool may not be as robust, but if the organization handles big data, the selection of an ETL tool must be suitable for complex datasets. The question should be: Can the team build API workflows via CLI scripting?
Some ETL tools are built for developers, meaning checking whether the team is familiar with the language is crucial. For example, Java-based tools need a different skill set than SQL-based tools. Still, if the company needs a tool to service business experts with no scripting knowledge, opting for a no-code graphical drag-and-drop graphical user interface would perhaps be wise.
Setting the budget is indispensable when evaluating ETL software and choosing an open source ETL tool to get up and running. While the open source ETL tools are typically free and renowned for their low entry costs, no vendor fees, no licensing, and no consumption caps, they may offer fewer capabilities or support than enterprise-grade tools. In some cases, companies should consider an ETL tool with higher upfront costs but lower maintenance requirements as a cost-effective solution in the long term. Therefore, another key consideration is the resources required to hire and retain developers if the software code is intensive.
The cost of building the initial data pipeline, annual expenses to manage complex data, pricing model predictability, and increased costs for increased data in data sources are some of the few categories that impact the selection between tools. However, the open source solution’s cost is not a single monetary cost. There are some questions that should be appraised:
- What skill level is required so that the team can keep the ETL system running smoothly?
- If the ETL tool doesn’t have built-in integration, what data source does the company use, and how easy will it be to keep up with the changes in the data sources and data models?
- Is the tool easy to use, and are the error logs robust? For example, if there is an error in one of the data sources, can the team track down the problem and fix it promptly?
Extent of Data Integration
ETL tools have the option to connect to various data sources and destinations. Therefore, one of the factors that should be considered when choosing software is an ETL tool that offers a wide range of built-in integrations. For instance, if the company needs to move data from Google Sheets to Amazon Redshift, the ETL tool must support such connectors because using a tool to design an integration with a data source is more straightforward than writing code. Still, having a tool that takes care of the integration of all data sources is the best option possible.
No matter how easy-to-use the ETL tool is, you might need help at some point. Consequently, regardless of whether the tool has a marginally better feature set, if the company cannot get the critical work done because of a specific issue, everything is pointless. For example, the best ETL tool ever is not the best pick if the data fails to process and no support is available.
All support aspects available when things go wrong, from phone calls to documentation and Stack Overflow coverage, are important. For that reason, it is vital to ask questions before choosing an ETL tool:
- Is there high-quality live support for the ETL tool?
- How good is the documentation?
- Is there an online community for the tool? If yes, how long it takes until a question is answered? Are the responses helpful? Is the online community knowledgeable and experienced?
From the plenty of options of ETL tools, the best ones can be customized to meet the data needs of different teams and business processes. There are automated features, such as de-duplication, with which the ETL tools enforce data quality and reduce the labor required to analyze datasets.
Level of Customizability
Organizations should always opt for the only open source ETL tool or tools that meet their requirements for customizability and the technical expertise of the IT team. Not all open source ETL tools offer the same level of customizability, particularly in the extractor features. For instance, some companies need batch processing or filtering in their data extraction scripts to avoid heavy data loads, which is why checking if the open source tool used is flexible and can be adjusted to specific needs is crucial.
A start-up company will likely need built-in connectors and transformations, while large enterprises with bespoke data collection will need the flexibility to craft modifications supported by a team of data engineers themselves.
Some other considerations for open source ETL tool include the following:
- The level of automation provided
- Level of security and compliance of the open source ETL tool
- The performance and reliability of the tool
- Data transformation capabilities
- Scalability of the ETL tool
Limitations of Open Source ETL Tools
Beyond doubt, open source ETL tools are a solid foundation for performing Extraction, Transformation, and Loading to build basic data pipelines. While being a backbone for the data pipeline for organizations of all sizes and industries, they have several limitations, particularly in the field of providing support.
The open source ETL tools are still developing, and as work-in-progress tools, the most common limitations include the following:
Enterprise Application Connectivity
The open source ETL tools lack proper enterprise integration patterns and connectivity with organizations’ in-house software and applications.
Management & Error Handling Capabilities
These open source ETL lack error-handling capabilities.
Specific ETL tools available on the market are not able to connect with various data management software of RDBMS systems, which can interfere with the performance of the data pipeline when data is collected from these data sources and multiple platforms.
Large Data Volumes & Small Batch Windows
Some of the open source ETL tools have the capability to analyze large data sets, but the downside is the fact that they can process data solely in small batches, which can reduce the effectiveness of the data pipeline.
Complex Transformation Requirements
Organizations that handle big data and have very complex data transformations can’t use open source ETL tools because of the lack of support for performing such transformations.
Lack of Customer Support Teams
The open source ETL tools are managed by communities and developers worldwide, meaning they often lack specific customer support teams to handle issues.
Poor Security Features
In terms of security features, open source ETL tools typically are worse than vendor tools. Some tools have poor security infrastructure, from regulation-compliant data processing (GDPR) to lineage tracing, making them prone to cyber-attacks while data is at rest and in transit. Companies that handle sensitive data should pick tools that have the option to be run on-premise, where users can configure the data security rather than web-based or cloud-based tools.
Data Transformation Capabilities
Open-source ETL tools most notably vary in how they perform transformations. Typically, the no-code tools offer only canned transformations. Therefore, depending on the company’s needs, users should consider a tool with a complete set of flexible transformations via scripting in programming languages (Python, SQL, and Java).
Some open source ETL tools lack scalability, and hence, when choosing an open source ETL tool, companies need to pay attention to whether the open source tool can grow with the data needs.
Difficult to Navigate and Debug
Most of these ETL tools are interface-driven, making them difficult to navigate and debug; consequently, the open source tools introduce reproducibility issues.
How Does KNIME Help With ETL?
Methodical and future-proof ETL tools enable users to get swift and reliable insight into their data. KNIME Analytics Platform is a free and open source platform that guarantees powerful, scalable, repeatable, and reusable ETL processes.
With KNIME, users can extract, transform, and load data.
KNIME services ensure powerful and flexible file handling, a feature that enables data workers to spend more time on the data analysis, with no limits on the amount and close to no limits in formats. Users can access multiple disparate data sources with its dedicated database, web, and cloud services connectors.
By utilizing KNIME ETL software, data experts can blend, validate, authenticate, and perform data anonymization within KNIME, in-database, or in the cloud. This ETL tool also integrates Python, R, and H2O, enabling users to send data directly to PowerBI and Tableau. Starting from structured, textual, chemical data, audio, and image to model files, users can read and write with KNIME.
Last but certainly not least, with KNIME, users can load data to target destinations, and the databases can be updated with dedicated database writer, connector, and utility operations.
Redfield and KNIME partnership: Who We Are
At Redfield, we help customers get started on projects efficiently. Our dedicated team has over 19 years of experience in advanced analytics and business intelligence. Blended with 4 years of successful partnership with KNIME, Redfield experts are focused on developing core KNIME functionality:
- KNIME Analytics Platform Setup
- KNIME Server Setup
- KNIME Server Managed Service
- Rapid development of prototype
- Reference implementation
- End-to-end data science project
- Education & support
- Cloud implementation in AWS
- Node development
We are happy to announce that over the years, the Redfield team developed 20 commercial nodes and extensions designed for your business needs, and our 50 satisfied customers from all over the world in various spheres, including Finance and Financial Services, Media, Government, Healthcare, Engineering, and Telecommunications are proof of our diligence, commitment, and enthusiasm.