For businesses dependent on large amounts of data, ETL software is essential. ETL allows you to extract data from a database, transform the data so it is organized and accurate, and load the data into a target destination, where you can put this data to work for your business.
There are a number of ETL software tools on the market. They vary their approach with regards to data structure, location and volume. Here are some factors to consider when choosing an ETL software for the first time, or if you need to switch from your current ETL tool.
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What is ETL and How Does it Work?
Before you can decide on an ETL tool, you need to understand what ETL is and how it works. How that process occurs is less apparent to those who do not regularly engage with these systems. In the modern data-driven world, businesses of all sizes collect vast amounts of information on a regular basis. The potential of this data is extraordinary. It can help elevate business intelligence to streamline marketing, product development, and even internal operations.
That's where the ETL process comes in. In short, ETL collects and transform from a number of different sources into one accessible, functional database. This is necessary because data sources are structured for different purposes. For example, much of a business' data may come from a source like MySQL or PostgreSQL, which is developed to make it easy for information to be entered and saved into the system. It is not, however, optimized to cleanse and analyze that data for the kind of analysis that's useful for more strategic purposes.
Comparing ETL to ELT
The features of ETL are quite clear. But you may also have heard a similar acronym: ELT. Both acronyms refer to the same words: Extract, Transform and Load. However, In ELT, the loading happens before the data is transformed.
This is a key difference between ETL and ELT. Again, the reasons for this benefit may be quite intuitive. When a business wants to glean usable intelligence from its mountain of data, it wants to do so as quickly as possible. This allows companies to be agile and responsive and to make relevant strategic choices. The process is much longer and more cumbersome if a business uses a system that has to transform the data after its loaded, in effect waiting to make the data usable until it is needed.
In addition, it is at that transformation stage that businesses discover what steps they must take to protect, encrypt or even delete particular data to comply with legal regulations. In other words, the transformation stage is best utilized as soon as possible, before a company needs to use the information for business intelligence. Expediting the transformation stage also ensures that there's less risk of security breaches that can subject the business to legal and financial liability.
ETL Technology Types
The need for ETL is clear, but extract-transform-load tools come in many shapes and sizes:
Batch Processing
Examples: Apache Hadoop, Apache Kafka
For more than two decades, "batch processing ETL" pipelines have allowed organizations to efficiently update their data warehouses. At one time, these systems operated through onsite servers. Now, most batch processing ETL software are moving to the cloud.
Just as the term implies, batch processing strategies involve the saving of data until a specific time, and processing it together (like delivering a batch of blueberries). Batch processing updates might happen during off-peak hours for massive nightly uploads, but they can also happen on an hourly, even minute-by-minute basis, when dealing with smaller batches.
As the most efficient and time-proven way to load data into a data warehouse, there are many reasons to rely on batch processing today:
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Efficient and stable: Instead of carrying out an ETL process multiple times—i.e., one time for every transaction—batch processing lets you carry out the ETL process once to update numerous transactions at the same time. This reduces the burden on system resources, offering a more efficient and stable data ecosystem.
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Can achieve near-real-time updates: Although batch processing ETL isn’t “real-time” per se, uploading batches every 60 seconds offers the benefit of a stable and secure ETL pipeline for near-real-time updates.
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Reduce load on the system: Batch processing lets you carry out data system updates during off-peak hours to limit the burden on server resources during daytime business hours.
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A long history of use and a well-understood process: Batch processing has a long history of being used by banks and other large organizations. Banks use batch-processing during the overnight hours to handle tasks like payroll, transaction settlements, and month-end reconciliation.
Cloud-Based
Examples: Integrate.io, Matillion
Onsite data warehouse servers are, for the most part, falling to the wayside. That’s because cloud-based data warehouses are more cost-effective and require zero maintenance for the user. Software service providers have also moved to the cloud or begun to offer cloud-services.
To make the cloud-migration process easier, cloud-based ETL software like Integrate.io, offer automatic data integrations, intelligent schema detection, and automatic ETL for the most popular SaaS cloud-service providers—such as Salesforce, Google Analytics, Heap, Facebook Ads, Chartio. This makes it possible to ingest all the data from your various SaaS platforms into a single data warehouse for analysis (with the touch of a button).
If you haven’t moved your data ecosystem to the cloud, this cost calculator for the cloud-based server Microsoft Azure is a great way to explore how much money cloud migration can save. Because most cloud servers offer pay-per-use pricing (and the ability to scale services up or down), you only pay for what you need, when you need it.
Open-Source
Examples: Apache Airflow, Talend Studios
There’s nothing better than free, and that’s what you get with open-source ETL tools (if you have the requisite data engineering skills to use them). Most open-source ETL tools assist with the management of batch processing and streaming scheduled workflows.
Scheduled workflow ETL technology—like Apache Kafka and Apache Airflow—allows you to automate the streaming of information from one data system to another. When building a data warehouse for machine learning insights, these workflows are essential.
Streaming
Nightly or weekly batch processing ETL is excellent for data archives that don’t need up-to-the-minute accuracy—like tax and payroll records. But if your customer orders a widget today, you want to send it immediately (not wait until tomorrow). Also, if a sudden influx of orders exhausts your inventory, you want to replenish supplies as soon as possible. This is where streaming real-time data comes in.
Here’s the catch: the barrier for entry is steep. You have to train someone or pay someone to build the system, not to mention someone has to be on payroll or retainer to maintain the system. Cloud-based ETL software like Integrate.io, solve the barrier for entry to streaming data integrations by blending Hadoop, Kafka, and Airflow, into an easy-to-use, graphical interface. That way you don’t have to be a data engineer to set up cutting-edge, streaming ETL integrations for your data warehouse.
Key Features of ETL Software
There are a number of factors that build upon the concerns of security and data usability. Combined, they can help you to find the right ETL tool for your business needs. So what should you look for when deciding on an ETL software tool?
Accessibility of business intelligence reports: Some ETL software expedites the reporting process for you. In the absence of such a feature, a business may have to develop its own reports from the loaded data. An ideal tool allows for both customized reports from transformed and loaded data, as well as some common, pre-filled data reports to help save time.
Data identification at all levels: An optimal ETL starts as soon as the data is extracted to identify what a company actually has. It is difficult to overstate the importance of this process. In order to use data effectively, and to uphold its responsibility as a repository of information, your ETL software tool must not only collect data but offer in-depth visibility to users. That way, businesses know right away what steps they have to take to protect that data.
Masking of personally identifiable information: Increasingly, businesses are called upon to properly protect all personally identifiable information they hold. This is the result of such sweeping laws as the EU's General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). The right ETL software can identify this sensitive data, mask and/or encrypt it, and load it into a data warehouse.
Data cleansing and optimization: Many solutions offer an efficient collection process, but fail to properly vet data or to combine it with existing information. An optimal ETL software solution would have built-in capabilities to clean up the data during the transformation and loading processes. The need for this is to reduce errors in the database and to reduce duplication and unnecessary information.
Using the Data Inside and Outside Its Destination: During the loading stage, it is essential for companies to be able to use data in the most effective way possible. Since the data lake contains both structured and unstructured information, processors should have the option to manipulate that data in the lake before using it in another form. At all stages of ETL, businesses should be able to see what information they have and to modify and restructure it as they see fit, in a secure and intuitive manner.
Safeguards for developers: As a final point, the ETL software you choose should allow in-house IT staff to manipulate the data flow, but without damaging the original data. Safeguards that stop more than one person from using the system, or a particular ETL flow, at the same time, as well as backups and other security measures, can help keep a company's data safe.
How to Choose the Right Software
Before opting for an ETL software, it is key for companies to think about the disparity of their data sources and how they ultimately want to use information. From there, they can find the right software to meet their needs.
Superior solutions not only manipulate data, but do so in a way that offers maximum benefit to the end user. Companies understand what kind of data they really have, and how they can use it. Importantly, they also have the opportunity to reflect on their responsibility with regards to that data.
Consider these factors when choosing between ETL software tools:
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A solution that helps you design data pipelines: A superior product allows your team to create an ETL that functions at optimal levels for your organization. That takes into account the complete pathway of your data, from the original sources to business intelligence reports and similar outputs. Look for a solution that is fully customizable, but intuitive to use in order to save your team time and resources.
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A solution that helps you control data flow: In the massive flow of information, it is possible for data to be lost, transformed in a way that is inappropriate or unusable, or duplicated. Your ETL should give you access to data while also determining how and when it goes through each stage of the ETL process.
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A solution that is transparent and easy to use: The volume of data reporting means your team members regularly interact with the ETL solution. They should find this process easy, free of frustrations such as complicated command processes. A superior ETL allows expert IT staff to manipulate code when it works for them, or to use a simple point-and-click process when that is most effective.
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A solution that contains checks and balances: Anyone using the ETL should know where data is at any given time. They should have the capacity to spot errors at the earliest stage. The solution should have periodic reports and status updates that are easy to comprehend. They should be able to produce sample reports before an entire job is complete, in order to rework the data flow if necessary.
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A solution that ensures a high level of security and legal compliance: Use of the ETL should elevate a business' compliance with laws surrounding the collection, use and storage of personal information. The automated process can ensure hashing of personal information so businesses can extract meaningful business intelligence while still randomizing the data. This reduces the likelihood of a security breach, or the unintended disclosure of personal data, that might otherwise occur through a manual masking or encryption process.
Final Remarks
There are a number of ETL software tools on the market. They vary in their approach with regards to data structure, location and volume. As a result, finding the right one for your business depends on a number of different factors, listed above. If you want an ETL software that includes all of these important factors, Integrate.io is a great option.
Integrate.io is a code-free, cloud-based ETL data integration platform. Our powerful on-platform transformation tools allow our clients to clean, normalize and transform their data while also adhering to the best data security and compliance practices. Check out our Top 7 ETL Tools to see how Integrate.io stacks up.
To find out more about ETL and how it can work for your organization, contact Integrate.io today for a personalized demo.