Organizations of all sizes and industries now have access to ever-increasing amounts of data, far too vast for any human to comprehend. All this information is practically useless without a way to efficiently process and analyze it, revealing the valuable data-driven insights hidden within the noise.
The ETL (extract, transform, load) process is the most popular method of collecting data from multiple sources and loading it into a centralized data warehouse. During the ETL process, information is first extracted from a source such as a database, file, or spreadsheet, then transformed to comply with the data warehouse’s standards, and finally loaded into the data warehouse.
Best 10 ETL Tools in 2022
ETL is an essential component of data warehousing and analytics, but not all ETL software tools are created equal. The best ETL tool may vary depending on your situation and use cases. Here are 7 of the best ETL software tools for 2022 and beyond:
- AWS Glue
- Informatica PowerCenter
Oracle Data Integrator
Top 8 ETL Tools Comparison
Hevo is a Fully Automated, No-code Data Pipeline Platform that helps organizations leverage data effortlessly. Hevo’s End-to-End Data Pipeline platform enables you to easily pull data from all your sources to the warehouse, and run transformations for analytics to generate real-time data-driven business insights.
The platform supports 150+ ready-to-use integrations across Databases, SaaS Applications, Cloud Storage, SDKs, and Streaming Services. Over 1000+ data-driven companies spread across 40+ countries trust Hevo for their data integration needs. Try Hevo today and get your fully managed data pipelines up and running in just a few minutes.
Hevo has received generally positive reviews from users with 4.5 out of 5 stars on G2. One reviewer compliments that “Hevo is out of the box, game changer with amazing support”.
2. Integrate.io Platform ex. Xplenty
Integrate.io Platform (ex. Xplenty) is a cloud-based ETL and ELT (extract, load, transform) data integration platform that easily unites multiple data sources. The Xplenty platform offers a simple, intuitive visual interface for building data pipelines between a large number of sources and destinations.
More than 100 popular data stores and SaaS applications are packaged with Xplenty. The list includes MongoDB, MySQL, PostgreSQL, Amazon Redshift, Google Cloud Platform, Facebook, Salesforce, Jira, Slack, QuickBooks, and dozens more.
Scalability, security, and excellent customer support are a few more advantages of Xplenty. For example, Xplenty has a new feature called Field Level Encryption, which allows users to encrypt and decrypt data fields using their own encryption key. Xplenty also makes sure to maintain regulatory compliance to laws like HIPPA, GDPR, and CCPA.
Thanks to these advantages, Integrate has received an average of 4.4 out of 5 stars from 83 reviewers on the G2 website. Like AWS Glue, Xplenty has been named one of G2’s “Leaders” for 2019-2020. Xplenty reviewer Kerry D. writes: “I have not found anything I could not accomplish with this tool. Support and development have been very responsive and effective.”
3. AWS Glue
AWS Glue is a fully managed ETL service from Amazon Web Services that is intended for big data and analytic workloads. As a fully managed, end-to-end ETL offering, AWS Glue is intended to take the pain out of ETL workloads and integrates well with the rest of the AWS ecosystem.
Notably, AWS Glue is serverless, which means that Amazon automatically provisions a server for users and shuts it down when the workload is complete. AWS Glue also includes features such as job scheduling and “developer endpoints” for testing AWS Glue scripts, improving the tool’s ease of use.
AWS Glue users have given the service generally high marks. It currently holds 4.1 out of 5 stars on the business software review platform G2, based on 36 reviews. Thanks to this warm reception, G2 has named AWS Glue a “Leader” for 2019-2021.
Alooma is an ETL data migration tool for data warehouses in the cloud. The major selling point of Alooma is its automation of much of the data pipeline, letting you focus less on the technical details and more on the results.
Public cloud data warehouses such as Amazon Redshift, Microsoft Azure, and Google BigQuery were all compatible with Alooma in the past. However, in February of 2019 Google acquired Alooma and restricted future signups only to Google Cloud Platform users. Given this development, Alooma customers who use non-Google data warehouses will likely switch to an ETL solution that more closely aligns with their tech stack.
Nevertheless, Alooma has received generally positive reviews from users, with 4.0 out of 5 stars on G2. One user writes: “I love the flexibility that Alooma provides through its code engine feature… [However,] some of the inputs that are key to our internal tool stack are not very mature.”
Talend Data Integration is an open-source ETL data integration solution. The Talend platform is compatible with data sources both on-premises and in the cloud, and includes hundreds of pre-built integrations.
While some users will find the open-source version of Talend sufficient, larger enterprises will likely prefer Talend’s paid Data Management Platform. The paid version of Talend includes additional tools and features for design, productivity, management, monitoring, and data governance.
Talend has received an average rating of 4.0 out of 5 stars on G2, based on 47 reviews. In addition, Talend has been named a “Leader” in the 2019 Gartner Magic Quadrant for Data Integration Tools report. Reviewer Jan L. says that Talend is a “great all-purpose tool for data integration” with “a clear and easy-to-understand interface.”
Stitch is an open-source ELT data integration platform. Like Talend, Stitch also offers paid service tiers for more advanced use cases and larger numbers of data sources. The comparison is apt in more ways than one: Stitch was acquired by Talend in November 2018.
The Stitch platform sets itself apart by offering self-service ELT and automated data pipelines, making the process simpler. However, would-be users should note that Stitch’s ELT tool does not perform arbitrary transformations. Rather, the Stitch team suggests that transformations should be added on top of raw data in layers once inside the data warehouse.
G2 users have given Stitch generally positive reviews, not to mention the title of “High Performer” for 2019-2021. One reviewer compliments Stitch’s “simplicity of pricing, the open-source nature of its inner workings, and ease of onboarding.” However, some Stitch reviews cite minor technical issues and a lack of support for less popular data sources.
7. Informatica PowerCenter
Informatica PowerCenter is a mature, feature-rich enterprise data integration platform for ETL workloads. PowerCenter is just one tool in the Informatica suite of cloud data management tools.
As an enterprise-class, database-neutral solution, PowerCenter has a reputation for high performance and compatibility with many different data sources, including both SQL and non-SQL databases. The negatives of Informatica PowerCenter include the tool’s high prices and a challenging learning curve that can deter smaller organizations with less technical chops.
Despite these drawbacks, Informatica PowerCenter has earned a loyal following, with 44 reviews and an average of 4.3 out of 5 stars on G2—enough to be named a G2 “Leader” for 2019-2021. Reviewer Victor C. calls PowerCenter “probably the most powerful ETL tool I have ever used”; however, he also complains that PowerCenter can be slow and does not integrate well with visualization tools such as Tableau and QlikView.
8. Oracle Data Integrator – part of Oracle Cloud
Oracle Data Integrator (ODI) is a comprehensive data integration solution that is part of Oracle’s data management ecosystem. This makes the platform a smart choice for current users of other Oracle applications, such as Hyperion Financial Management and Oracle E-Business Suite (EBS). ODI comes in both on-premises and cloud versions (the latter offering is referred to as Oracle Data Integration Platform Cloud).
Unlike most other software tools on this list, Oracle Data Integrator supports ELT workloads (and not ETL), which may be a selling point or a dealbreaker for certain users. ODI is also more bare-bones than most of these other tools, since certain peripheral features are included in other Oracle software instead.
Oracle Data Integrator has an average rating of 4.0 out of 5 stars on G2, based on 17 reviews. According to G2 reviewer Christopher T., ODI is “a very powerful tool with tons of options,” but also “too hard to learn…training is definitely needed.”
No two ETL software tools are the same, and each one has its benefits and drawbacks. Finding the best ETL tool for you will require an honest assessment of your business requirements, goals, and priorities.
Given the comparisons above, the list below offers a few suggested groups of users that might be interested in each ETL tool:
- Hevo: Companies who are looking for a fully automated data pipeline platform; Companies who prefer to use a Drag-drop interface; Recommended ETL tool.
- AWS Glue: Existing AWS customers; companies who need a fully managed ETL solution.
- Xplenty: Companies who use ETL and/or ELT workloads; companies who prefer an intuitive drag-and-drop interface that non-technical employees can use; companies who need many pre-built integrations; companies who value data security.
- Alooma: Existing Google Cloud Platform customers.
- Talend: Companies who prefer an open-source solution; companies who need many pre-built integrations.
- Stitch: Companies who prefer an open-source solution; companies who prefer a simple ELT process. Companies who don’t require complex transformations.
- Informatica PowerCenter: Large enterprises with large budgets and demanding performance needs.
- Oracle Data Integrator: Existing Oracle customers; companies who use ELT workloads.
Who is ETL Developer?
An ETL developer is a type of software engineer that manages the Extract, Transform, and Load processes, implementing technical solutions to do so.
ETL developer is a software engineer that covers the Extract, Transform, and Load stage of data processing by developing/managing the corresponding infrastructure.
The five critical differences of ETL vs ELT:
- ETL is Extract, Transform and Load while ELT is Extract, Load, and Transform of data.
- In ETL data moves from the data source, to staging, into the data warehouse.
- ELT leverages the data warehouse to do basic transformations. No data staging is needed.
- ETL can help with data privacy and compliance, cleansing sensitive & secure data even before loading into the data warehouse.
- ETL can perform sophisticated data transformations and can be more cost effective than ELT.
ETL and ELT is easy to explain, but understanding the big picture—i.e., the potential advantages of ETL vs. ELT—requires a deeper knowledge of how ETL works with data warehouses, and how ELT works with data lakes.
How HotelTonight Streamlined their ETL Process Using IronWorker. For a detailed discussion of Harlow’s ETL process at work, check out Harlow’s blog at: http://engineering.hoteltonight.com/ruby-etl-with-ironworker-and-redshift
ETL Role: Data Warehouse or Data Lake?
There are essentially two paths to strategic data storage. The path you choose before you bring in the data will determine what’s possible in your future. Although your company’s objectives and resources will normally suggest the most reasonable path, it’s important to establish a good working knowledge of both paths now, especially as new technologies and capabilities gains wider acceptance.
We’ll name these paths for their destinations: The Warehouse or the Lake. As you stand here are the fork in the data road considering which way to go, we’ve assembled a key to what these paths represent and a map to what could be waiting at the end of each road.
The Data Warehouse
This well-worn path leads to a massive database ready for analysis. It’s characterized by the Extract-Transform-Load (ETL) data process. This is the preferred option for rapid access to and analysis of data, but it is also the only option for highly regulated industries where certain types of private customer data must be masked or tightly controlled.
Data transformation prior to loading is the key here. In the past, the transformation piece or even the entire ETL process would have to be hand-coded by developers, but it’s more common now for businesses to deploy pre-built server-based solutions or cloud-based platforms with graphical interfaces that provide more control for process managers. Transformation improves the quality and reliability of the information through data cleansing or scrubbing, removing duplicates, record fragments, and syntax errors.
The Data Lake
This new path how only recently begun to open up for wider use thanks to the massive storage and processing power of cloud providers. Raw, unstructured, incompatible data streams of all types can pool together for maximum flexibility in handling that data at a later point. It is characterized by the Extract-Load-Transform (ELT) data process.
The delay in transformation can afford your team a much wider scope of possibilities in terms of data mining. Data mining introduces many of the tools at the edge of artificial intelligence, such as unsupervised learning algorithms, neural networks, and natural language processing (NLP), to serendipitously discover new insights hidden in unstructured data. At the same time, securing the talent and the software you need to refine raw data into information using the ELT process can still be a challenge. That is beginning to change as ELT becomes better understood and cloud providers make the process more affordable.
Choosing the Right Path
To go deeper into all of these terms and strategies, consult our friends over: ETL vs ELT: Top Differences. You’ll find a nuts and bolts discussion and supporting illustrations that compare the two approaches in categories such as “Costs”, “Availability of tools and experts” and “Hardware requirements.” The most important takeaway is that the way we handle data is evolving along with the velocity and volume of what is available. Making the best call early on will have significant repercussions across both your market strategy and financial performance in the end.
To understand why Virtual Private Clouds (VPC) have become very useful for companies, it’s important to see how cloud computing has evolved. When the modern cloud computing industry began, the benefits with cloud computing were immediately clear; everyone loved its on-demand nature, the optimization of resource utilization, auto-scaling, and so forth. As more companies adopted cloud, a number of organizations asked themselves, “how do we adopt the cloud while keeping all these applications behind our firewall?” Therefore, a number of vendors built private clouds to satisfy those needs.
In order to run a private cloud as though it were on-premises and get similar benefits to having a public cloud, you need a multi-tenant architecture. It helps to be a big company with many departments and divisions that all use the private cloud’s resources. Private clouds work when there are enough tenants and resource requirements are ebb and flow so that a multi-tenant architecture works to the advantage of the organization.
In a private cloud model, the IT department acts as a service provider and the individual business units act as tenants. In a virtual private cloud model, a public cloud provider acts as the service provider and the cloud’s subscribers are the tenants.
Moving away from traditional virtual infrastructures
A private cloud is a large initial capital investment to set up but, in the long run, it can bring savings––especially for large companies. If the alternative is every division gets its own mainframe, and those machines are over-engineered to accommodate peak utilization, the company ends up with a lot of expensive idle cycles. Once a private cloud is in place, it can reduce the overall resources and costs required to run the IT of the whole company because the resources are available on-demand rather than static.
But not every company has the size and the number of tenants to justify a multi-tenant private cloud architecture. It sounds good in principle, but for companies at a particular scale, it just doesn’t work. The alternative was the best of both worlds; have VPC vendors handle the resources and the servers but keep the data and applications behind the company’s firewall. The solution was a Virtual Private Cloud; it is behind the firewall and is private to your organization, but housed on a remote cloud server. Users of VPCs get all the benefits of the cloud, but without the cost drawbacks.
Today, about a third of organizations rely on private clouds, and many companies embarking on the cloud journey want to know whether a private cloud is the right move for them; they also want to ensure that there are no security concerns. Without going too far into those debates, there are certainly advantages to moving to a private cloud. But there are disadvantages as well; again, it is capital and resource intensive to set up. However, running a private cloud can lead to significant resource savings, but some organizations do not have enough tenants to make hosting their own cloud worth it.
VPCs give you the best of both worlds in that you’re still running your applications behind your firewall, but the resources are still owned, operated, and maintained by a VPC vendor. You don’t need to acquire and run all the hardware and server space to set up a private cloud; a multi-tenant cloud provider will do all of that for you––but you will still have the security benefits of a private cloud.
How Anypoint Virtual Private Cloud provides flexibility
Anypoint Platform provides a Virtual Private Cloud that allows you to securely connect your corporate data centers and on-premises applications to the cloud, as if they were all part of a single, private network. You can create logically separated subnets within Anypoint Platform’s iPaaS, and create the same level of security as your own corporate data centers.
More and more companies require hybrid integration for for their on-premises, cloud, and hybrid cloud systems; Anypoint VPC seamlessly integrates with on-premises systems as well as other private clouds.
I like Google Drive Services and use iPhone. I need move my Photos and Videos from Google Photos to iCloud.
Google Photos’ free unlimited storage has been such a lucrative offer that even many iOS users have opted for the service over Apple’s own offering. But as per a recent announcement, this particular feature will be available only until June 1st, 2021 after which all “High” quality photos will also be counted against the storage space.
This is the main reason iOS users could be considering the option of moving back to iCloud storage.
If you are one of those and wondering how to transfer Google photos to iCloud, you have opened the right article. In this tutorial, we will first tell you how to export Google Photos’ photos within a few clicks. The next phase will be to move that exported data to Apple’s iCloud service without any hiccups. So without any further ado, let’s get started.
Steps to download all Google photos in one go:
- Visit Google Takeout (takeout.google.com) website on your desktop or mobile web browser.
- Click the “Deselect All” option and then select Google Photos service from the list of all Google apps and services.
- Scroll to the bottom and click the “Next” button.
- Set your preferences for download frequency, file type, and file size.
- Click the “Create Export” button to proceed further.
- This will begin the process of exporting photos in your chosen file type. It will take time depending on the size of the data that you are exporting. Google will send you an email with a download link once the process is complete.
Here’s how this looks:
Steps to download select Google photos:
- If you want to download only a few select photos, visit Google Photos (photos.google.com) website on your desktop or the app on your iOS device.
- On a desktop, select the photos that you want to download and use the “Shift + D” keyboard shortcut to begin the download process. You can also click the three-dot icon in the top right corner to click on the Download option.
- On the iOS app, tap and hold on a photo to get into the selection mode.
- Choose all the photos that you would like to download and tap on the sharing icon at the top.
- Next, tap the “Share to…” option and choose your preferred sharing destination like AirDrop and Save to Device. The latter option is meant for storing photos on your iOS device and is recommended only if you have enough storage space available.
Now that you have downloaded Google photos, it is now time to proceed to the second half of the tutorial i.e. to make them available on your iCloud storage.
Steps to transfer Google photos to iCloud:
- Visit the iCloud (icloud.com) website and choose the Photos option.
- Click the cloud icon in the top right corner with an upwards arrow. This is the upload option.
- It will open a dialogue box for you to navigate your desktop’s file locations. Choose the Google Photos folder(s)/file(s) that you downloaded through Google Takeout or Google Photos website and they will start uploading.
- If you used the iOS app to store photos locally on your iOS device, simply visit Settings->Photos to ensure that the iCloud Photos toggle is turned on. This will automatically sync your photos with the iCloud storage.