Is your organization ready for cloud-based ETL tools? With things like business intelligence (BI), data-driven strategies, and comprehensive analytics becoming increasingly integral parts of today's long-term business strategies, it's no surprise that ETL platforms hold a more prominent role than ever.

When evaluating a cloud-based ETL tool, you should consider the: 

  1. Intended destination of your data after processing. 
  2. Data sources you need to integrate with.
  3. Internal resources are available to implement the tool.
  4. Required ongoing developer maintenance.
  5. Ease of connecting to new sources in the future.

So, what is ETL, what are your ETL options, and how do you find the best choice for your business? Here's what you need to know about cloud-based ETL tools along with some information about, an ETL platform offering advanced features, ease of use, and scalable pricing. Let's dive in. 

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Extract, Transform, Load (ETL) platforms have long been a staple tool for many businesses working with big data. More recently, however, they've also begun to take center stage with small-to-medium-sized businesses as these companies try to wrangle their data sources and make the most out of the information at hand.

So how does it work, and how do you know if you need cloud-based ETL tools for your business? 

Do You Need Cloud-Based ETL Tools?

As the name implies, ETL is a three-step process by which users turn disparate data streams into clean, organized data sets. Here's how it works: users extract data from source systems, enforce data quality and consistency standards, conform the data to use separate sources together, and deliver the data in a clean, consistent format for making decisions and improving strategies.

Here's what happens during each stage with cloud-based ETL tools:

  • Extract: Data gets extracted from a business's important data sources, including their CRM, social media, legacy systems, etc. At this stage, you not only determine your sources, but also things like the refresh rate (velocity) of each source, and priorities (extract order) between sources — all of which heavily impact time-to-insights.
  • Transform: The extracted data arrives in an interim staging area, where it converts into usable formats by cleansing, qualifying, and combining data. For example, dates consolidate into specified time buckets, transactions model into events, location data translates to coordinates, etc.
  • Load: The transformed data uploads to a new home, or destination, where your organization can mine it for BI and improve operations. Data is usually sent to one of the major cloud services, but it can also go somewhere on-premises. 

When you choose a platform like, you also unlock Reverse ETL capabilities. Reverse ETL taps into your data warehouse to produce insights in real-time. As a company, that means you can power business intelligence (BI) tools to guide internal workflows, processes, and decision-making across the organization. 

In the big picture, the ETL process saves significant time on data extraction and preparation — time better spent on conducting analytics and gaining actionable insight. This process with cloud-based ETL tools also performs a number of important functions that can help you better organize and understand your data, including:

  • Parsing/Cleansing — Data generated by applications appears in various formats like JSON, XML, or CSV. During the parsing stage, data maps into a table format with headers, columns, and rows, extracting specified fields. That way, you can merge it and understand it more comprehensively overall.
  • Data Enrichment — In order to prepare data for analytics, certain enrichment steps are usually required, including filling in missing data, fixing duplicate data, geo modifications, matching between sources, and more.
  • Setting Velocity — Velocity refers to the frequency of data loading, whether new data needs insertion or if existing data needs updating.
  • Data Validation — There are cases where data is empty, corrupted, missing crucial elements, too thin, or too bloated. ETL finds these occurrences and determines whether to stop the entire process, skip it, or set it aside for inspection while alerting the relevant administrators.

If you would benefit from these functions — or if your business is dealing with things like inconsistent data, hand-coding, compliance issues, or data-related SaaS problems — then an ETL tool like might be a good choice for your business. 

How To Choose the Right Cloud-Based ETL Tools

Choosing the right cloud-based ETL tool is a multifaceted decision that requires a deep dive into your organization's data strategy, technical requirements, and business goals. Here are considerations to keep in mind when selecting an ETL tool:

1) Consider Your Destination

ETL tools don't come with a destination or data warehouse solution (DWH) built-in. That means you're either going to have to use an existing database — if you have one available — or you're going to have to set up a new DWH to house your ETL data. There are lots of considerations to keep in mind here.

  • Data Compliance and Privacy: Ensure the destination complies with data governance and privacy standards relevant to your industry.

  • Performance Metrics: Understand the performance capabilities of the DWH, including query performance, load times, and concurrency.

  • Disaster Recovery: Check if the DWH offers robust backup and disaster recovery options to safeguard your data.

  • Integration with Data Lakes: If you're using a data lake, ensure the ETL tool can handle the integration between the data lake and the DWH.

  • Support for Data Types and Formats: Confirm that the DWH can support various data types and formats that you might work with.

Overall, make sure you have your destination set up and ready to go before you begin with ETL.

The biggest takeaway? You have to start with a comprehensive understanding of your business and your needs. Once you establish your requirements, you'll be able to focus on visualizing your data to drive key business decisions and unlock valuable insights. 

When it comes to a future-proof ETL solution that will scale with you, offers a large selection of pre-built connectors, which your team can use to create a single source of truth across all of your data sources. Plus, the robust API means is flexible enough to fit any use case, now or in the future.

2) Think About Internal Bandwidth

  • User-Friendly Interface: Opt for ETL tools with intuitive interfaces that reduce the learning curve for your team.

  • Customization and Flexibility: The tool should offer customization options to tailor the ETL processes to your specific needs.

  • Support and Training: Consider the level of support and training the ETL vendor provides to ensure smooth operation.

  • Monitoring and Logging: Look for tools with comprehensive monitoring and logging capabilities to troubleshoot and optimize ETL jobs.

  • Collaboration Features: Evaluate if the tool facilitates collaboration among team members, which is crucial for larger teams.

Using a tool that requires constant coding and engineering resources can be an expensive, long-term problem. That's why it's important to find an ETL platform that does not require heavy setup or extensive maintenance from your engineering team. is an ETL platform that checks these boxes.

Compared to other tools, greatly simplifies the ETL process for your development team by minimizing the amount of coding necessary to glue your cloud data warehouse together. allows your team to tap into automation workflows, which eliminate time-consuming processes by streamlining nearly every step of the process, from data integration and ingestion to designing advanced data processing workloads.

With a robust, secure, and cost-effective data transformation pipeline, your team will spend less time on data management and more time focusing on customer experience, sales, and growth.

3) Connect to Your Sources

Finally, it's important to find cloud-based ETL tools that can connect to all of the sources you use now and those that you might potentially need in the future. Preventing roadblocks in this area and maintaining a unified infrastructure can help prevent integration failures and improve your long-term success as you continue on your data journey. Consider the following:

  • Real-Time Processing: Assess whether the tool can handle real-time data processing if your business requires immediate data insights.

  • Data Quality Features: The tool should include features to clean, validate, and ensure the quality of the data being processed.

  • Connector Availability: Beyond pre-built connectors, check the ease of creating custom connectors for niche or proprietary data sources.

  • API Extensibility: Ensure the tool's API is robust enough to integrate with other systems and can be extended for custom requirements.

  • Change Data Capture (CDC): Determine if the tool supports CDC to efficiently process only the data that has changed, reducing load and processing time.

With pre-built connectors for the most popular storage platforms, Integrate ensures both accessibility and scalability, whether you use Azure, Amazon, Microsoft, or any number of third-party providers. Additionally, with the ability to utilize advanced features like unstructured data processing, machine learning, and Reverse ETL, empowers businesses to transform data into insights in real-time.

4) Additional Considerations

  • Scalability and Elasticity: The tool should be able to scale up or down based on data volume and processing needs without manual intervention.

  • Cost Management: Understand the cost implications of data processing volumes and choose a tool that offers cost predictability.

  • Vendor Stability and Roadmap: Research the vendor's stability in the market, their reputation, and their product development roadmap.

  • Community and Ecosystem: A strong community and ecosystem around the tool can be invaluable for getting help and sharing best practices.

  • Trial and Proof of Concept: Engage in a trial or proof of concept to test the ETL tool with your own data and use cases.

By considering these factors, you can make a more informed decision that not only addresses your current data integration needs but also positions your organization to handle future data challenges and opportunities.

How Can Help Your Organization with Cloud-Based ETL Tools

When it comes to cloud-based ETL tools, checks all the boxes: It simplifies integration for your developers, it unifies data sources for your teams, and it drives real-time intelligence to help your business grow.'s solution provides a simple, visualized data pipeline for automated data flows across a vast range of sources and destinations — allowing you to transform, normalize, and clean your data while keeping your organization in compliance.

When combined with's lightning-fast CDC platform, our ETL and Reverse ETL capabilities help e-commerce companies sell more, scale better, and delight customers along the way. Looking to see what can do for you? Click here to schedule an intro call to see how can help your business grow.