Top ETL Tools

Top 10 ETL Tools for Data Integration

Top 5 Qlik Competitors & Alternatives 2024

April 3, 2024

Data analytics and visualization have become an inseparable part of business functioning. From small companies to large-scale industries, everything from building strategies to better execution today depends on data analysis. However, to draw maximum actionable insights from data, you must employ a robust tool that fulfills your data-driven needs. Here’s how the popular platform Qlik comes into play. It is an all-in-one solution that not only allows you to load and manage data but also visualize and analyze it.

In this article, you will gain an overview of the Qlik platform and its key features. We will also review the top five Qlik competitors in 2024.

Qlik Overview

Introduced in 1993, Qlik is primarily a data analytics and visualization platform that provides solutions for drawing data-driven insights from a dataset. It has expanded to different products such as Qlik Sense, Qlik Replicate, Talend Data Fabric, Stitch Data, and many more. These products offer multiple features like data integration, replication, analytics, and visualization according to your business requirements. For instance, you can employ Qlik Sense to perform visualizations from different data sources and leverage in-depth analytics using its intuitive interface.

Qlik

Some of its key features are:

  • Qlik has an alerting feature that helps you monitor your datasets. You can immediately respond to any data modifications and keep track of them. 
  • With Qlik, you can also automate repetitive tasks and synchronize your dataset to perform seamless analytics.
  • It allows you and your team to develop ML models with an automated machine-learning tool called Qlik AutoML.
  • Qlik Sense offers an extensive feature set for augmented data analytics. This feature allows you to combine human and artificial intelligence to create basic, visual interactions with data.

Top 5 Qlik Competitors

Let's take a look at the top five Qlik alternatives that you can use to enhance your data analytics and visualization process:

Alteryx

Alteryx

Alteryx is a data analytics platform that provides numerous solutions to assist you in evaluating data. It eliminates the need for coding or prior programming expertise and simplifies cleaning, processing, and analyzing big datasets from diverse sources. Alteryx's intuitive interface makes it hassle-free to automate workflows while handling complex tasks like predictive modeling, geospatial analysis, and advanced reporting.

Some of the key features of Alteryx are:

  • Using Alteryx, you can effortlessly schedule and run your workflows automatically without any manual intervention. This feature not only saves your time but also eliminates the chances of any errors in the dataset.
  • Alteryx is also equipped with data integration solutions such as ETL and ELT. These allow you to seamlessly extract data from internal and external sources and load it into a centralized repository for data analytics under one roof.
  • Access to high-quality data is essential for accurate data analysis. Alteryx enables you to automate data collection from various sources. Once the data is collected, you can clean, organize, and analyze data to produce reliable results.

Sisense

Sisense

Sisense is a highly flexible and intuitive platform for data analytics and visualization. It provides data preparation tools that allow you to effortlessly collect, organize, transform, and load your data in a unified system to make it analytics-ready. You can perform complex queries and calculations on data, create customized charts, and build visually appealing dashboards. 

Some of the key features of Sisense are:

  • With Sisense, you can leverage AI and ML capabilities as they assist in data forecasting and predictive analysis. This, in turn, helps you predict future market trends, plan your strategies accordingly, and customize them to suit your business requirements.
  • Sisense provides many pre-built data connectors to bring your data stored in a database or flat files into one location for performing flexible data analytics.
  • To protect your data from external threats and access, Sisense is equipped with various data security measures. For instance, you can use its in-built encryption features, such as SHA-256 and TripleDES, to secure your account credentials and authorization.

Microsoft Power BI

Microsoft Power BI

Power BI is a software application designed by Microsoft that lets you combine data from multiple sources to create coherent, engaging insights. It offers options for visualization, from basic charts to intricate representations with data modeling capabilities. For example, you can analyze all the important KPIs and indicators to comprehend your business's performance, help you make improved decisions, and develop long-term strategies.

Some of the key features of Power BI are:

  • One effective tool for data processing in Power BI is Power Query. With Power Query Editor, you can import data from multiple sources and execute transformations, such as filtering rows and columns. You can also carry out advanced tasks, including grouping, pivoting, and data manipulation.
  • Power BI offers connectors for connecting to diverse data sources, such as on-premises, cloud-based databases, and Excel spreadsheets.
  • The scheduled refresh feature in Power BI can be used to eliminate tedious manual report updates. For instance, you can automate your semantic model or dataset by specifying the frequency and time intervals for data refreshes.

Looker (Google)

Looker

Looker is a self-service analytics tool offered by Google that enables you to streamline operations and create reports from several data sources. It is a multi-cloud unified platform that provides data analytics and visualization for cloud-based systems like Azure as well as on-premise databases like MariaDB and PostgreSQL. 

Some of the key features of Looker are:

  • Looker simplifies data exploration with its intuitive interface, enabling you to build custom data models and draft insightful reports quickly. The platform offers dashboards featuring customized charts, graphs, and reports that display data and insights.
  • You can leverage third-party network ecosystems on Looker to strengthen your data visualization process. It allows you to integrate data into applications like PowerPoint, Salesforce, and Confluence.
  • By utilizing DuetAI, Looker has improved its data visualization capabilities. With only a few instructions and an intelligent analyst, your organization can create intelligent data visuals.

Zoho Analytics

Zoho Analytics

Zoho Analytics is a data analytics and visualization tool that makes it seamless to create visually appealing dashboards. With this tool, you can monitor key performance metrics, identify trends, forecast future developments, and uncover previously undiscovered insights. In addition to these features, you can also create workspaces to collaborate with others and develop reports.

Some of the key features of Zoho Analytics are:

  • With Zoho, you can integrate data from diverse sources, such as sales, marketing, finance, and others, to unify your business data in one place. You can easily use its pre-built connectors or create custom ones to combine data, thus facilitating end-to-end insights.
  • It helps you to discover deep and meaningful insights from your data stored across on-premise or public cloud platforms like AWS, Azure, and Google.
  • Zoho provides the best security measures to ensure robust protection and governance. These include continuous support for data backup, design privacy, user log-in, control access, and data confidentiality.

Elevate Data Analytics Journey With Airbyte 

Airbyte

Using the above-mentioned platforms, you can effortlessly analyze or visualize your dataset and formulate better strategies accordingly. However, the presence of data at multiple locations can cause unnecessary hassle in the process. Therefore, it is crucial to integrate all the data into a central system for streamlined analysis. This is where a flexible and reliable platform such as Airbyte comes into the picture. 

Employed by 40,000+ engineers, Airbyte is a popular data integration platform that streamlines the process of connecting data sources to target systems without writing a single line of code. It facilitates the modern ELT approach to combine data from databases, SaaS applications, and APIs into data lakes, warehouses, and other repositories. 

Beyond integration capabilities, Airbyte also provides data replication features. It comes with Change Data Capture functionality to identify changes in the source file and replicate them into the destination system, thus ensuring data integrity.

Some of the unique features of Airbyte:

  • With Airbyte, you can leverage 350+ pre-built connectors to automate data pipelines. If a connector of your choice is unavailable, it provides you the option to build a custom connector using CDK.
  • Airbyte has recently introduced its developer-friendly UI, known as PyAirbyte. It is an open-source Python library suitable for programmers to manage and design data pipelines within minutes.
  • Being an open-source platform, Airbyte has built a vibrant community of data practitioners and developers. You can collaborate with others to discuss best integration practices, resolve issues in data ingestion, or share ideas for improvements.
  • You can rapidly integrate with tools like Kubernetes, Dagster, Airflow, and dbt to facilitate quick data processing and management.

Final Word

As the importance of data increases, the need for efficient data analytics and visualization tools also grows. These tools offer extensive features and functionalities to draw productive insights and make appropriate decisions. In this article, we have comprehensively covered the Qlik platform and its key features, as well as discussed its various alternatives. You can employ the above-mentioned tools based on your enterprise requirements.

We suggest using Airbyte to optimize your business operations and streamline your data analytics journey. Sign up on the Airbyte platform today to leverage its data integration capabilities for reliable data analytics.

What should you do next?

Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

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TL;DR

The most prominent ETL and ELT tools to transfer data from include:

  • Airbyte
  • Fivetran
  • Stitch
  • Matillion
  • These ETL and ELT tools help in extracting data from and other sources (APIs, databases, and more), transforming it efficiently, and loading it into a database, data warehouse or data lake, enhancing data management capabilities. Airbyte distinguishes itself by offering both a self-hosted open-source platform and a Cloud one..

    What is ETL?

    ETL (Extract, Transform, Load) is a process used to extract data from one or more data sources, transform the data to fit a desired format or structure, and then load the transformed data into a target database or data warehouse. ETL is typically used for batch processing and is most commonly associated with traditional data warehouses.

    What is ELT?

    More recently, ETL has been replaced by ELT (Extract, Load, Transform). ELT Tool is a variation of ETL one that automatically pulls data from even more heterogeneous data sources, loads that data into the target data repository - databases, data warehouses or data lakes - and then performs data transformations at the destination level. ELT provides significant benefits over ETL, such as:

    • Faster processing times and loading speed
    • Better scalability at a lower cost
    • Support of more data sources (including Cloud apps), and of unstructured data
    • Ability to have no-code data pipelines
    • More flexibility and autonomy for data analysts with lower maintenance
    • Better data integrity and reliability, easier identification of data inconsistencies
    • Support of many more automations, including automatic schema change migration

    For simplicity, we will only use ETL as a reference to all data integration tools, ETL and ELT included, to integrate data from .

    How data integration from to a data warehouse can help

    Companies might do ETL for several reasons:

    1. Business intelligence: data may need to be loaded into a data warehouse for analysis, reporting, and business intelligence purposes.
    2. Data Consolidation: Companies may need to consolidate data with other systems or applications to gain a more comprehensive view of their business operations
    3. Compliance: Certain industries may have specific data retention or compliance requirements, which may necessitate extracting data for archiving purposes.

    Overall, ETL from allows companies to leverage the data for a wide range of business purposes, from integration and analytics to compliance and performance optimization.

    Criterias to select the right ETL solution for you

    As a company, you don't want to use one separate data integration tool for every data source you want to pull data from. So you need to have a clear integration strategy and some well-defined evaluation criteria to choose your ETL solution.

    Here is our recommendation for the criteria to consider:

    • Connector need coverage: does the ETL tool extract data from all the multiple systems you need, should it be any cloud app or Rest API, relational databases or noSQL databases, csv files, etc.? Does it support the destinations you need to export data to - data warehouses, databases, or data lakes?
    • Connector extensibility: for all those connectors, are you able to edit them easily in order to add a potentially missing endpoint, or to fix an issue on it if needed?
    • Ability to build new connectors: all data integration solutions support a limited number of data sources.
    • Support of change data capture: this is especially important for your databases.
    • Data integration features and automations: including schema change migration, re-syncing of historical data when needed, scheduling feature
    • Efficiency: how easy is the user interface (including graphical interface, API, and CLI if you need them)?
    • Integration with the stack: do they integrate well with the other tools you might need - dbt, Airflow, Dagster, Prefect, etc. - ? 
    • Data transformation: Do they enable to easily transform data, and even support complex data transformations? Possibly through an integration with dbt
    • Level of support and high availability: how responsive and helpful the support is, what are the average % successful syncs for the connectors you need. The whole point of using ETL solutions is to give back time to your data team.
    • Data reliability and scalability: do they have recognizable brands using them? It also shows how scalable and reliable they might be for high-volume data replication.
    • Security and trust: there is nothing worse than a data leak for your company, the fine can be astronomical, but the trust broken with your customers can even have more impact. So checking the level of certification (SOC2, ISO) of the tools is paramount. You might want to expand to Europe, so you would need them to be GDPR-compliant too.

    Top ETL tools

    Here are the top ETL tools based on their popularity and the criteria listed above:

    1. Airbyte

    Airbyte is the leading open-source ELT platform, created in July 2020. Airbyte offers the largest catalog of data connectors—350 and growing—and has 40,000 data engineers using it to transfer data, syncing several PBs per month, as of June 2023. Major users include brands such as Siemens, Calendly, Angellist, and more. Airbyte integrates with dbt for its data transformation, and Airflow/Prefect/Dagster for orchestration. It is also known for its easy-to-use user interface, and has an API and Terraform Provider available.

    What's unique about Airbyte?

    Their ambition is to commoditize data integration by addressing the long tail of connectors through their growing contributor community. All Airbyte connectors are open-source which makes them very easy to edit. Airbyte also provides a Connector Development Kit to build new connectors from scratch in less than 30 minutes, and a no-code connector builder UI that lets you build one in less than 10 minutes without help from any technical person or any local development environment required.. 

    Airbyte also provides stream-level control and visibility. If a sync fails because of a stream, you can relaunch that stream only. This gives you great visibility and control over your data. 

    Data professionals can either deploy and self-host Airbyte Open Source, or leverage the cloud-hosted solution Airbyte Cloud where the new pricing model distinguishes databases from APIs and files. Airbyte offers a 99% SLA on Generally Available data pipelines tools, and a 99.9% SLA on the platform.

    2. Fivetran

    Fivetran is a closed-source, managed ELT service that was created in 2012. Fivetran has about 300 data connectors and over 5,000 customers.

    Fivetran offers some ability to edit current connectors and create new ones with Fivetran Functions, but doesn't offer as much flexibility as an open-source tool would.

    What's unique about Fivetran? 

    Being the first ELT solution in the market, they are considered a proven and reliable choice. However, Fivetran charges on monthly active rows (in other words, the number of rows that have been edited or added in a given month), and are often considered very expensive.

    Here are more critical insights on the key differentiations between Airbyte and Fivetran

    3. Stitch Data

    Stitch is a cloud-based platform for ETL that was initially built on top of the open-source ETL tool Singer.io. More than 3,000 companies use it.

    Stitch was acquired by Talend, which was acquired by the private equity firm Thoma Bravo, and then by Qlik. These successive acquisitions decreased market interest in the Singer.io open-source community, making most of their open-source data connectors obsolete. Only their top 30 connectors continue to be  maintained by the open-source community.

    What's unique about Stitch? 

    Given the lack of quality and reliability in their connectors, and poor support, Stitch has adopted a low-cost approach.

    Here are more insights on the differentiations between Airbyte and Stitch, and between Fivetran and Stitch.

    Other potential services

    Matillion

    Matillion is a self-hosted ELT solution, created in 2011. It supports about 100 connectors and provides all extract, load and transform features. Matillion is used by 500+ companies across 40 countries.

    What's unique about Matillion? 

    Being self-hosted means that Matillion ensures your data doesn’t leave your infrastructure and stays on premise. However, you might have to pay for several Matillion instances if you’re multi-cloud. Also, Matillion has verticalized its offer from offering all ELT and more. So Matillion doesn't integrate with other tools such as dbt, Airflow, and more.

    Here are more insights on the differentiations between Airbyte and Matillion.

    Airflow

    Apache Airflow is an open-source workflow management tool. Airflow is not an ETL solution but you can use Airflow operators for data integration jobs. Airflow started in 2014 at Airbnb as a solution to manage the company's workflows. Airflow allows you to author, schedule and monitor workflows as DAG (directed acyclic graphs) written in Python.

    What's unique about Airflow? 

    Airflow requires you to build data pipelines on top of its orchestration tool. You can leverage Airbyte for the data pipelines and orchestrate them with Airflow, significantly lowering the burden on your data engineering team.

    Here are more insights on the differentiations between Airbyte and Airflow.

    Talend

    Talend is a data integration platform that offers a comprehensive solution for data integration, data management, data quality, and data governance.

    What’s unique with Talend?

    What sets Talend apart is its open-source architecture with Talend Open Studio, which allows for easy customization and integration with other systems and platforms. However, Talend is not an easy solution to implement and requires a lot of hand-holding, as it is an Enterprise product. Talend doesn't offer any self-serve option.

    Pentaho

    Pentaho is an ETL and business analytics software that offers a comprehensive platform for data integration, data mining, and business intelligence. It offers ETL, and not ELT and its benefits.

    What is unique about Pentaho? 

    What sets Pentaho data integration apart is its original open-source architecture, which allows for easy customization and integration with other systems and platforms. Additionally, Pentaho provides advanced data analytics and reporting tools, including machine learning and predictive analytics capabilities, to help businesses gain insights and make data-driven decisions. 

    However, Pentaho is also an Enterprise product, so hard to implement without any self-serve option.

    Informatica PowerCenter

    Informatica PowerCenter is an ETL tool that supported data profiling, in addition to data cleansing and data transformation processes. It was also implemented in their customers' infrastructure, and is also an Enterprise product, so hard to implement without any self-serve option.

    Microsoft SQL Server Integration Services (SSIS)

    MS SQL Server Integration Services is the Microsoft alternative from within their Microsoft infrastructure. It offers ETL, and not ELT and its benefits.

    Singer

    Singer is also worth mentioning as the first open-source JSON-based ETL framework.  It was introduced in 2017 by Stitch (which was acquired by Talend in 2018) as a way to offer extendibility to the connectors they had pre-built. Talend has unfortunately stopped investing in Singer’s community and providing maintenance for the Singer’s taps and targets, which are increasingly outdated, as mentioned above.

    Rivery

    Rivery is another cloud-based ELT solution. Founded in 2018, it presents a verticalized solution by providing built-in data transformation, orchestration and activation capabilities. Rivery offers 150+ connectors, so a lot less than Airbyte. Its pricing approach is usage-based with Rivery pricing unit that are a proxy for platform usage. The pricing unit depends on the connectors you sync from, which makes it hard to estimate. 

    HevoData

    HevoData is another cloud-based ELT solution. Even if it was founded in 2017, it only supports 150 integrations, so a lot less than Airbyte. HevoData provides built-in data transformation capabilities, allowing users to apply transformations, mappings, and enrichments to the data before it reaches the destination. Hevo also provides data activation capabilities by syncing data back to the APIs. 

    Meltano

    Meltano is an open-source orchestrator dedicated to data integration, spined off from Gitlab on top of Singer’s taps and targets. Since 2019, they have been iterating on several approaches. Meltano distinguishes itself with its focus on DataOps and the CLI interface. They offer a SDK to build connectors, but it requires engineering skills and more time to build than Airbyte’s CDK. Meltano doesn’t invest in maintaining the connectors and leave it to the Singer community, and thus doesn’t provide support package with any SLA. 

    All those ETL tools are not specific to , you might also find some other specific data loader for data. But you will most likely not want to be loading data from only in your data stores.

    Which data can you extract from ?

    How to start pulling data in minutes from

    If you decide to test Airbyte, you can start analyzing your data within minutes in three easy steps:

    Step 1: Set up as a source connector

    Step 2: Set up a destination for your extracted data

    Choose from one of 50+ destinations where you want to import data from your source. This can be a cloud data warehouse, data lake, database, cloud storage, or any other supported Airbyte destination.

    Step 3: Configure the data pipeline in Airbyte

    Once you've set up both the source and destination, you need to configure the connection. This includes selecting the data you want to extract - streams and columns, all are selected by default -, the sync frequency, where in the destination you want that data to be loaded, among other options.

    And that's it! It is the same process between Airbyte Open Source that you can deploy within 5 minutes, or Airbyte Cloud which you can try here, free for 14-days.

    Conclusion

    This article outlined the criteria that you should consider when choosing a data integration solution for ETL/ELT. Based on your requirements, you can select from any of the top 10 ETL/ELT tools listed above. We hope this article helped you understand why you should consider doing ETL and how to best do it.

    What should you do next?

    Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

    flag icon
    Easily address your data movement needs with Airbyte Cloud
    Take the first step towards extensible data movement infrastructure that will give a ton of time back to your data team. 
    Get started with Airbyte for free
    high five icon
    Talk to a data infrastructure expert
    Get a free consultation with an Airbyte expert to significantly improve your data movement infrastructure. 
    Talk to sales
    stars sparkling
    Improve your data infrastructure knowledge
    Subscribe to our monthly newsletter and get the community’s new enlightening content along with Airbyte’s progress in their mission to solve data integration once and for all.
    Subscribe to newsletter

    Build powerful data pipelines seamlessly with Airbyte

    Get to know why Airbyte is the best Qlik Competitors

    Sync data from Qlik Competitors to 300+ other data platforms using Airbyte

    Try a 14-day free trial
    No card required.

    Frequently Asked Questions

    What is ETL?

    ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.

    What is ?

    What data can you extract from ?

    How do I transfer data from ?

    This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps: set it up as a source, choose a destination among 50 available off the shelf, and define which data you want to transfer and how frequently.

    What are top ETL tools to extract data from ?

    The most prominent ETL tools to extract data include: Airbyte, Fivetran, StitchData, Matillion, and Talend Data Integration. These ETL and ELT tools help in extracting data from various sources (APIs, databases, and more), transforming it efficiently, and loading it into a database, data warehouse or data lake, enhancing data management capabilities.

    What is ELT?

    ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.

    Difference between ETL and ELT?

    ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.