Top ETL Tools

Top 10 ETL Tools for Data Integration

Top 5 Data Observability Tools for Efficient Monitoring 

April 2, 2024

Keeping a close eye on your data is crucial in ensuring its reliability and accuracy. Data observability tools help you monitor your data systems to catch problems early and maintain data integrity.

This article is about the top observability tools for keeping tabs on your data. These tools help you monitor your data, spot issues, and make sure everything's working as it should. Whether finding mistakes or ensuring thing's run smoothly, these tools have you covered.

What are Data Observability Tools?

Data observability tools are designed to provide insights into the health, performance, and reliability of your data systems. They enable you to monitor various aspects of your data infrastructure in real time, allowing for proactive detection and resolution of issues. These tools let you collect, analyze, and visualize data metrics, offering a comprehensive view of your data pipelines, processes, and workflows.

Here are some criteria to help you with the right observability tool selection:

  • Use Case: Consider your specific data monitoring needs and requirements. Determine the key functionalities and features necessary to address your unique use cases, ensuring that the selected tool aligns with your goals and objectives.
  • Budget: Evaluate the cost implications associated with deploying and maintaining the data observability tool. Consider both initial investment costs and ongoing expenses, such as licensing fees, support, and maintenance, to ensure that the selected tool fits your budget constraints.
  • Ease of Use: Assess the user interface and usability of the data observability tool to ensure widespread adoption across your operations. Pick a tool that is intuitive and user-friendly, minimizing the need for extensive training and facilitating seamless integration into existing workflows.
  • Support and Documentation: Look for a data observability tool that offers comprehensive support and documentation. Consider factors such as vendor authenticity, availability of customer support, and the quality of documentation and resources provided. A reliable support system is essential for resolving issues promptly and maximizing the value of the tool for your operations.

Top Data Observability Tools

By diving into these data observability tools, you'll learn what they do, how they work, and when to use them.

Datadog

Datadog

The Datadog observability platform offers full visibility into each layer of a distributed environment. There is built-in support for more than 650 third-party integrations. It provides a single pane of glass for troubleshooting distributed systems, optimizing application performance, and supporting cross-team collaboration. Datadog pairs automatic scaling and deployment with intuitive tools that incorporate machine learning for more reliable insights into your applications and infrastructure.

Some key features include:

  • Datadog is accessible as a Software-as-a-Service (SaaS) platform, ensuring ease of access and minimal setup requirements.
  • The platform empowers you to monitor a wide range of components, including infrastructure, applications, databases, network performance, and the entire DevOps stack. You can utilize its support for user and network monitoring, synthetic monitoring, as well as log and incident management functionalities.
  • Open-source agents deployed on your monitored systems collect metrics and events and report them back to the Datadog platform. These agents are versatile and capable of running on both bare metal servers and containerized environments. Datadog also offers its own monitoring agents for broader compatibility.
  • Datadog offers tiered subscription plans with features like Infrastructure Monitoring, Log Management, and Application Performance Monitoring (APM). Many plans have sub-tiers to match specific needs.

Grafana

Grafana

Grafana provides a centralized platform for exploring and visualizing metrics, logs, and traces. The platform includes alerting capabilities while also providing tools for turning time series database data into insightful graphs and visualizations. From a central interface, you can create a rich set of dashboards. These dashboards show telemetric data from a variety of sources, including Kubernetes clusters, cloud services, Raspberry Pi devices, and Google Sheets.

Here are some key features of Grafana:

  • Grafana allows you to monitor a variety of aspects, such as infrastructure, applications, data sources, microservices, and third-party platforms, ensuring complete coverage of your environment.
  • It utilizes an open-source agent deployed on your monitored devices to collect metrics, logs, and traces. This agent efficiently gathers telemetry data and forwards it to the Grafana platform, regardless of whether it's hosted in the cloud or on-premises.
  • Grafana offers deployment flexibility and scalable pricing. Choose between the fully managed Grafana Cloud service for convenient monitoring with minimal setup or the Self-managed Grafana Enterprise Stack for on-premises or cloud deployments. Whichever option you select, there are tiered subscription plans, with the Enterprise edition offering a streamlined version of the Enterprise Stack for on-premises users.

Monte Carlo Data

Monte Carlo Data

Monte Carlo offers a cutting-edge observability platform designed to effectively meet your specific monitoring requirements. Its advanced anomaly detection algorithms and machine learning capabilities enable you to proactively identify and resolve data issues, ensuring consistent and accurate data. Integrated with various data sources and platforms, Monte Carlo centralizes your monitoring efforts, empowering you to optimize data processes and make informed decisions based on trusted insights.

Some key features of Monte Carlo include:

  • With the real-time monitoring feature, you can track your data as it is generated, allowing you to identify and address potential issues as they occur swiftly.
  • Monte Carlo empowers you with a no-code interface, eliminating the need for complex coding to set up and manage data monitoring pipelines.
  • It prioritizes data security by adhering to SOC 2 compliance standards. This rigorous certification assures that your data is protected with robust security measures.

New Relic

New Relic

The New Relic observability platform offers you a suite of tools for full-stack monitoring across applications and infrastructure. It covers Kubernetes, browser, mobile, network, and synthetic monitoring. Additionally, the platform includes log management and error-tracking functionalities. It allows you to integrate with more than 500 third-party technologies and utilizes applied intelligence to provide insights into incident root causes automatically. Moreover, it features CodeStream integration, providing a developer collaboration platform.

Here are some key features of New Relic:

  • New Relic is a Software-as-a-Service (SaaS) solution, ensuring easy access and minimal setup.
  • You can effortlessly install agents on hosts or within applications to collect performance data and send these metrics to the New Relic platform. Further, it also supports OpenTelemetry, an open-source framework for improving data collection capabilities and compatibility.
  • New Relic allows you to scale to meet your needs. Choose from the Free, Standard, Pro, and Enterprise tiers, each offering increasing features and support levels to match your monitoring requirements.

Dynatrace

Dynatrace

Dynatrace is an integrated platform for monitoring infrastructure and applications, covering networks, mobile apps, and server-side services. With Dynatrace, you can analyze the performance of user interactions across various applications. It features an AI-driven causation engine named Davis to support root cause analysis.

Dynatrace offers comprehensive monitoring by supporting over 600 third-party technologies. You can utilize the Dynatrace API, SDK, or plugins to integrate with custom tools and extend the platform's functionality to fit your needs.

Some key features of Dynatrace include:

  • Dynatrace presents comprehensive monitoring capabilities across various domains, including infrastructure, applications, microservices, application security, digital experience monitoring, and business analytics support.
  • Each monitored host runs an agent responsible for collecting system, application, network, and log data. This data is then transmitted to the Dynatrace platform for analysis and insights.
  • Dynatrace offers flexible pricing to suit your needs. You can choose from a consumption-based model with hourly billing, ideal for cloud-native environments, or opt for a traditional annual commitment with volume discounts. This variety ensures you only pay for the monitoring resources you utilize.

Strengthening Data Monitoring With Airbyte

Using the above-mentioned tools, you can ensure the security and reliability of your datasets. However, before performing efficient data monitoring and tracking, it is crucial to integrate data from diverse platforms for a unified view of datasets. Airbyte is a popular data integration platform that you can leverage to move and consolidate your data.

Airbyte

Launched in 2020, Airbyte is a cloud-based platform that employs a modern ELT approach that allows you to gather data from multiple sources and load them into a destination. With a rich library of 350+ pre-built connectors, you can create automated data pipelines. If you can’t find a suitable connector, then Airbyte provides the flexibility to design custom connectors using CDK or request a new one by contacting its team.

In addition to the above capabilities, Airbyte also supports data sources that have unstructured, semi-structured, and structured data, thus making it a flexible platform for modern integration practices. 

Some of the unique features of Airbyte include:

  • Multiple User Interfaces: It offers a user-friendly UI for those without programming experience alongside three developer-friendly options—API access, Terraform Provider, and the new open-source Python library, PyAirbyte. This suite of tools empowers you to automate, customize, and manage data integrations effectively.
  • Data Replication Capabilities: Airbyte has Change Data Capture functionality, enabling you to identify source data changes and quickly replicate them into the target system. This empowers you to keep track of the data modifications, thus ensuring the consistency of the dataset.
  • Data Security: Airbyte provides a range of security measures, including encryption, access controls, audit logging, and authentication mechanisms, to safeguard your data from external threats. It also conforms to security standards such as ISO 27001 and SOC 2 Type 2 to maintain data integrity.

Conclusion

The data observability tools we've covered offer valuable options for improving your data monitoring. With features like real-time monitoring and easy-to-understand data visuals, these tools help you catch problems early and ensure data reliability. You can integrate seamlessly, and they provide vital support, making it easier to keep your data in check, boost efficiency, and make smart decisions. Additionally, we recommend considering Airbyte as a complete solution for data integration to further enhance your data monitoring capabilities.

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 Data Observability Tools

    Sync data from Data Observability Tools 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.