Top ETL Tools

Top 10 ETL Tools for Data Integration

Top Data Lake Tools in 2024: Empowering Your Data Management

March 29, 2024

Your businesses acquire and generate vast amounts of data from various sources on a large scale. To effectively harness the value of this data, you need a robust and scalable data management solution. This is where a data lake becomes crucial. It is a centralized repository that enables you to store massive amounts of data in its raw form. Data lakes offer flexibility, scalability, and cost-effectiveness, as they can accommodate diverse data types and handle massive data volumes without requiring any transformations.

In this article, you will explore the top data lake tools that can empower your business to manage your data efficiently. Let’s explore each of them in detail, along with their key features.

Top 5 Data Lake Tools

Let’s explore the best data lake tools to consider in 2024:

AWS S3

AWS S3

Amazon Simple Storage Service (S3) is AWS’s most popular object storage solution for storing structured and unstructured data. It allows you to collect data from various sources in real-time or in batches and store it in its original format. Furthermore, it enables you to seamlessly integrate with powerful AWS services like Athena, Redshift Spectrum, AWS Glue, and Lambda, enabling you to query, process, and analyze your data efficiently.

Here are some important features of Amazon S3:

  • AWS S3 makes it simple to create a multi-tenant environment that allows multiple users to run various analytical tools on the same data copy. This reduces costs and enhances data consistency compared to traditional solutions, which require distributing multiple data copies across several processing platforms. 
  • It offers multiple storage classes, each optimized for specific use cases. This allows you to optimize costs by storing data based on its access patterns.
  • Amazon S3 prioritizes security by default and offers robust user authentication features. It provides access control mechanisms like bucket policies and access-control lists to allow fine-grained access to data stored in S3 buckets.
  • S3 Cross-Region Replication enables you to copy your objects across S3 buckets, even across different accounts. This minimizes latency by storing the objects closer to the user's location.

Cloudera

Cloudera

Cloudera provides a comprehensive Data Lake Service built on open-source technologies like Hadoop, Hive, and Spark. It differentiates itself by prioritizing enterprise-grade security, governance, and compliance features. Cloudera empowers you to set up and manage data lakes, ensuring the safety of your data wherever it’s stored, from object stores to Hadoop Distributed File System (HDFS).

Here is an overview of Data Lake Service key features:

  • Data Lake storage resides in external locations independent of the hosts running the Data Lake Services. This ensures that workloads are protected from data loss in the event of a failure of the Data Lake nodes.
  • It automatically captures and stores metadata definitions as they're discovered and created during platform workloads. This transforms metadata into valuable information assets, enhancing their usability and overall value.
  • A Data Lake cluster utilizes Apache Knox to offer a secure gateway to access Data Lake component UIs.
  • Data Lake Service enforces granular, role, and attribute-based security policies. It encrypts data at rest and in motion and efficiently manages encryption keys.

Apache Hudi

Apache Hudi

Apache Hudi is an efficient open-source data lake platform that offers data ingestion, storage, and querying capabilities. It includes DeltaStreamer, a dedicated tool designed for ingesting real-time data. This allows you to capture and process data continuously as it arrives from streaming sources like Apache Kafka, Apache Pulsar, or other messaging systems.

Here are the key features of Apache Hudi :

  • Apache Hudi ensures the ACID (Atomicity, Consistency, Isolation, and Durability) properties for data operations within the data lake. This makes it well-suited for use cases where maintaining data integrity and consistency is crucial.
  • It supports various cloud storage systems, including Amazon S3, Microsoft Azure, and Google Cloud Storage (GCS), allowing for deployment in cloud-based data lake environments.
  • Hudi maintains a timeline of all activities performed on the table at different instants of time. This facilitates quick access to historical data and enables efficient querying.
  • It ensures data integrity and consistency through atomic file commits and write-ahead logs. This guarantees that data changes are not lost in case of failures.
  • Hudi's data compaction feature consolidates small data files into larger ones, reducing storage overhead and improving query performance.

Snowflake

Snowflake

Snowflake’s cloud-built architecture provides a flexible solution to support your Data Lake needs. It allows you to store all your data, regardless of the format (unstructured, semi-structured, and structured), within Snowflake’s optimized, managed storage. Furthermore, it secures your data lake with detailed, granular, and consistent access controls, ensuring data remains protected.

Here are some of the key features of Snowflake :

  • Snowflake's cloud architecture allows for independent scaling of storage and compute. This separation enables you to optimize costs by scaling resources based on your needs.
  • It also supports a schema-on-read approach for data storage. You can store data in its original format and define the schema only when querying the data.
  • Data Lake distinguishes itself by being open to all data types and storing data in its original raw state. It transforms data only when required for analysis based on query criteria.
  • Snowflake allows you to use pre-built views that are readily available for querying to comply with regulatory auditing requirements. These views provide insights into data lineage, usage patterns, and relationships.
  • It enforces column-level security through dynamic data masking. This allows you to protect sensitive data by dynamically masking specific columns based on the privileges and access rights.

Infor Data Lake 

Infor Data Lake

Infor Data Lake is a scalable and flexible platform that offers a unified repository for storing your enterprise data. It supports data ingestion from multiple sources through connectors and functions like ION Messaging Service (IMS), AnySQL, and File Connector. This facilitates the loading of data from various systems and databases into Data Lake, ensuring a seamless flow of information.

Here are some of the known features of Infor Data Lake:

  • The Infor Data Catalog offers various services to help you analyze and track changes in your captured data. This helps you understand your data by providing information about its origin, format, and usage patterns.
  • Infor Data Lake prioritizes data security and governance. Data objects stored in Data Lake are encrypted with AES-256 bit encryption to ensure data security.
  • It supports a schema-on-read approach and a fast, flexible data consumption framework for making informed decisions based on captured data.
  • Infor Data Lake provides indexing capabilities to make data easily accessible. Using the indexing functionality, you can efficiently search and retrieve specific data objects or information.
  • It seamlessly integrates with tools like Birst for advanced data analytics and visualization.

Seamlessly Move Your Data into Data Lake Using Airbyte

Airbyte

Data lakes have become essential to store vast amounts of raw data from various sources for analytics and insights. This data may reside in diverse sources such as APIs, databases, files, and data warehouses, requiring a streamlined approach to move data into the data lake. While this data holds immense value, managing and gathering it all instantly can be a challenge. That's where platforms like Airbyte can help! 

Airbyte is a cloud-based data integration and replication platform that can expedite the process of extracting data from multiple data sources and loading it to your target system. It offers a vast catalog of over 350 connectors, including AWS S3 and Azure Blob Storage.

Here are the key features of Airbyte:

Ease of Use: Airbyte prioritizes ease of use, offering a user-friendly interface for configuration, monitoring, and management. You can conveniently utilize multiple options, including UI, API, Terraform Provider, and PyAirbyte, to design and manage data pipelines. 

Customization: If the required connector is not available in the pre-existing list, Airbyte lets you build custom connectors using the Connector Development Kit (CDK). This empowers you with the flexibility to create tailored connectors that align with the specific requirements, ensuring seamless integration with the desired data sources.

Data Security: Airbyte incorporates various robust security measures, such as access control, audit logging, encryption, and authentication mechanisms. These ensure data integrity, confidentiality, and safety throughout the migration process.

Transformations: Airbyte adopts the ELT (Extract, Load, Transform) approach, where data is loaded into the target system before transforming it. However, Airbyte allows you to integrate with dbt (data build tool) to facilitate customized transformations. By leveraging dbt's robust capabilities, you can perform advanced data transformations.

Flexible Pricing: It provides flexible pricing options to accommodate diverse business needs. It offers three distinct plans—Airbyte Cloud, Airbyte Self-Managed, and Powered by Airbyte. The Self-Managed version is open-source and free to use, while the Airbyte Cloud plan operates on a pay-as-you-go model. The Powered by Airbyte version offers pricing based on specific syncing frequency requirements.

Wrapping Up

The data lake is becoming increasingly important in managing large volumes of data, and your business needs to leverage the right data lake tools to ensure effective data management. Whether you're dealing with structured or unstructured data, these tools offer the scalability, flexibility, and security required to manage your data lake environment effectively. Investing in the right data lake tool can transform the way you handle data, leading to enhanced productivity.

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 Top Data Lake Tools in 2024: Empowering Your Data Management

Sync data from Top Data Lake Tools in 2024: Empowering Your Data Management to 300+ other data platforms using Airbyte

Try a 14-day free trial
No card required.

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 Top Data Lake Tools in 2024: Empowering Your Data Management

    Sync data from Top Data Lake Tools in 2024: Empowering Your Data Management 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.