Managing data can be a challenging task, especially when you're dealing with large amounts of information. That's where data management tools come in. They can help you organize your data, streamline your operations, and make informed decisions based on accurate and up-to-date information.
This article provides an overview of the essential features to consider when selecting a data management tool. It also explores the different options available in the market, along with their unique features.
What are Data Management Tools?
Data management tools are software applications that help you store, organize, and manage data efficiently. With these tools, you can better understand your data, identify patterns and trends, and make adjustments to optimize your business processes.
The fundamental purpose of data management tools is to streamline the data integration process to optimize efficiency. Furthermore, they incorporate privacy and security features and mechanisms to eliminate data duplication.
Primary Features of Data Management Tools
When evaluating a data management tool, it's essential to consider certain key features that can contribute to its effectiveness. Let’s explore some of them:
Data Integration and Cleansing: A robust data management tool should seamlessly integrate and manage data from various sources such as servers, databases, and legacy systems. In addition, it should have advanced data cleansing features that can accurately identify and resolve errors and duplicates from the data, resulting in improved quality and reliability.
Scalability: By leveraging a flexible and scalable tool, you can effortlessly handle growing data volumes, ensuring your data management solution remains effective for the long term. This helps you avoid costly migrations and achieve goals more efficiently.
Ease of Use: When choosing a data management tool, it's important to consider its ease of use for both technical and non-technical users. It includes assessing the functionality of the user interfaces, the quality of support provided, and the availability of documentation and resources.
5 Top Data Management Tools in 2024
Data management can be overwhelming, but the right tools can make all the difference. Let’s explore some of the top data management tools that will help streamline your data processes.
Airbyte
Airbyte is a highly efficient and reliable data integration tool that simplifies the management of all your data pipelines. It enables swift data consolidation from various sources and unifies it in one place. This means that if you have a lot of data stored in different locations, you can use Airbyte to bring it together in a single platform. This makes it super easy to manage and analyze your data.
Key Features Include:
Extensive Connector Support: Airbyte provides more than 350 pre-built connectors that enable you to transfer data from the source to the destination seamlessly. You can easily select the required connectors and build the data pipelines without complexity. This simplified process does not require you to write any code, making it accessible to anyone.
Customization: Airbyte provides a Connector Development Kit (CDK) that allows you to customize existing connectors or build new ones from scratch within two hours. With this feature, you can tailor your connectors to meet unique requirements without any coding skills.
Ease of Use: It features a user-friendly configuration, monitoring, and management interface. The intuitive design enables easy setup and management of data pipelines, even for non-technical users.
Transformation: Airbyte uses an ELT (Extract, Load, Transform) approach, which means it loads data from sources before transforming the data. However, it also offers integration with dbt (data build tool) for complex transformation.
Hevo Data
Hevo Data offers a low-code data integration platform to create end-to-end pipelines. It simplifies extracting data from multiple sources, transforming it into analysis-ready, and delivering it to target warehouses. With Hevo's platform, you can transform data before or after data loading, making it a suitable tool for both ETL and ELT.
Key Features Include:
Connectors: Hevo offers over 150 connectors, covering a wide range of SaaS applications and databases. However, it supports only 15 destinations, including data warehouses and databases. If you require a connector that is not already built-in, you can request Hevo's team to create a custom one for you.
Transformation: Hevo offers different data transformation approaches. In-flight transformations automatically modify source data while loading, while user-drive enables data cleaning, filtering, and deduplication before loading. Finally, performing post-data transformations is also possible by using SQL queries on the data already loaded into the destination.
Automated Schema Management: Hevo's Automated Schema Mapper automates the complete process of schema management. Any changes in the source schema are automatically reflected in the destination, eliminating the need for manual intervention.
Security: Hevo Data adheres to strict guidelines to ensure the security of your data. It guarantees the privacy and confidentiality of your data by complying with GDPR, SOC2, HIPAA, and CCPA. It establishes connections to sources and destinations through SSH tunnel and encrypts SaaS sources through HTTPS.
Dell Boomi
Dell Boomi is one of the best data management tools. It is a cloud-based integration platform-as-a-service (iPaaS). It provides a unified solution that enables easy access to devices and applications, whether on-premise or on the cloud. Boomi offers a comprehensive platform to move, manage, and efficiently govern data across your business.
Key Features Include:
Ease of Integration: Dell Boomi provides a simple browser user interface. It enables you to create integrations quickly and easily through simple point-and-click and drag-and-drop actions, often eliminating the need for coding.
Connectors: The Boomi platform offers a wide range of connectors, allowing seamless integration with both on-premise and cloud-based applications. You can ensure a smooth and unrestricted workflow by leveraging these connectors.
Improved Data Management: The platform offers a powerful solution known as Master Data Hub, which allows you to manage your data seamlessly across various applications. This provides a holistic view of your data management framework, making it easier for you to track the flow of data between different applications.
Data synchronization: Boomi ensures continuous and accurate data exchange between its Master Data Hub and connected systems through real-time bidirectional flow. This functionality guarantees real-time synchronization of data across multiple applications.
Tableau
Tableau is a powerful data visualization platform that can help you transform your data into actionable insights. With it, you can easily simplify raw data and present it in a clear and understandable format using interactive charts, graphs, and other graphical representations.
Key Features Include:
Supports Numerous Data Sources: You can use Tableau to connect to various data sources, including local files, spreadsheets, databases, big data, and on-cloud data. Furthermore, you can combine data from multiple sources to generate a more comprehensive and holistic view of your data through visualizations.
Data Stories: With Data Stories, you can save valuable time and resources when analyzing data. This powerful tool can summarize critical insights clearly and concisely and even add automated plain-language explanations to your dashboards.
Informative Dashboards: Tableau Dashboards use various components like images, visual objects, and text to present data comprehensively. They offer different layouts and styles, allow you to apply filters, and provide data in the form of stories. You can easily duplicate a dashboard or its individual features from one worksheet to another.
Security: Tableau ensures the safety of data and users by implementing a fail-safe security system based on authentication and authorization mechanisms for user access. Additionally, it allows you to connect to other security protocols such as Kerberos and Active Directory.
Microsoft Azure
Microsoft's Azure cloud computing platform enables you to build, manage, and deploy applications globally using your preferred tools and frameworks. It offers various services, including Infrastructure as a Service, Platform as a Service, and Software as a Service. It also provides serverless computing, where the platform manages all backend activities.
Key Features Include:
Data Storage and Management: With Azure Blob Storage, you can securely store and retrieve vast quantities of unstructured data, including images, videos, and documents. Azure SQL Database provides a fully managed, scalable, relational database service. Additionally, Azure Cosmos DB handles diverse data types with globally distributed capabilities.
Hybrid Environments: By leveraging Microsoft Azure, you can create hybrid environments that allow your resources to be located on both the cloud and on-premise infrastructure. This helps you avoid expensive solutions and stay cost-efficient.
AI and Machine Learning: The integration of Azure with artificial intelligence (AI) and machine learning (ML) services provides you with advanced analytics and automation capabilities. With Azure Machine Learning, you can build and deploy ML models, allowing them to extract valuable insights from your data.
Data Recovery: You can back up your data in different Azure regions or data centers. With the capability to maintain up to six copies of your data, you can be confident that the possibility of losing your data on Azure is very low.
Conclusion
Efficient data management is vital for anyone dealing with large amounts of information. Choosing the set of right data management tools is crucial for maximizing the value of your data. By selecting a tool that aligns with your needs and goals, you can streamline your operations, reduce costs, and gain valuable insights from your data.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
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
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.
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.
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.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
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.
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.
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.