Top 10 Best Data Integration Tools of 2023

Luke Smith
Enterprise Solutions Architect
March 7, 2023
Matt Tanner
Developer Relations Lead
March 7, 2023
Luke Smith
Matt Tanner
Enterprise Solutions Architect
Developer Relations Lead
March 8, 2023
 min read
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Data integration tools are extremely common in the modern data landscape. As the push to bring data together in new and unique ways becomes the norm, data integration tools are the driving force in making this possible. From the legacy approaches and challenges of manual data integration and batch jobs which brought data together, modern tools have come a long way. Easy to set up, maintain, and scale, data integration tools are empowering organizations to push the limits of their data and optimize their data architectures. Let’s take a look at exactly how data integration tools work and some of the most popular tools on the market right now.

Table of Contents

What is Data Integration?

Data integration is the process of bringing data from different sources together into a single destination. Having data available in a single place makes it a lot easier to enable analytics, business intelligence, reporting, or onward loading to another application. The outcome of retrieving data from various source systems into a single unified view leads to meaningful, relevant, valuable, and actionable insights. Data integration itself usually involves a few steps. Depending on the complexity of the integrations and the use cases, the steps required to integrate data may vary. The data integration process typically includes data extraction, data cleansing, data ingestion, data validation, data modeling, and so on. 

Data integration unlocks hidden opportunities for organizations to make better and more informed decisions. Data integration does this by enabling a holistic view of an organization's operations to improve business productivity and get the most out of their data. Data integration can be accomplished in many ways, but in general, it is accomplished through a tool or platform. There are several integration tools available to empower businesses to maximize the value of the data. We will cover many of these tools in our analysis later in the article. First, let us look at what data integration tools are and what they can do.

What Do Data Integration Tools Do?

A typical business may use lots of systems and applications to store and process its data. This spreading of data throughout an organization makes it difficult for teams to access data across the organization in a unified view. Data from CRMs, ERPs, web traffic, marketing and sales platforms, and other important systems may pose technical issues and be tough to assimilate in a single place. Issues such as system compatibility, data formats, and others may lead to difficulties with data integration or sub-optimal results leading to stale, incomplete, or missing data. 

Technology has been helping businesses to thrive in a constantly evolving data-driven world. The impact on data integration tools is no exception to this improvement. These improvements have helped data integration tools to help us get comprehensive, reliable, correct, and current data. The push for more scalable and easy-to-use data integration tools has opened up use cases that previously seemed impossible.

A data integration tool moves data from different sources into a destination or target database system. Such tools typically automate manual processes and routines, while also performing complementary tasks such as mapping, data transformation, and cleaning. The automations ensure a scheduled or triggered data flow, simplifying data integration processes. By helping organizations save time and cost, these tools can significantly improve overall business operations. Organizations can also integrate several of these tools with data governance and data quality tools.

Organizations leverage data integration tools to connect siloed systems and move transformed data across the organization with ease. This helps to avoid bottlenecks and roadblocks encountered by teams when dealing with data from multiple sources. In the past, moving data across an organization without such a tool could potentially lead to issues with data quality, data integrity, availability, lost time, and ultimately, missed opportunities. Modern data integration tools have alleviated many of these worries and issues.

The adoption of modern data integration tools have given rise to better data, improved data governance, and tighter data security. These tools have enabled better collaboration between departments within an organization by supplying faster connections between data storages and increased efficiency. Improved management of business and customer data are the main goals and benefits of using data integration tools.

Challenges in Data Integration

With the complexities around modern data, including where the data is stored and its format, integration can be tough to do. Challenges in linking a multi-faceted set of systems together can usually be dampened by the right data integration tool, but not always. The challenges can include ones that are technical in nature as well as organizational. Below are a few challenges that can afflict a data integration project:

Lack of Planning 

Data is only relevant when it is used in an operation where insights can be derived from it. This makes it pertinent to plan how you want to go about your data integration before venturing into it. Questions such as the purpose of integration, the usefulness of the data, and the formats to be joined should be answered and thought of. This will eliminate using the wrong integration tool and help you achieve your data integration goals.

Difficulty in Locating or Finding Your Data Quickly

A lot of time can be wasted when trying to find the data that you need if you do not know where to locate the data. A challenge like this can lead to low productivity because of the inability to access required data on time and easily.

Having to Deal With Too Much Data

When there is no plan for when and how data is to be collected, you may be faced with the challenge of having to deal with much more data than is required. If data is not needed, your destination database or data warehouse may end up with lots of undesired data in it, leading to unwanted data redundancy and increased storage costs.

Using the Wrong Integration Tool

Not using the right integration software that meets your business needs can be a challenge in integrating your data correctly. Exploring the available data integration tools on the market to know which best works for you and using them correctly to accomplish your aim is imperative in surmounting this challenge. Many tools have similar functionalities but almost all have some type of differentiator that may increase or decrease the tool's usefulness for your use case.

Formats and Sources

Data formats from disparate sources might not be consistent. Different teams or departments such as sales, customer service, human resources, logistics, marketing, and social media feed data likely have different data formats which will need to be handled. This data may become difficult to access, organize, maintain, and integrate. This can be addressed by having specified data formatting across multiple platforms within the organization to ensure data quality and consistency. Data wrangling and cleaning tools can also be used to sort out this problem as they are designed to format data across different platforms into a single, usable base format.

Low-Quality or Outdated Data

If there are no measures set for data entry and maintenance, the quality of data you get during data integration may be stale and docile. This can come as a result of collecting data constantly without having checks to ensure that data is not inaccurate, outdated, duplicated, or incomplete. This can be fixed by enforcing data quality management practices to help organize and deal with inconsistent data. 

Coupled Data

Having data coupled with or dependent on other applications can be difficult to extract and integrate elsewhere. This is a major challenge, especially with legacy applications. The approach to handling this is generally dealt with on an individualized basis, making it a heavy lift in some cases.

Using Manual Data Integration

Smaller businesses without the resources to use automated data integration tools may opt for a manual data integration process. This leads to significant data migration challenges as they are prone to human errors that are hard to debug and diagnose. Generally, these projects involve lots of man hours to be consumed during the data integration process.

What are the types of Data Integration Tools?

Many types of data integration tools exist. Each has its pros and cons and some may not work for every use case. The types of data integration tools we will explore include On-premise, Cloud-based, and Open-source. Let’s take a look at each of the types in more detail.

On-premise Data Integration Tools

These are data integration tools that are deployed in a self-managed, on-premise fashion. These can be deployed on an organization's bare metal or private cloud infrastructure, as well as on public clouds like Google Cloud. The on-premise part of the tool usually just denotes that the software should be self-managed. On-premise tools tend to be the most flexible when it comes to customization and connector support.

Cloud-based Data Integration Tools

These are data integration platforms that are fully managed and are usually the easiest to get up and running. These tools are SaaS solutions, specifically known as Integration Platform as a Service (IPaaS). They run as web applications allowing you to set up integrations from your browser without having to install a cloud data integration tool except for add-ons that may be provided additionally.

Open-source Data Integration Tools

The open-source data integration tool is used when you want complete control of your data integration process. The codebase can easily be customized and tweaked to fit the exact needs of your data integration use case. Configuring the tool will usually require coding skills and is a little more cumbersome than the previously mentioned tools. The benefit of open-source tools is that they are a cheaper option than proprietary and enterprise software. These tools are free to deploy but may have some costs associated with add-ons and any enterprise support packages, if available.

Factors to Consider When Choosing Data Integration Tools

Before choosing a data integration tool, it is important to determine the purpose of your data integration use case. This will determine how you want to go about integrating your data so you can make an informed decision before selecting a data integration tool. This becomes important since not all the available data integration tools will answer or meet your specific business needs. Below, are some key features and criteria to consider before picking the best data integration tool for your organization:

Type of Data

Choosing the correct data integration tool for your business should be determined by the types of data you’re generating and want to integrate. There are lots of vendors out there, but you have to choose the one that takes into account your specific data type needs. The data type may be structured, semi-structured, or unstructured and the tool should be easy to connect to internal and external data sources. 

Data Size

Select a data integration tool that will cater to the exact size of your data needs. In some cases, you may not have an enormous amount of data so choosing a data integration tool to handle a small volume would do just fine. However, if you expect your data volumes to increase as the business grows, it is better to choose an integration tool that can allow you to scale. This ensures that the tool meets your future expectations without having to change to another tool.

Data Transfer Frequency

This has to do with the rate at which you will need to transfer or integrate data from the data source to the destination. You have to select an efficient data integration tool that can handle the data volume and frequency of your data updates to ensure a smooth and hassle-free data integration. This is especially important if you need data in real time since not all platforms support real-time data integration.

Data Quality

With the emergence of big data, data quality has become a substantial challenge for data-driven organizations since it helps in decision-making. Good data quality is vital to the success of your data integration project and not using a good data integration tool can compromise the quality of your data before, during, and after the data integration is operational. This is a major factor in what can make a data integration project eventually fail.

Cloud support

As mentioned in the types of data integration tools, some offer cloud-based support while others do not. Deciding if you need a cloud-supported data integration tool will be dependent on your business needs.

Data Transformation

It is also necessary to select a data integration tool that makes it easy to transform data. Most times, data coming from different applications may not all be in the same format or a usable format. Having an integration tool that can perform data transformation when moving data in a common data format from the data source to the destination makes data integration fast and simple.


Before deciding on the integration tool to use, you’ll need to understand the amount you’ve budgeted for data integration tools and the cost of implementation. Look at the total cost of ownership, including licensing and usage fees, support costs, and costs for maintaining the tool, including any infrastructure costs.

Data Sources and Destinations

When choosing a data integration tool, it is important to choose a tool that can accommodate and offer support for many data sources or formats. There are structured and unstructured data and lots of available data sources today ranging from databases and data warehouses to streams and web-based applications. It is important to choose a tool that is flexible and can help you grow and adapt to the expanding list of data sources and destinations you may need now and in the future.


If something goes wrong during the implementation or operation of the data integration tool, you’ll want to ensure that the vendor can offer support services. This can come in a few different flavors, including offering you direct customer support, providing documentation or educational materials, or having an active community that can answer any questions that come up.

Top 10 Best Data Integration Tools of 2023

Although many tools exist, we have compiled a list of the 10 best data integration tools currently on the market. Some of these tools are legacy providers with a proven track record, while others are newer to the scene and offer new and exciting approaches to data integration. Let’s explore each of the options below.


Arcion helps transition legacy on-prem systems to modern, cloud-based architectures through an intuitive, near-zero-code interface, ensuring faster and more cost-effective data migrations. Arcion empowers enterprises to withstand modern challenges and usher in the next evolution of digital transformation without bogging down their data engineering team. It helps you migrate your data reliably across applications and systems with Arcion’s streaming ELT and user-friendly migration options without worrying about volume and complexity. 

Some highlights of Arcion include:

  • Arcion ensures zero-downtime migration with low-latency Change Data Capture. Arcion is the only distributed, end-to-end multi-threaded CDC solution that auto-scales vertically & horizontally. Arcion user, Publica, experienced an average of 1 second lag between a change in their MySQL and SingleStore. 
  • Arcion is 100% agentless with its CDC approach. The agentless CDC applies to all the complex enterprise databases including SQL Server. Arcion reads directly from database logs, never reading from the database itself. 
  • Automatic schema conversion and schema evolution (DDLs)
  • Zero data loss migrations and continuous data validation
  • Arcion supports bi-directional replication out of the box for users to fallback at any time
  • Arcion logically partitions sources with thousands of partitions & sub-partitions plus billions of rows to load to the target in parallel blocks that eliminate latency.  For each partition, Arcion maintains a metadata checkpoint, which means that if there is any disruption, each partition is automatically scanned to determine the exact recovery need.
  • Arcion reduces migration project budgets and timelines by at least 90%
  • Arcion offers out-of-the-box High Availability functionality to all customers without additional infrastructure costs.
  • Arcion has the lowest Total Cost of Ownership (TCO) for any CDC tools in the market


Fivetran is a data integration tool that allows users to replicate large volumes of data in real time between the data source and the destination with minimal impact on database performance. It enables data movement out of, into, and across your cloud data platforms.


  • Fivetran has an easy-to-use interface with data governance, data controls, data mapping, etc.
  • Fivetran helps reduce latency and it is a comprehensive solution for cloud data integration.
  • It accelerates data movement ensuring real-time and accurate data reporting.
  • It seamlessly integrates with over 300+ pre-built, no-code source connectors.
  • It provides customer support when needed and it is fully extensible and configurable with any data stack.


  • Majority of the pre-built 300 connectors are SaaS connectors. With the recent acquisition of HVR, Fivetran started to integrate database connectors. 
  • It does not have a robust consulting/integration support service.
  • Fivetran doesn’t provide real-time data integration and its pricing model determines its sync frequency
  • It only offers one-directional data sync.


Informatica is a comprehensive data management cloud service that can be used to drive ideas, create efficiencies and reduce costs.  It helps businesses to unlock data through its Intelligent Data Management Cloud companies using AI engines all on one platform to drive better business results. It also offers two similar tools that aid in data integration namely Informatica PowerCenter and the Informatica Cloud Data Integration (ICDI) geared towards large enterprises for high data integration at scale.


  • Informatica is a polished and refined data integration tool that is open and highly flexible.
  • It supports multi-cloud and hybrid models for migrating on-premise data.
  • It has a low-code, no-code model to avoid the risk of custom coding.
  • It provides a unified data management cloud solution.
  • It can scale seamlessly with your big data needs.


  • It is a powerful but complex tool as you will need a lot of tutorials and learning before you can unlock its full potential.
  • It has non-transparent pricing and no self-service model. You have to contact sales just to use it and go through a contracting process.


Talend is an open-source data integration platform that combines data integration, data integrity, and data governance in a single, low-code model that works with various data sources and architecture. It is known for modern data management and high performance as it is built to meet your analytics needs. Talend has a smart data migration mechanism that can be executed on the cloud or on-premise as paid data integration software.


  • Talend offers complete support throughout your data’s lifecycle across platforms from integration to delivery.
  • It can be deployed on-premise, cloud, multi-cloud, or hybrid cloud. 
  • It has a simple GUI to visualize data pipelines to help in building scalable ETL and ELT data pipelines.
  • It is capable of performing both simple and complex transformations.
  • It has over 300+ connectors to help in business data integrations.


  • This open-source solution does not include tools for CDC, continuous integration, data security, versioning, or data cataloging. 
  • Talend lacks documentation for some of its features for learning.
  • Lots of components are reserved for its paid service and are often locked out.
  • Writing transformations in Talend is often labor-intensive.

Oracle Data Integrator 

Oracle Data Integrator is a comprehensive data integration platform that covers all data migration requirements. It is one of the popular data integration tools that offer seamless integration for SaaS and SOA-enabled data services. Its flow-based declarative GUI tools provide a fast and wonderful user experience offering interoperability with Oracle Warehouse Builder (OWB) for quick and simple migration.


  • Oracle Data Integrator automatically detects any error in the data when performing the transforming and loading of the data. It corrects the error before reloading it again.
  • It offers great productivity with low maintenance and high performance.
  • It has support for file technologies like XML, ERPs, and RDBMSs like Oracle, IBM, Netezza, Teradata, and so on.


  • It does not offer a free trial or free/freemium versions
  • It does not have an easily accessible support system or access to documentation.
  • It can be confusing to use for one who is not vested as you may find implementation difficult.


Integrate provides a cloud-based solution to integrate, process, and prepare data for analytics. It can be used for building data pipelines regardless of your technical expertise since it offers no-code and low-code options, and for implementing a variety of data integrations such as replication, transformation, data preparation, etc. 


  • It has an intuitive GUI making it easy to implement ETL and ELT.
  • It is efficient for transforming and preparing data for analytics.
  • It has 100+ pre-built connectors.
  • It supports a REST API connector needed to pull in data.
  • It is easy to use Integrate to transfer data between databases, data warehouses, or data lakes.
  • It has a low or no-code option


  • It does not offer a free trial or free/freemium versions
  • It does not have a robust consulting/integration support service.
  • It can be difficult to use if you have no experience in data development
  • When faults or errors occur, debugging can become an issue.  


Ascend is a unified and automated platform to ingest, transform, and deliver data to any system anywhere. It helps break through constraints to build, manage, and optimize any increasing amount of data workloads. It delivers native connections to libraries of common data sources with its Flex-Code connectors using a Python snippet to connect your data to Ascend’s systems.


  • Ascend offers reliable data transformation to build, iterate on, and run with its multi-language flex-code interface.
  • DataAware orchestration works continuously in the background to guarantee data integrity and optimize data workloads.
  • You can quickly view your data lineage, data profiles, job, and user logs, system health, and other critical workload metrics at a glance using Ascend’s workload observability.


  • It does not have a free trial version.
  • It cannot directly connect to a larger range of domains.
  • It has a more differentiated member-only subscription page.


Coalesce is a data transformation tool that is built using a column-aware architecture enabling users to automate and enhance column lineage to be able to scale data. Its unique code-first, GUI user experience makes it easy to access your data in new ways unimagined before and its automated templates can be shared across your entire teams.


  • Coalesce is built exclusively for Snowflake
  • Coalesce code-first, GUI-driven approach enables you to build and maintain your data transformation.
  • It is built using a column-aware architecture to automate and enhance column lineage, impact analysis, and the creation or maintenance of database objects.


  • It can be costly for small-scale businesses.
  • It is a powerful but complex tool therefore, materials will be needed to use it efficiently.


Dataddo is a data integration platform that is used to equip professionals by collecting data from any source to a destination of your choice giving insights for better decisions that count. It helps you send data from online sources to dashboarding applications like Power BI, Tableau, and Looker Studio. Dataddo also supports reverse ETL and data replication functionalities. 


  • Simple Data integration is easy with Dataddo as API changes, data pipeline maintenance, and scalability issues are handled without stress with its Headless data integration plan.
  • Dataddo offers SmartCache storage where you can store your long-term data without the need of having an external storage system.
  • It uses snapshotting mechanisms to create data footprints of the source of data at a particular point in time.
  • It has over 200+ connectors.


  • Majority of the pre-built connectors are SaaS-based. It has very limited support in database or data warehouse connectors (e.g., MSSQL, PostgreSQL, CockroachDB, MongoDB, Vertica, MySQL), especially enterprise database connectors. 
  • It can be difficult to cancel a subscription.
  • They can be costing duplications encountered when pulling from the same source of data.
  • Its cost is on the high side for small businesses.


Digibee helps you build, update, extend, run, and operate complex integrations at scale with ease and confidence. It digitally transforms and moves your data with low code using unparalleled speed and scalability. It is cloud-based and offers support and maintenance.


  • Digibee is designed to solve integrations across certain systems by accelerating developers instead of replacing them. 
  • It applies an easy-to-learn, low-code approach that scales easily to achieve your objectives.
  • It has native components such as enterprise applications, files, and other tools.
  • It is cloud-native and based fully on Kubernetes.
  • The Digibee system provides unparalleled protection and security.


  • It has limited support for enterprise databases and data warehouses support.
  • No free trial or free version.
  • The log interface could be difficult to use.
  • Less detailed documentation on its operations. 


In this article, we talked about data integration tools and highlighted the 10 best data integration tools for 2023. We discussed the fine details of what data integration is and how data integration tools work. The overview of data integration tools should enable you to have a better understanding of what tools exist and their capabilities so you can choose the right tool to meet your business needs.

Looking for the easiest path to get your data integration use case up and running while ensuring reliability and scalability? Look no further than Arcion. As mentioned above, Arcion is one of the best data integration tools that meet all the requirements for a flexible, intuitive, easy-to-manage database integration solution. It is available in both on-premises and cloud offerings and has connectors to the most popular database and data warehouse systems including Snowflake, Databricks, Oracle, and SQL Server. Arcion is also incredibly quick to configure and delivers extremely performant data pipelines. Book a call with our database team today to see Arcion in action or sign up for Arcion Cloud for free and unlock the power of your data through zero data loss and zero downtime pipelines in minutes.

Matt is a developer at heart with a passion for data, software architecture, and writing technical content. In the past, Matt worked at some of the largest finance and insurance companies in Canada before pivoting to working for fast-growing startups.
Luke has two decades of experience working with database technologies and has worked for companies like Oracle, AWS, and MariaDB. He is experienced in C++, Python, and JavaScript. He now works at Arcion as an Enterprise Solutions Architect to help companies simplify their data replication process.
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