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Top 15 data integration tools of 2023

Top 15 data integration tools of 2023

September 14, 2023
September 14, 2023
Top 15 data integration tools of 2023
Everything you need to know about the top 15 data integration tools of 2023.

In the modern world, organizations today have to deal with immense amounts of data coming from different sources and being stored in many locations. It can be difficult to gain a complete view of the details when data is dispersed across various platforms. Having the ability to get a comprehensive understanding of aggregated data can help immensely in decision making processes. Knowing where the specifics are involved and how they interact with other pieces of data gives insight that is otherwise hidden or hard to unlock without having a 360° image of the situation. In this way, understanding all aspects of your organization's information makes it easier for leaders to assess their own business as well as its competitive environment more confidently.

By incorporating data integration Tools into the data ecosystem, the segregated data across multiple locations  can be combined, turned into understandable form, and finally can be loaded into a centralized data storage unit to thus draw valuable business insights.

This article demonstrates everything you need to know about data integration and the top 15 Best data integration tools for 2023. Read along to unveil it layer-by-layer and satisfy your appetite for learning. 

data integration flow chart

What is data integration?

Data integration is the process of consolidating the data available in diverse forms or structures from several data sources into a single centralized destination which could be a database, data warehouse, or any desired destination. It aims to provide a 360-degree comprehensive and holistic view of the organizational data. Data integration enables firms to use raw data to support and improve business value by offering critical insights into both operations and consumer needs. 

Data integration vs ETL

ETL flow chart

Extract, transform and load (ETL) technique was an early attempt to merge & consolidate data spread across various data sources whereas data integration is substantially a more broader procedure. Data integration can be used for more than just moving data from data source to desired data destination. It often includes:

  • Data quality: It refers to defining & maintaining the accuracy, completeness, and timeliness of the data.
  • Defining master database: The people, locations, and things engaged in an organization's operations are described in master data. Master data is used by organizations to apply quality rules, manage transaction structure data, and generate a single golden record which gives context to all business transactions.

To understand further, data integration can produce a consolidated picture of data from numerous sources or converted data ready for use in a specific application or process. In contrast, the ETL-ed data is loaded onto the centralized data repository or the desired data destination where it may be accessed for further analysis.

Since the early days of ETL, the realm of data integration has progressed. However, it must continue to evolve in order to keep up with changing organizational needs and the big data revolution that awaits!

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Data integration vs data ingestion

Now we know what data integration is, so let's dive in to learn about data ingestion to figure out the data integration vs data ingestion battle!

Data ingestion is the process of moving the data from one source or location to another for storage in a data lake, database, or a data warehouse. After extracting the data from its original form it is then transformed into a suitable form for storage purposes. Typically the data is extracted from CSV, Excel, JSON, and XML files, and then finally is loaded onto the target system.

Whereas in data ingestion the data is not processed before being loaded into the desired destination. It merely transfers data from one system to another. This means that data is sent in its original form, with no alteration or filtering.

The process of collecting and transmitting data from numerous input sources to the target storage for further processing is known as data ingestion. On the other hand, data integration brings together raw data from diverse sources, transforms it, and loads it into a data warehouse or desired destination.

What do data integration tools do?

what do data integration tools do?

Todays’ rapidly changing times necessitates data integration to be scalable and multi cloud compatible. Manual Data integration is time-consuming, expensive, error-prone and consumes a large engineering bandwidth. It surely is a time-consuming job that requires continuous monitoring of the Data ecosystem to guarantee no data is lost. 

This challenge has given rise to data integration Tools for providing the flexibility and scalability required by enterprises to stay up with new big data use cases. Data integration tools are software-based solutions that ingest, consolidate, transform, and transmit data from its origin to a destination location, as well as conduct mappings and data cleansing. Data integration tools potentially simplify and make the process efficient & hassle-free. 

Challenges in data integration 

The quintillion bytes of data generated everyday has made ETL and data integration essentially crucial for enterprises. To fully leverage the available data, firms now need to integrate, manage & organize the available data obtained from diversified data sources.It often becomes challenging for developers to use ETL and data integration tools as the organization expands & the volume of data grows.

Data integration is no cake-walk. Data integration can be tiresome & a challenging task to pursue.. Here are some of the common issues that organizations face: 

Data quality:

The utility and dependability of data are sometimes limited by out-of-date, incorrect, insufficient, and improperly prepared data. Data inconsistencies, sluggish data integration, and inaccurate outcomes can all result from this. Data quality problems may also occur as an outcome of aggregating many data formats across multiple sources which thus leads to false conclusions and poor decisions. 

Data volume:

It can be challenging for conventional platforms to handle the data effectively when the volume of data that needs to be integrated is enormous.

Security:

Organizations must be extra precarious during the data integration to guarantee the security of their data. To protect sensitive data, it's crucial to implement strong security measures. This involves setting up access control mechanisms to restrict who can view the data and encrypting it before it is delivered or loaded in a cloud-based system. 

Scalability:

As organizations expand, the volume of data will also grow which will require the organization to scale the Data integration process.Therefore, a prudent and forward-looking choice must be made to prevent losing any critical insights or possibilities as a result of sluggish or out-of-date data processing.

Cost:

Data integration and ingestion necessitate an investment of both time and money. Costs might vary greatly depending on the intricacy of the project, therefore it is critical to examine the resources that the project requires and how much it will affect the budget.

Diverse data sources:

The formats, structures, and schemas of data that are available across numerous sources vary. In order to integrate data from all your sources, they typically require extensive modification and mapping. Integrating the data that is kept in many places, such as cloud networks and systems as well as on-premise infrastructure, becomes a challenging issue.

Ineffective integration solutions:

Integration systems that have been poorly developed or executed may have problems with their performance under varying workloads. They lack the ability to map data from various sources, or the compatibility for various data types/structures. 

Types of data integration tools

1. On-premise:

This type of data integration Tool is the preferred choice for organizations that need to combine data in a variety of formats from numerous on-premise or local sources. They are hosted on a local network/private cloud together with native connectors that have been tuned for batch loading from various data sources.

2. Cloud-based:

Cloud-based data integration tools, integration platforms as a service (iPaaS), enable organizations to access and manage apps and data from multiple sources into a cloud-based Data Warehouse. By breaking down software silos, it allows the firm to monitor and control different apps from a single, centralized system. Cloud integration tools can help IT teams bridge the digital divide by Cloud integration brings together various cloud-based applications into a unified platform.

3. Open-source:

To avoid using proprietary and perhaps pricey corporate software development tools, these are the best available options. Additionally, it also allows total control over the internal data.

4. Proprietary:

The cost of these tools is essentially what sets them apart from open-source ones. They are primarily created to cater very effectively to particular corporate use cases.

Key factors to consider data integration tools

The need for the best data integration Tools is prevailing across the globe throughout a wide range of industries, and the demand changes according to the company' requirements for integrating data from various sources as well as the volume and complexity of data.

A procedure can be built to automate manual chores and streamline processes for accuracy for each use case of corporate data integration. The data integration system encompasses the fundamental tasks of merging, cleansing, and transporting data from source(s) to destination, all of which can be accomplished in a variety of ways.

The first step in evaluating data integration tools is to gather and prioritize requirements, such as the data source and desired destination. Thus, the organization will be better equipped to traverse the vast selection of data integration Tools accessible if it first has a firm understanding of data management, integration, and business requirements.

The next stage is to compile a list of specific features and functionalities for comparison and evaluation after the requirements are known. The data integration tool that the organization considers should ultimately be the one that best suits its use cases, budget, resources, and capabilities — not necessarily the product with the highest ranking or the most features.

1) Type of data:

Assessing based on the  data source & destination coverage is important if you intend to combine data from many sources and load it into data repositories for analysis or storage. Data sources in a company typically include accounting software, electronic spreadsheets, online monitoring, local databases,, marketing tools, customer relationship management (CRM) systems, enterprise resource planning (ERP) systems, and other tools. 

2) Data Size:

It's critical to assess the data asset the company holds and verify the suitability of the data integration Tool based on the volume of data that needs to be processed.

3) Data transfer frequency:

Select the data integration tool that enables continuous sync scheduling based on how frequently your organization requires data to be updated. You can use this function to define fixed intervals for frequent, brief data syncing. For instance, you might configure your CRM system so that it automatically updates your data warehouse every hour. To transmit data more quickly and with greater comfort, seek for a data integration Tool that offers near real-time integration.

4) Data quality:

Before utilizing a specific data integration Tool, make sure that all errors in the mandatory fields are handled appropriately and that the data flowing into the final database destination is usable. Major data quality concerns include missing fields, duplicate records, and invalid data. Examining the data quality & practicing data cleansing is an essential step to ensure that the key stakeholders have unambiguous data & analysis-ready data

5) Cloud support:

The data obtained after the successful data integration must be able to operate natively across various cloud infrastructures. Data integration tools should work smoothly in operating environments - both on-premises and in the cloud, the latter through hosted deployments or PaaS integration options (platform-as-a-service). A variety of operating systems should be supported by the data integration Tool.

6) Data transform:

Depending on the organizational requirements, it may be required to replicate data in more than one way. As a result, the data integration tool should be able to provide flexible support for replicating the transformed data. 

7) Pricing:

Pricing is an important consideration that alone may determine which data integration solution you select. Broadly the below mentioned categories of data integration tools have unique price schemes as listed:

  1. Open-source: You are responsible for paying the hosting costs, which normally range from $0 to $50 ($0 if you can use an existing server). But the technical cost in terms of time spent implementing and maintaining the solution is a significant expense that frequently goes unnoticed.
  2. SaaS: The standard billing structure is to charge for the data incorporated after the initial load. Depending on how much new data the organization produces and whether your sources can handle increasing loads, you can expect to pay $100 to $5,000 each month.
  3. Enterprise: The cost of enterprise solutions depends on a variety of factors. However, a conservative estimate can range around $5,000 per month.

8) Data sources and destinations:

Make a list of all your potential sources and destinations, both present and future, and confirm that your potential tool will cover each one. Different connectors are more or less willing to be added using these tools. Do keep in mind that different tools have different data interfaces. A tool may have the data port of your choice, but that doesn't automatically make it user-friendly. The setup of some data connectors can be challenging, which can make it challenging for end users to transmit data. Therefore, before selecting a data integration tool, examine the user-friendliness of connections.

9) Support:

Data integration tools should be able to provide 24/7 live & easily accessible support to address any query. In the event that a technical query arises during the initial setup or later, video tutorials and extensive official product documentation should be made available.

Top 15 best data integration tools in 2023

Once you've honed your research on data integration tools with the functionality you require, you should try them out for yourself. The majority of providers offer a free trial that can last a week or longer, giving you enough time to integrate it with your systems and evaluate it. See for yourself how long it takes to synchronize your data or how convenient your internal users find your product by connecting data connectors with your operational sources and data repositories, such as a data lake or data warehouse.

List of Top 15 Data integration tools

  1. Fivetran
  2. Stitch
  3. Integrate
  4. Informatica
  5. Panoply
  6. Talend
  7. Boomi
  8. Snaplogic
  9. Zigiwave
  10. Oracle Data integrator
  11. Pentaho
  12. Jitterbit
  13. Qlik
  14. Alooma
  15. IBM

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1. Fivetran

Fivetran is a low-code, data integration tool that automates ELT processes and offers a multitude of pre-built connectors for well-known data sources. Additionally, users can also raise a request for a connector or build their own if one is not already accessible, as additional connectors are constantly being added by Fivetran’s engineers.

The enormous functionality in Fivetran allows the users to automate pretty much the entire data pipeline. It allows the users to transform & export the data easily using the pre-built data models.

When it comes to streamlining data flows without investing extensive engineering bandwidth writing custom SQL queries, Fivetran is a very practical tool. At times, when a data source is not yet available, custom scripts & models must be developed by the engineering team. With little to no scripting required for ELT, Fivetran comes equipped with all the tools you need.

The cost for any user is solely dependent on how much data is processed through the platform. Fivetran additionally offers a volume discount, which means that the cost per row of data decreases as you sync more rows. You are charged based on the number of rows you insert, amend, or delete each month. Regardless of pricing tier, the cost of utilizing Fivetran will rise as the amount of data you sync increases.

2. Stitch

Stitch is an ETL solution for developers that is now owned by Talend (bought in late 2018). Stitch is a cloud-first, open source data integration tool for quickly migrating data, according to Talend. Stitch is used by more than 3,000 businesses to transfer billions of records from databases and SaaS apps into data warehouses and lakes so they can be evaluated with business intelligence (BI) tools. It comes with a Free plan, a Standard plan, and an Enterprise plan, both of which offer more advanced capabilities. 

PROS

  • Offers easy integration with many data sources
  • Extensible
  • Very straightforward & transparent pricing model

CONS

  • When replicating data repositories like MongoDB to relational databases, Stitch is not very effective. Fair enough, this is a challenging task. Stitch flattens the items, but the final product is cumbersome.
  • It doesn’t support concurrent replication of data to multiple destinations. For instance: It does not allow replication of a few tables from a datastore to X and the rest to Y.

PRICING: Starts at $100/monthly for Standard plan and can go up to $2500/monthly* for a Premium plan. Advanced and Premium plans are billed annually.

3. Integrate

It is a straightforward, easy visual interface that takes the hard work out of creating data pipelines between various sources and destinations. This platform offers more possibilities for data integration than ever before by performing ELT, ReverseETL, actionable insights for the Data warehouse, data observability, and quick Change Data Capture (CDC)..

PROS

  • Easy-to-use and user-friendly ETL tool.
  • Incredibly customizable.
  • Drag-and-drop UI for easy use.
  • Easy third-party platform integration.
  • Excellent customer service team.

CONS

  • The platform's internal error reporting has been puzzling.
  • Demonstrates delay in addition of more data connectors
  • Some features are robust & up-to-date

PRICING: Starts at $15,000/yr for the Starter plan and can go up to $25,000/yr for the Professional plan.

4. Informatica

Informatica Cloud data integration, for Cloud ETL and ELT, enables users to ingest, integrate and cleanse data within Informatica's cloud-native ETL and ELT solution. Users can link source and target data with thousands of connectors that recognize metadata, to make it easier to run complex integrations.

PROS

  • Ability to deal with a high volume of data is commendable.
  • Proactively provide solutions to all emerging data engineering use cases. 
  • Robust & remains up to date with recent data engineering developments.

CONS

  • A bit expensive
  • Lacks internal compatibility between products in the same league without charging more.
  • Incompetent documentation & video tutorial support.

PRICING: Beginning at $2,000 a month is the price for Integration Cloud's Base plan. The cost of the add-on tiers is not made public. Many of Informatica's products are available for a 30-day free trial.

5. Panoply

Panoply is a self-serviced, automated cloud data warehouse that seeks to make the data integration process simpler. Panoply is a cloud data platform that makes it simple to sync, store, and access your data. It can unlock complex insights without investing huge data engineering efforts. To further improve their data integration procedures, Panoply can be used in conjunction with other data integration Tools like Stitch and Fivetran.

PROS

  • Simple and straightforward data integration Tool
  • Quick & offers speedy run time
  • Offers excellent experience with the data models' performance

CONS

  • Pricing models can be quite finance-focussed.
  • The quantity of data sources could be greater, but they are quite accommodating when more sources are requested.
  • Support intervals can be troublesome.

PRICING: Starts at $399.00 per month for the Lite plan and can go up to $2729.00 per month for the Premium plan.

6. Talend

A safe cloud integration platform-as-a-service to integrate all of your cloud and on-premises data (iPaaS). You have access to strong graphical tools, premade integration templates, and a large library of components thanks to Talend Integration Cloud. You can make data-driven decisions with confidence thanks to the market-leading data integrity and quality solutions offered by Talend Cloud's range of apps.

PROS

  • Offers automated backup and disaster recovery.
  • Easy to scale up & down as required.
  • Enhanced data protection mechanism.

CONS

  • Insufficient memory capacity can cause speed & performance degradation
  • Nested options can cause a decay when operating individually
  • Expensive licensing cost.

PRICING: Starts at $1,170 per user, per month or $12,000 annually.

7. Boomi

Boomi is a data Integration Tool that may be used in the cloud, on-premises, or in a hybrid environment. It provides a low-code/no-code interface with the ability to link to external organizations and systems.

PROS 

  • Pre-built connectors for virtually anything substantially speed up the time.
  • Drag & drop-compatible development platform that is simple to use. 
  • Excellent user community with timely assistance.

CONS

  • Utilizing the features of a property is never simple & can be challenging. 
  • Dell Boomi has to focus more on API Management and is not just a data integration Tool. 
  • Demonstrates latency in implementing user’s feedback.

PRICING: Starts at $2,000/month* Pro Plus plan and can go up to $8,000/month* for the Enterprise Plus plan.

8. Snaplogic

SnapLogic's low-code/no-code platform enables data teams to quickly hydrate data lakes, build data pipelines, and provide business teams with the insights they need to make better business choices.

PROS

  • Data from any source system can be extracted in any format & is easily accessible. 
  • Offers sheer convenience to non-technical users by attractive visual depiction of available data transformations.
  • Helpful community forum and offers reasonably good customer service.

CONS

  • Lacking the feature to enable users to retain their version control in GitHub, they must have a method of connection.
  • It gets harder to stitch together all the snaps as pipelines get more complicated
  • No mechanism to prevent an unintentional preview invocation. 

PRICING: Starts at $9995.00/Per-Year*.

9. Zigiwave

Zigiwave, a data integration tool automates the data streaming with just a few clicks. It is a No-code interface for simple integrations that can map entities of any level. Zigiwave (located in Bulgaria), has completed 500 successful integrations and has a growth score of 200%. 

PROS

  • It is very easy to use and understand
  • Comprehensive support & documentation is available to answer how to connect to the source and target tools, how to transform the data, and configuration of the use cases, etc.
  • The support team is readily available, equipped with the necessary knowledge to address and assist with the users’ concern.

CONS

  • Lacks the option for a SaaS implementation.

PRICING: The pricing model of ZigiWave is based on a flat, fixed and yearly billing structure. To know the exact rates, you will have to book an exploratory meeting.

10. Oracle Data Integrator

Oracle provides two distinct products for data integration. On-premises software for it is called Oracle Data Integrator (ODI). It is a comprehensive data integration tool that caters to all data integration needs. It can handle high volumes of data without compromising the high product performance. It is available in two varieties: ODI for Big Data and the Enterprise Edition.

The Oracle data integration Platform Cloud is a cloud-based alternative to it. With a browser-based interface and pre-built connectors for software as a service (SaaS) programs, it offers quick performance.

PROS

  • Ability to easily integrate new technology stacks by creating personalized knowledge modules.
  • Native big data support & fast performance
  • Capacity to write native code for the data management technology being used.

CONS

  • Procedural coding is complex.
  • Requires to load the database before implementing the transformations.

PRICING: On-premise Oracle Data Integrator pricing is negotiated contracts and not made public.ODI Cloud starts at 1.2 $ /GB /hour.

11. Pentaho

The Pentaho platform from Hitachi Vantara unites IT and business users to ingest, prepare, combine, and analyze all data that has an impact on business results. This is accomplished by tightly coupling data integration with business analytics. Pentahos open source history powers a cutting-edge data Integration Tool that aids businesses in accelerating their analytics & data pipelines.

PROS

  • Open-source Java classes that can be used to create personalized UDJCs, expressions, and capability for creating further personalized plug-ins.
  • Simple to set up for transformations and recursive jobs.
  • Numerous data connections are available.

CONS

  • Javascript steps can have a high learning curve for troubleshooting. A modified Javascript step might provide more details on the Pentaho Java classes that are available. 
  • Additional transformation phases, such as a calendar, financial functions like modified return series, covariance (and covariance matrices), and standard deviation, could be offered.

PRICING: Cost can range from $25 to $300 user/month.

12. Jitterbit

Jitterbit is committed to utilizing the power of APIs, integration, and artificial intelligence to accelerate innovation for our customers. Companies can quickly link SaaS, on-premise, and cloud apps with the Jitterbit API integration platform, and they can instantaneously integrate AI into any business process. You may easily transport enormous amounts of data with Jitterbit because of its high-performance parallel processing methods. 

PROS

  • Reliable & easy to use interface
  • Detailed trial 
  • Rapid customer support

CONS

  • Improved versioning and collision detection of updates can be implemented. As sometimes a commit from one Jitterbit developer will overwrite modifications made by another when multiple developers are working simultaneously. 
  • The support forum is helpful, however it can be challenging to browse at times.

PRICING: Jitterbit Pricing is fixed at $1000 per month for the regular version, $2500 for the professional edition, and $5000 for the enterprise edition. To learn more about any further specs and Jitterbit Pricing, speak with vendors.

13. Qlik

Qlik  gives businesses the ability to speed up data replication, ingestion, and streaming across a wide range of databases, and big data platforms. Qlik Replicate transports your data quickly, securely, and efficiently with no operational impact. It is used by hundreds of businesses globally.

Data is sent to the desired streaming system, using Qlik Replicate, which offers automatic, real-time, and universal data integration across all important source endpoints, including databases, SAP systems, mainframes, and Salesforce both on-site and in the cloud.

PROS

  • Near real-time replication capability.
  • Extremely trustworthy and quick in terms of performance
  • Large-coverage of data sources and destinations.

CONS

  • The Replicate web GUI's foundation is weak. It's challenging to gauge how many duties you have if you have dozens or more. All of these issues are resolved by the Enterprise Manager, which must be installed separately.
  • Navigating & finding exactly what you're looking for in the help portal is challenging.
  • The fact that a full reload is necessary to re-sync during replication when the connection is lost wastes a lot of time.

PRICING: Pricing models are not disclosed. Contact Qlik to get a quote!

14. Alooma

Alooma is a data integration Tool that offers ETL capabilities with a focus on the output which is sent to the desired data warehouse. It provides protocols for pipeline monitoring and error management.

PROS

  • Data transformation before loading the data into the data warehouse enables analytics teams to adopt a standard, intelligible schema.
  • Alooma includes a sizable collection of pre-built, typical third-party vendors for which ETL-ing data is frequently required.

CONS

  • Although the data integration Tool works excellent at ingesting and transforming data, additional assistance documentations with managing outputs would be appreciated.
  • Instead of using single event transformations, increase the possibilities during the transformation stage.

PRICING: The pricing models are not made public. To get a quotation, speak with the Alooma Team!

15. IBM

Leading ETL platform IBM® InfoSphere® DataStage® integrates data from many enterprise systems. It makes use of a high performance parallel framework that is accessible both locally and online. Extended metadata management and enterprise connectivity are provided by the scalable platform.

PROS

  • Automated load balancing mechanism.
  • Powerful tool for handling large volumes of data.
  •  Offers a range of partitioning strategies that can assist in optimizing parallel jobs.

CONS

  • Interconnectivity with diverse systems.
  • Detailed user manual is missing.
  • Doesn’t support debugging using check points.

PRICING: Starting at USD 934/month* and can go up to USD 12,142/month*.

Note: Pricing information is either supplied by the software provider or retrieved from publicly accessible pricing materials. Final cost negotiations to purchase must be conducted with the seller.

Conclusion 

Data integration entails several processes, including establishing the scope and objectives of the integration project, cleaning and processing the data, and combining the data using data integration Tools or other methods. It can be difficult to set up an efficient system for integrating data because of challenges with data quality, variations in data format and structure, and security worries.

Nevertheless, it is a crucial component of business intelligence since it makes it possible for firms to obtain and evaluate the data they want in order to make wise decisions. Organizations can overcome these difficulties and successfully use data to promote business success by adhering to best practices for integrating data.

Now that you have a better understanding of data replication, you can begin working on a solution that meets your company's requirements. Writing unique scripts or manually duplicating the data is a viable option if you occasionally need to transmit data from a few sources. However, if your business teams depend on data from many sources every few hours in a compatible format for analysis, you may need to burden your engineering team with unique data connections or you can just automate your data integration procedure using one of the Top data integration Tools as discussed.

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