The need for data integration comes out of complex data center surroundings where multiple systems are creating large volumes of info. It helps you query and manipulate all of your data from one interface, performs analytics, and generates statistics.
Of course, your data sources won’t integrate themselves. For that, you’ll have to use an information integration tool or platform, preferably one designed to handle your specific data needs.
These tools often include functionality aimed toward cleansing, transforming, and mapping the info, still monitoring the combination flow itself (error handling, reporting, etc.).
Data integration could be a critical component of a more effective data analytics strategy with data from local, software-based “batch” sources or web-based streaming sources.
What is a Data Integration Tool?
Data integration is the process of intermixing data from different data sources to provide a unified look of combined data. This data must be understood in aggregate instead of in isolation. Data integration is nothing but a technology method for the output of unified and consistent enterprise-type data.
Data Integration Tools Features & Capabilities
Below are the features of the best data integration tools:
- Ability to process data from a large style of sources like mainframes, enterprise applications, spreadsheets, proprietary databases, etc.
- Has capabilities for converting unstructured data from various social media, email, web pages, and more.
- Syntactic and semantic checks to form sure data conforms to business rules and policies.
- Removal of improperly or incorrectly formatted data.
- Support for metadata
Types of Data Integration
There are several different approaches to achieving this goal, which is quite different from every other and practically solve slightly different problems: the most technologies for data integration are Extract, Enterprise Application Integration (EAI), Transform Load (ETL), and Enterprise Information Integration (EII), or data virtualization as called more often these days.
Products listed during this category belong to the ETL data integration approach. Unlike the opposite listed methods, ETL is intended for data migration and integration of vast volumes of information to produce a basis for decision-making.
What is an ETL?
ETL stands for Extract, Transfer, and Load, a data integration process of three steps. ETL is acquiring large volumes of data extracted from many databases and then converted into a format. The info is then cleaned and loaded into the specialized reporting database called an information warehouse. It’s then available for traditional reporting purposes.
The ETL data pulls out from the sources with Excel data, flat files, mainframe application data, CRM, and EPR data. Perhaps the foremost difficult part of the method is that the “Transform” component. Here, not only must the information be cleansed and any duplicates removed, but the software also must resolve data consistency issues. It applies rules to consistently convert data to a suitable form for the info warehouse or repository.
Once the information has been uploaded into a knowledge warehouse, it’s available for querying by business intelligence front-end processes that may pull consolidated data into reports and dashboards.
Different Types of Data Integration Tools
Here are the different types of data integration tools:
On-Premise Data Integration Tools
These tools stand out by integrating data from various on-premise or local data sources. Generally, data integration tools are loaded within the local networks or private cloud. They optimized local connectors for batch loading from different familiar data sources. On-premise data sources tend to incorporate more extensive or legacy databases.
Here’s an inventory of standard on-premise data integration tools:
- Centerprise Data Integrator
- IBM InfoSphere
- Informatica PowerCenter
- Microsoft SQL
- Oracle Data Service Integrator
- Talend Data Integration
Open-Source Data Integration Tools
If you have the house expertise, you may want to contemplate open source solutions to your data integration needs. An open-source is an honest option if you’re trying to avoid using proprietary, potentially expensive enterprise solutions or if you wish to own complete control over your data in-house.
Confine mind, though, that internal open source projects often have hidden or unexpected costs (servers/hardware, network throughput, training, etc.). And, counting on your situation, you will even have to handle data security and privacy compliance.
Here’s a listing of standard open-source data integration tools:
- Talend Open Studio
Cloud-Based Data Integration Tools
Most cloud-based data integration tools are integrated platforms that merge data from multiple platforms into the data warehouse, which is cloud-based. This type of service is usually “born of the web” and designed to handle newer, web-based streaming data sources further because of the common databases.
New web-based data sources tend to return online frequently. A key component of cloud-based services is integrating them quickly, sometimes via APIs/SDKs/Webhooks.
Here’s an inventory of a number of the more common cloud-based data integration services and tools:
- Dell Boomi AtomSphere
- Informatica Cloud Data Integration
- MuleSoft Anypoint Platform
- Oracle Integration Cloud Service
- Salesforce Platform: Salesforce Connect
- Talend Cloud Integration
How to Choose the Appropriate Data Integration Tool
That’s a lengthy list of candidates, and there are other, smaller solutions not present. But what is the right way to choose the correct data integration tool to use?
Consider these factors in your decision:
For the larger enterprise, your data needs to grow, so does the data integration formula’s complexity. You must remember that there are more and more streams and web-based data sources being created daily — selecting a tool or service which will grow to accommodate your expanding data is paramount.
New Data Sources and Throughput:
Remember, you will need quite just additional storage. You will need an answer that may hook up with the assorted new streaming and web-based data sources. Some legacy/on-prem tools aren’t ready to handle streaming data sources or do so sub-optimally.
Your Integration Use-Case:
An on-premise solution is often the correct call; if you’re sure that your data analysis plans won’t involve a full-scale move to the cloud, you have got data growth in restraint. There also are open source/”roll your own” approaches, though be sure before attempting those: you will need to make sure you’ve got the right expertise and resources in-house.
Security and Compliance:
Confirm that your solution (or in-house team) has the expertise and resources to ensure you’re covered when it involves security/privacy and compliance.
Shortcomings of Information Warehouses
One shortcoming of the information warehouse approach is that the info isn’t always current. Data warehouses take data periodically from the databases, not in real-time.
If the source database’s info has changed, this won’t be reflected within the warehouse’s data. Various strategies will be employed to attain “real-time ETL,” although many of them place a big load on the database. It will have performance repercussions.
The simplest thing to try to do is increase the frequency of batch updates to close real-time operations. But there are other solutions, including continuously feeding the database using real-time data transport technologies, the utilization of staging tables, or a real-time data cache.
Enterprise-level data integration tools come at a high cost. For instance, some products’ prices rise to $10,000 per annum per year. On top of that, you will get hold of professional services to induce up and to run. SMB solutions are significantly cheaper than this.
List of Top 7 Most Prominent and Best Data Integration Tools
Microsoft provides its own SQL Server Integration Services for connecting SQL Server Data of various databases and allows easy migration onto one arrangement. All the information will be easily migrated with no data loss.
Data Integration: Hybrid data integration service.
- Through SSIS complex joins queries, data replication is used for bulk and batch data migration techniques.
- These data can also be placed under the Extract, Transform, and cargo tool for better performance.
- As well as supports business intelligence support to resolve very complex solutions efficiently with lesser effort.
Connectors: Multiple native data connectors.
Price: Data Pipeline: Starts at $1 /1000 activity runs per month. SQL Server integration services: $0.84/hour.
For the latest information on price, visit the page, Microsoft.
2. Hevo Data
Hevo is an automatic data pipeline platform that helps you bring data from an extensive range of knowledge sources (Databases, Cloud Applications, SDKs, and Streaming) into any data warehouse without having to put in writing any code.
Data Integration: Automated Data Pipeline Platform. Supports both ETL and ELT.
- Hevo is easy to implement, as it is often founded and run in precisely some minutes.
- Hevo’s powerful algorithms can detect incoming data schema and replicate the identical within the data warehouse with none manual intervention, robust automatic schema detection, and mapping.
- Hevo is made on a real-time streaming architecture, ensuring that it is loaded to your warehouse in real-time.
- It has powerful features that allow transforming, scrub, and enrich data while to and fro the warehouse. The process ensures you for having analysis-ready data.
- Hevo has enterprise-grade security.
- This tool provides detailed alerts and granular monitoring found, so you’re always on top of your data.
Connectors: Cloud Applications (Salesforce, Google Analytics, Facebook Ads, Google Ads and more), 100+ Pre-built Connectors across Databases (MongoDB, MySQL, PostgreSQL, and more), File Storage (Google Cloud Storage, Amazon S3, etc.) and Streaming (SQS, Kafka, Webhooks, REST API, etc.)
Price: Contact the company for cost.
For the latest information on price, visit the page, Hevo Data.
Oracle Data Integrator may be a comprehensive data integration platform, which provides continued and uninterrupted access to data across various systems.
Data Integration: Cloud-based data integration.
- Enables huge data manipulation and integrations.
- Oracle has a performance-oriented approach for elegantly managing the info.
- Oracle incorporates a unique assertive design approach for flawless, instantaneous data integration.
- Oracle provides organizations to manage data efficiently and effectively through its smart mechanisms for data migration and straightforward graphical tools.
- It has a powerful sketch-up mechanism that allows easy monitoring of multiple systems.
Connectors: All RDBMS, Oracle, and Non-Oracle technologies.
Price: $ 0.9678 OCPU per hour on monthly flex.
For the latest information on price, visit the page, Oracle.
4. IRI Voracity
IRI Voracity is a one-stop, significant data discovery that includes integration, migration, governance, and analytics platform built on Eclipse.
Data Integration: Fast and cost-effective ETL for unstructured, semi-, and structured data; built-in data profiling, PII masking, quality, BI, test data, CDC, SCD, and metadata management.
- Data profiling, classification, and search for scanning and reporting data sources.
- Support for URL, multi-threaded DB extracts (IRI FACT), Kafka, plus ODBC, MQTT, S3, piped HDFS, NoSQL, or REST sources.
- Data and database migration and replication.
- Data cleansing, validation, and enrichment.
- DB subsetting, PII masking (and re-ID risk scoring), and artificial test data capabilities.
- Embedded reporting, data wrangling for analytic platforms, change data capture, and integrations with KNIME and Splunk.
- It has 4GL metadata management and graphical job design options, such as wizards, diagrams, dialogs, form editors, and script.
Connectors: provides various native and standard connectors for modern sources, on-premise, cloud, or streaming.
Price: CapEx or OpEx for the full platform and point solutions. Unlimited users, inputs, cores per host.
For the latest information on price, visit the page, IRI Voracity.
Talend provides an open-source choice to integrate quickly and is customizable by anyone. It is well-known for its high performance, built on the premise to satisfy analytical data expectations.
They have the most straightforward and most cost-efficient thanks to connecting data. Its unique analytical data-oriented approach brings within the best business analysis and improvises accordingly. Enables the majority development process for faster data migration. Talend incorporates a unique smart data migration mechanism, which binds the info supported by some criteria and migrates it onto the system.
Data Integration: Integrates data with unified development and management tools.
- Open and scalable architecture.
- Five times faster and efficient than Map Reduce.
Connectors: RDBMS: Oracle, Teradata, Microsoft SQL Server, etc. SaaS like NetSuite & more Packaged apps like SAP and Technologies like DropBox.
Price: Free of the plan cost. Talend’s cloud data integration: $1170/ user per month. Two more plans are available for users.
For the latest information on price, visit the page, Talend.
Informatica is a sophisticated Data Transformation system, which supports B2B Data Exchange. It is available to integrate business solutions.
Data Integration: Advanced hybrid data integration capabilities.
- Eliminates the danger of manual infestation with its high performance-oriented data techniques such as data reuse, automation, and agile support.
- It has a smart Data Integration Hub that delivers innovative point-to-point integration with a distributed model.
- It incorporates an unique and agile, end-to-end data integrated source.
- Informatica, a data integrated tool, mixes with Power Center and provisions operational data, which are instantaneous and scalable.
Connectors: Connects to every platform and server.
Price: Starts at $2000 / month.
For the latest information on price, visit the page, Informatica.
Xplenty gives cloud-based infusion for integrating, processing, and preparing data for analytics. It’s an entire toolkit for building data pipelines. Anyone can create a knowledge pipeline with the assistance of Xplenty no matter their tech experience because it offers no-code and low-code options.
By using its API component, you’ll get advanced customization. You’ll be able to implement a range of knowledge integration use cases with Xplenty’s package designer’s assistance. It includes complex data preparation, simple replication, and transforming tasks.
Data Integration: Data Integration platform. API component for advanced customization & flexibility. Supports both the ETL and the ELT.
- It has an intuitive graphic interface for implementing ELT, ETL, or ETLT.
- Efficiently transform, centralize, and prepare data for analysis.
- Transmit data between data warehouses, databases, and data lakes.
- 100+ pre-built connectors available.
- Xplenty supports a Rest API connector to tug in data from any Rest API you would like.
- 24/7 customer support through email, call, chat, and online meeting support.
- It offers low-code or no-code options.
Connectors: Integrations are available for BI Tools, Databases, Logging, Advertising, Analytics, Cloud Storage, etc.
Price: Get a quote—free trial available for seven days.
For the latest information on price, visit the page, Xplenty.
In short, a data integration tool is a software application for performing a data integration process on the data source. It is designed as per your requirements for data integration. Data integration tool helps to transfer, map, and cleanse data. This tool can also be integrated with data governance and data warehouses.
FAQ: Data Integration Tools
What are data integration and its example?
Data integration is the process of intermixing data from different data sources to provide a unified look of combined data. For example, the best data integration tools are Heve Data, Oracle, and Microsoft.
What is ETL data integration?
ETL stands for Extract, Transfer, and Load, a data integration process of three steps. ETL is acquiring large volumes of data extracted from many databases and then converted into a format.
What is ETL best for as compared to other data integration tools?
ETL data integration is best for integrating large volumes of data at one and mass data migration. ETL can also access data from many sources and formats, so it is also useful for businesses with many data types and sources.
What is the purpose of data integration?
The primary purpose of data integration comes from complex data center surroundings where multiple systems create large volumes of info. It helps you query and manipulate all of your data from one interface, performs analytics, and generates statistics.
What businesses benefit most from data integration tools?
The more complex a business’s data infrastructure is, the more they benefit from a data integration tool. More data sources, complex metadata, and large volumes of data are all challenges that data integration tools can help.