The importance of data integration has increased significantly in recent years due to its ability to combine information from multiple sources into a single system. This process helps businesses make better decisions, streamline operations, and improve efficiency. The amount of data gathered from different sources in a business ecosystem is increasing, which has also increased the need and utilization of reliable data extraction, transformation, and storage tools. Data integration tools solve this problem by automating the process, reducing errors, and saving time.
The data integration tools provide a wide variety of solutions that can cater to the needs of different businesses. They allow data collection from different sources, reformatting, and real-time or batch storage as per the consumer requirements.
Companies now have access to highly scalable, easy-to-use platforms with features like real-time integration, data mapping, and AI-based automation. As businesses grow, these tools ensure that their data remains consistent, reliable, and ready for analysis.
In this article, we will discuss the different types of data integration tools available in the market, their features, and the pros and cons of each solution.
What is Data integration?
Data integration combines data from different sources into a single unified storage unit. The data integration process involves data replication, ingestion, and transformation to standardize data received from different sources, which is then stored in a unified data repository known as the data warehouse.
What are Data Integration tools?
Data integration tools are software solutions that collect, combine, transform, and move data from its source to a target destination. These tools also handle tasks like data mapping and cleansing to ensure the data is accurate, consistent, and ready for use.
Types of data integration tools
There are several types of data integration tools available in the market. SAAS companies update their existing tools, and new products are regularly introduced.
Here, we have discussed different types of data integration tools used in the market.
Manual data integration
The data is extracted, transformed and stored in the target location by manual coding and queries without using automated software. This approach is feasible at a small scale while working with small datasets, but it has some significant drawbacks, such as being time-consuming, prone to errors, and inefficient while working on large data sets.
Unified Data Integration tools
These tools provide a complete solution for data integration to simplify and automate the entire data integration process. These tools consist of built-in connectors to allow uninterrupted database connectivity, cloud applications, and third-party systems. They also gave features like data mapping, pre-built data transformations, and data governance, allowing businesses to manage and maintain their data pipelines efficiently.
Cloud-based data integration tools
The use of cloud computing technology is increasing. Hence, many businesses are moving their data to the cloud. Cloud-based data integration solutions help organizations easily integrate and manage data from cloud and onsite sources.
Cloud-based data integration tools allow businesses to scale their data operations quickly as they grow. It initially saves them the upfront cost of a big data storage system. This feature helps them optimize their performance and efficiency and minimize the risk of overcapacity or underutilizing of resources.
Extract, transform, and load (ETL) tools
ETL performs data integration in three steps. The first step is to connect different data sources to the system (such as databases, applications, files, and APIs) to extract the required data. In the next step, the data is transformed into a preset format to ensure accuracy and compatibility. In the final step, the transformed data is stored in the target location to be used as required.
Extract, load, and transform (ELT) tool
The ELT is a relatively new category of customer data integration tools. This tool provides better performance and is more scaleable. ELT tools work similarly to ETL tools but handle the data transformation step after loading the data into the destination system. In this approach, data is first loaded in its raw, native form into the target system, such as a data warehouse or cloud-based storage. Once loaded, the transformation and processing are performed using the computing power and capabilities of the target system.
Best Data integration tools list
Altova
Altova is a data integration tool focusing on data transformation and mapping for different file formats, including XML and JSON. It allows users to efficiently transform and map data using its Windows-based IDE and graphical user interface (GUI). As Altova supports various data types, it is much more flexible and can be used in different projects. The interface of Altova can be complex for beginners; it requires time and training to learn and use effectively. Altova’s primary focus is data transformation and mapping, which makes it suitable for data integration tasks like converting data from one format to another.
Astera
Astera uses artificial intelligence (AI) and automation to provide big organizations with an all-in-one data integration solution. It automates data integration by extracting, cleaning, transforming, and storing data. The tool provides GUI and drag-and-drop features that help users perform tasks without manual coding.
AWS App Sync
The APP Syn uses GraphQL APIs to provide a real-time application development environment to help with data integration and file access from different sources in real-time. This tool can be used in IOT applications, real-time chatbots and collaboration, gaming applications, and other cases where the delivery and synchronization of real-time data are essential. The App Sync provides integration with multiple sources like RDS, DynamoDB, Lambda Functions, and HTTP APIs, making it easier to collect data from various sources.
Azure Data Factory
Microsoft Azure Data Factory is a cloud-based data integration tool. It is part of Microsoft’s Azure platform, designed to handle extensive data stored onsite, in cloud storage, or a hybrid system consisting of onsite and cloud storage.
Azure Data Factory works by creating pipelines for data transfer. A pipeline workflow automates data extraction from different sources such as databases, files, or APIs. The data is then cleaned, transformed, and loaded into target systems such as data warehouses or cloud storage. It has a drag-and-drop interface and also supports coding for advanced users.
Celigo
Ceilgo is also a cloud-based platform designed for small and medium businesses. It helps in data collection and integration between different business tools. It has built-in connectors and integration templates, making data integration easier for different business tools. It also has tools to automate processes, data synchronization and workflow creation.
IBM Infosphere
Info Sphere is an ETL tool designed by IBM for data storage. It allows users to transform and clean their data for storage and analysis. Infosphere provides Massive Parallel processing functionality that enhances flexibility and scalability. A significant drawback of this system is its complex user interface, which makes it difficult for a typical user to use it initially. Another issue is its complex infrastructure, which makes it challenging to deploy in an organization.
Informatica
Informatica is an enterprise-level data integration system that uses an AI-based CLAIR engine for data integration. It uses an ETL-based cloud storage system that helps users to collect data from different sources. Informatica is a relatively complex system that makes it challenging to debug mapping and workflows.
Jitterbit
Jitterbit is a data integration tool providing uninterrupted data synchronization and connectivity between databases, systems, and applications. It comes with various tools for data integration, API management, and application integration. It also provides a drag-and-drop interface for ease of use.
Oracle Data Integrator (ODI)
Oracle Data Integrator is a solution designed for Oracle-based systems. It provides high-performance Data management, synchronization, movement, and quality services. It uses target database systems to provide a decreased load time compared to the traditional ETL systems. It also provides connectors to automate the manual integration tasks. Some of the main features of ODI include support for big data, scalability, and a high level of security.
Pentaho
Pentaho is a Business Intelligence tool that also provides Data Integration services. So, along with data integration, it also provides features like reporting dashboards, data analytics, and data mining. It provides a drag-and-drop feature that helps with data handling. Pentaho is an open-source business solution with a premium version available on the market. So, it is ideal for businesses looking for an open-source solution for their data integration and BI requirements. However, deploying an open-source solution like Pentaho can be complex because it requires considerable technical knowledge and expertise.
Snaplogic
Snaplogic is an integrated platform as a service (IPAAS) system based on cloud computing that offers application and data integration capabilities. The system offers an AI-driven integration system, built-in connectors for different functionalities, and a drag-and-drop interface. A significant drawback of the system is its lack of capabilities compared to other systems available in the market. Its user interface is also not very friendly, which makes it challenging to use as a business person.
Talend
Talend is another open-source data integration tool like Pentaho that offers an enterprise package. It offers cloud and onsite deployment options with integrated data quality and governance tools. The Talend can be a reasonable budget-friendly option for organizations, but the main drawback is the need for technical knowledge for its deployment. Another issue is the need for coding skills to be used at their full potential, which is not needed in most other customer data integration solutions.
AirByte
AirByte is an open-source data integration tool in the list. The tool addresses long-tail integrations and allows users to create and contribute their own connectors. This feature enables AirByte to integrate with almost any available source and destination. It offers both onsite and cloud deployment, which makes it more flexible.
Google Cloud Data Flow
Google Cloud flow is a part of the Google Cloud platform, which enables it to provide serverless data processing services. It can handle both real-time and batch processing of data. It uses Apache Beam SDK for data processing, which enables the tool to use the same code for batch and real-time processing. It can be integrated with other Google services like Pub/Sub, Cloud Storage, and Big Query.
Frequently Asked Questionsa
Q-1) What are data integration tools, and why are they important?
Data integration tools are software applications that combine data from various sources into a unified view. This is essential for businesses to make informed decisions, improve data quality, increase efficiency, and gain deeper insights into customer behaviour. Organizations can identify trends, optimize operations, and enhance overall performance by consolidating data.
Q-2) What are some popular data integration tools?
Several powerful data integration tools are available to meet diverse needs. Talend, Informatica, and Fivetran are popular choices for enterprise-level integration. Stitch is a user-friendly option for cloud-based data sources. Open-source tools like Apache Airflow, Apache NiFi, and Apache Kafka offer flexible and customizable solutions.
Q-3) How do I choose the right data integration tool for my business?
Selecting the right data integration tool involves considering factors such as the volume and complexity of data, scalability requirements, ease of use, cost, and integration capabilities. Assessing the tool’s ability to handle your specific data sources and transformation needs is crucial. Consulting with data integration experts can help make an informed decision.
Q-4) What are the challenges of data integration?
Data integration can be challenging due to data quality, security, and scalability. Ensuring data consistency, accuracy, and privacy is essential. As data volumes grow, efficient integration becomes increasingly complex. Real-time integration can be particularly demanding, requiring robust infrastructure and processing capabilities. Establishing effective data governance and management practices is crucial to overcome these challenges.
Conclusion
Data integration tools are necessary for businesses to manage the increasing volume of data. A wide range of tools are available in the market to handle data integration for business. These tools use different technologies like ETL, ELT, and cloud-based systems. Businesses can choose tools based on their needs, budget, and technical expertise.
Each tool has its strengths and limitations. For example, some platforms are suitable for smaller businesses due to their affordability and ease of use, while others offer advanced features that cater to large enterprises. Tools like Azure Data Factory, Google Cloud Data Flow, and Informatica provide solutions for businesses needing automation, scalability, and flexibility. Open-source options like Pentaho, AirByte, and Talend are also becoming popular due to their cost-effectiveness.
