How To Choose The Best Data Integration Tool For Business?

Finding the best tools for data integration can be a tough nut to crack since the market is full of options that make choosing the right one even harder. Reading this article will guide you to find the best suitable tool for data integration for your business.

Introduction To Data Integration

In simple words, Data Integration is a process for connecting data from any data warehouse to its respective destination. The ideal Data Integration purpose is to collect data from the source ensuring that it's reliable and secured and load it to a single destination and process instant analysis plus reporting.

What Is Data Integration Tools?

A Data Integration tool/platform is used for moving data from data warehousing sources to the destination. It is operated to accomplish data mapping, data transformation, and data cleansing.

Types Of Data Integration Tools

The types of Data Integration tools are as follows:

• On-premise Data Integration Tools: The tools which are installed on the private cloud or local network are the on-premise data source tools that help in optimizing native connectors to the destination from the data sources.
• Cloud-based Data Integration Tools: The cloud-based data integration helps the iPaas for moving data from the actual source and loading it to the cloud-based data warehousing system.
• Open-source Data Integration Tools: This variety of data integration supports the utilization of the ownership and the key software development components. Managing in-house data integration can be best suited with open-source data integration.
• Proprietary Data Integration Tools: This data integration tool lies in the little pricier segment, which implies that only larger business use cases opt for this specific one.

Factors Essential For Choosing The Right Data Integration Tools

1) Data Sources Supported:

For the firm's growth the data integration gets much more complex and hard to handle. However, there are several tools available for resolving the daily requirement of data integration needs of your organization so keeping it in mind is essential while choosing the right data integration interface.

2) Scalability:

Scalability is another essential factor required for managing the data volume while integrating a massive amount of data. Keeping the track record of the customers along with the data infrastructure and the volume of the database. Choosing the data integration tools while ensuring the flexibility of scaling up or scaling down is an extremely necessary component.

3) Security and Compliance:

Managing the massive amount of data and ensuring every bit is safe and secure along with the assurance of legal compliance and legitimacy. It is important to verify the critical data mission with security and compliance.

4) Real-Time Data Availability:

Real-time data availability is applicable only when the data analysis takes place at the same moment. It supports the primary use case however, the feature is not found in all the data integration tools. It also enables you to load data from sources to destinations in batches.

5) Data Transformations:

Data transformation is one essential component that emphasizes extracting data from several sources in various formatting styles. Data transformation plays an important role in faster and smoother analysis for data integration tools.

List Of Best Data Integration Platforms

• Lyftrondata Integration
• Hevo Data Integration
• Talend data integration
• Dell Boomi Data Integration
• Informatica Powercenter Data Integration

a. Lyftrondata

Lyftrondata enables you to fetch, transform and load data from all data warehousing sources to any destination using BI tools. Lyftrondata is a data fabric platform that supports you with 300+ data integrations to software as a service SaaS platforms. It facilitates a smooth data integration by implementing Lyft, shift, and load with no code/low code automation. Lyftrondata enables effortless data integration for all sources just like it does for ServiceNow integration.

Features Of Lyftrondata:

• Supported Data Source: The on-premise and cloud data sources can be registered. It can be queried in real-time and will also help build data replication or integration using targeted data warehouses in real time.
• Supported Target Data Warehouses: Connect a target data warehouse and replicate data from source systems. Create additional data sets in the target data warehouse and rebuild them without moving the data in or out of the data warehouse. You can find comparisons of cloud data warehouses such as Snowflake vs. Redshift or Snowflake vs BigQuery.
• Transform With SQL: Define data transformations as a standard SQL. Lyftrondata pushes down SQL to data sources and the cloud.
• Use Any BI Tool: Connect any BI tool to data sources or replicated tables. All sources and data pipelines are accessible from a single ODBC/JDBC/ADO.NET driver.
• Real-time data replication: Lyftrondata enables near real-time data replication on the fly. Treat yourself with our simple and eliminate complexity.
• Prototype Data Sets: Avoid needless delay in data preparation. Define virtual data sets before starting a data pipeline.

The Best Use Case For Lyftrondata Data Integration Is As follows:

Lyftrondata supports around 300+ integrations including ServiceNow integration. It offers no code/low code automation using the ANSI SQL data pipeline.

b. Hevo Data

As the ability of businesses to collect data explodes, data teams have a crucial role to play in fueling data-driven decisions. Broken pipelines, data quality issues, bugs and errors, and lack of control and visibility over the data flow make data integration a nightmare.

1000+ data teams rely on Anthon their Data Pipeline Platform to integrate data from over 150+ sources in a matter of minutes.

Key Features Of Hevo Data

• Near Real-Time Replication: Get access to near real-time replication on All Plans. Near Real-time via pipeline prioritization for Database Sources. For SaaS Sources, near real-time replication depends on API call limits.
• In-built Transformations: Format your data on the fly with Hevo’s preload transformations using either the drag-and-drop interface or our nifty python interface. Generate analysis-ready data in your warehouse using Hevo’s Postload Transformation.
• Monitoring and Observability: Monitor pipeline health with intuitive dashboards that reveal every stat of pipeline and data flow. Bring real-time visibility into your ETL with Alerts and Activity Logs.
• Reliability at Scale: With Hevo, you get a world-class fault-tolerant architecture that scales with zero data loss and low latency.
• 24×7 Customer Support: With Hevo you get more than just a platform, you get a partner for your pipelines. Discover peace with round-the-clock “Live Chat” within the platform.

c. Talend

Talend is an ETL solution that includes data quality, application integration, data management, Data Integration, data preparation, and big data, among other features. Talend has a large community because it is an open-source product and it’s the ideal venue for Talend users and community members to discuss their questions, queries, and experiences.

Key Features of Talend

• This is one of the most popular open-source Data Integration tools in the market today.
• Talend also has a wide array of services for advanced Data Integration, Data Management, Data Quality, and more. However, the one we are referring to here is Talend Open Studio.
• Talend lets businesses build a setup for both on-premise and cloud ETL jobs using Spark, Hadoop, and NoSQL Databases.

The Best-Suited Use Case for Talend

If you are fine with compromising on real-time data availability but are trying to optimize on the cost front – an open-source batch data migration application like Talend should be on your radar.

d. Dell Boomi

Dell Boomi is a cloud-based integration tool from Dell. With a visual designer and a variety of pre-configured components, it enables businesses to effortlessly integrate applications, partners, and customers via the web. Boomi can perform a variety of exciting tasks for organizations of all sizes.

It has all the tools you need to develop and maintain integrations between two or more endpoints.

Key Features of Dell Boomi

• This tool supports several application integrations as a service and is used by both SMBs and large companies.
• Dell Boomi offers different integration and data management capabilities ranging from private-cloud, on-premise, and public cloud endpoint connectors up to ETL support.
• The tool allows businesses to manage Data Integration in a central place via a unified reporting portal.

The Best-Suited Use Case for Dell Boomi

Dell Boomi data integration tool facilitates you to fetch and load data using hybrid IT architectures.

e. Informatica Power Center

Informatica PowerCenter Data integration tool offers services like master data management, data virtualization, ETL, data masking, etc. It enables you to fetch data from numerous sources and also facilitates services like data analysis and data processing.

Key Features of Informatica PowerCenter

• Favors in fetching data from all platforms: Informatica PowerCenter Integration tools also offer services like data migration and data validation.
• Proficient for large-scale business firms: Informatica PowerCenter is highly popular amongst large-size businesses and its upscaling is exponential.
• Enables different data management functions: Informatica PowerCenter data integration tool enables you to run a comprehensive journal of data like data cleansing, cloud data integration, and master data management, etc.

The Best Use Case For The Informatica PowerCenter Data Integration Tool Is:

Informatica PowerCenter enables you to deal with large amounts of on-premises legacy data sources.

FAQs

Q. What is a data integration model?
Ans. Data integration modeling is a technique that takes into account the types of models needed based on the types of architectural requirements for data integration and the types of models needed based on the Systems Development Life Cycle.

Q. Is SQL an ETL tool?
Ans. The SQL Server ETL (Extraction, Transformation, and Loading) process is especially useful when there is no consistency in the data coming from the source systems.

Q. Why is integrating data so important to the business firm?
Ans. It allows business firms to combine data residing in different sources to provide real-time business data moving and analysis.

Q. What are the four methods used in application integration?
Ans. Application integration has four standard levels:

• presentation-level integration
• business process integration
• data integration
• communications-level integration

About the Author:

Ali is a technical writer who loves to cover online data security insights and create reviews for users to buy software and IoT devices. He’s contributed five years of experience in technical writing and sharing security related how-to guides with readers worldwide.