Features & Performance of Data Integration tools – On Premise Vs Cloud

By May 7, 2020 May 25th, 2021 Blogs

Data integration is a method of merging technical and business rules used to incorporate data from different sources into relevant and valuable information. For valuable data, it needs to gather data from one/multiple sources or systems [E], organizes it together [T], and centralize it to a single repository [L]. Extract Transform Load summons plays a key role in data integration strategies, and sometimes, organizations simply need to extract data from a platform for other uses as well.

Why ETL Tools are Required for Data Integration:

ETL tools make it much easier to transfer and manage data. Without them, developers will have to be specialists in the area from which they use information. In addition, developers will need to hand-code all of that material. Data mitigation can consume significant cost, generate hours’ worth of additional work for developers who need to update the computations continuously.

Another reason to use ETL software is the fact that it can be extremely confusing and time-consuming to create an individual data pipeline as the amount of information contained in a database increase.

  • Scalability: ETL tools allow for easy scaling and the amount of information needed for its sustainability.
  • In-House: Alternate option to an ETL is for an organization to build its data copy pipeline, but when the amount of information in a database grows, this may be troublesome. Pipelines need to have continuous updates and changes to accommodate data sets that are continually increasing over time while also changing the processing of the information. This method will take hours and a lot to human error. ETL tools are perfect for overcoming this challenge as they save companies money and time.
  • Efficiency: Constant maintenance is not required, and ETL tools will improve overall productivity.

Traditional ETL Tool Design (On-Premise):

Tradition ETL tools followed a three-tier architecture, and all these layers will be part of a single package and present in the organization premise.

  1. The design interface for the user
  2. The Metadata repository
  3. The Processing Layer

On-Premise ETL Processes:

  • Raw data is extracted from a range of sources and sometimes placed in a destination like a data lake or data warehouse.
  • The transformation state is where all business rules and regulations are implemented to achieve standardization, unique, verification sorting, or any other custom tasks per requirement.
  • Load this extracted transformed data into a target destination by executing a task from the command line or interface.


  • On-Premise ETL tool has a wide range of support for databases and compatible with all mainstream DBMS.
  • Easy to develop ETL mappings and workflows.
  • Metadata manager is available, which has workflow run stats, folder/mapping/session details.
  • It processes large volumes of data without any disturbances.
  • We have a lot of options to choose from established ETL products in the market, to choose the tool for enterprise needs.


  • Complexity: Managing supporting redoing is complex.
  • Cost: Irrespective of tool usage, organizations need to pay for a license every year. However, maintenance costs depend on what you want, how long you need it. It is the same thing with support. If you need support, you must pay for it.
  • Too many interfaces: The various interfaces in ETL tools are not ideal for the user experience; this may confuse new developers.
  • Incompatible with AI: Not compatible to integrate with Artificial Intelligence.
  • Aligning with existing systems: Premise ETL tool has a client and required additional software that needs to be installed on machines.

ETL Tool Design in Cloud:

Data integration tools for data management are critical to leveraging increasing volumes of data. Such approaches have vied increasingly by organizations as essential for data acquisition, governance, and overall data management. It is only natural that enterprises are relying on integration tools, considering the pace with which data currently moves. Such initiatives have put data-driven businesses in a difficult situation. They have to decide whether they should continue with legacy offerings, which are struggling to meet demand, having served them well in the past, or go with forward-thinking self-service integration approaches that allow the many advantages of the Cloud.

Many cloud-based tools are integration platforms as services (iPaaS) that help integrate data from multiple sources. These services are usually web-based user interface, rapid, simple, and flexible capabilities for cloud services and third-party applications to integrate with enterprise applications. Unlike traditional ETL tools allowing complex low-level integration, this offers easy tooling for integration. Real-time integration can be done with the help of a Cloud-based ETL tool.

Pros of Cloud ETL Service:

  • Security and consistency: Cloud integration tools are secure and consistent in performance.
  • Cost and Time Saving: Cloud integration can eliminate the pressure on a company to maintain and update its own systems so that it can spend its time, money, and energy in concentrating on its core business strategies.
  • Subscription model pricing which includes operating expense, and does not require capital expenditure.
  • Easy-to-use user interface: There is an interface that is intuitive and very easy to use.
  • No software development required as necessary connectors should already be available.
  • Low maintenance: Developers do not maintain, deploy, or run the platform themselves.
  • Seamless updates: Cloud-based data integration tools have an advantage to seamless updates without any downtime.

Cons of Cloud ETL Service:

  • Narrow range of functionality: Cloud integration does not have all functionalities (Data Profiling, Data Quality tool, etc.) similar to the on-premise ETL tool.
  • No Metadata manager: It does not have a Metadata manager.

Thousands of user organizations have moved to Cloud data integration, and Gartner estimates that over 50,000 organizations used some form of iPaaS in the past 24 months. This trend is slated to continue for the next two to three years. Below are the leaders in the Enterprise Integration Platform as a Service. The evaluation criteria are Product, Service, Overall Viability, Pricing, Market Responsiveness, and Customer Experience.

Magic Quadrant for Enterprise Integration Platform as a Service (Source: Gartner)

Due to the overall advantages of Cloud-based data integration tools, many enterprises are migrating from Legacy systems to Cloud tools for ETL/ELT process. Moving from on-premise to the Cloud makes a lot of sense, especially for small and medium-size data-driven organizations, as such a move will reduce the cost and make real-time data available to end-users. Though a few cons exist, however, these are negligible when compared to the offered beneficial outcomes.

Lakshminarayana Sade

Lakshminarayana Sade

Lakshmi narayana working as a Senior Consultant at Saama Analytics with 9+ years of experience in BI Tools (ETL & Reporting), Data Warehousing and RPA