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Choosing the right data integration tools The need for pulling together growing volumes of data generated and collected by organizations has increased. Several types of data integration tools are available in the market. It has become a challenging task for IT leaders and CIOs to choose from so many products available to them.

• By Naveen Joshi • 6:54 am

The growing importance of data analytics and BI applications in taking strategic business decision has made data integration’s role crucial in an enterprise. From collecting data, transforming it into useful insights and delivering it to the users require effective data integration tools.

How to select right data integration tools?

Enterprise Size

While choosing a right data integration tool, you must evaluate the size and annual revenue of your company. The optimum utilization of an integration tool differs according to the enterprise size. There are three types of enterprise sizes:

Large Enterprise

A large enterprise consists of a diverse set of source systems which overlap with a high data volume. They require integrating structured data sources as well the non-structured data from sources like social media, web server logs, and flat files. The IT budget of a large enterprise is sufficient to purchase any of the available data integration tools and supporting infrastructure as necessary. Thus, a large enterprise must create a dedicated IT group consisting of data integration specialists to choose data integration tools and platforms. IBM’s InfoSphere Information Server for Data Integration is an example of a data integration tool that is suitable for the large enterprise.

Midsize Enterprise

A midsize enterprise consists of a variety of source systems that handle overlapping data subjects. This data can be on-premise or cloud-based. A midsize enterprise requires integrating dominant structured resources and any unstructured data that need to be integrated into a limited scope. The IT budgets of such enterprises are constrained. Thus, midsize enterprises having significant integration needs, but operating with constrained resources in regards to people, budget and time should consider data integration tools from Microsoft, Oracle, or Pentaho. The tools by these companies provide the capability of addressing the data variety, scope of integration uses and resource constraints typically found in such organizations.

Small Enterprise

A small enterprise consists of a variety of source systems with primarily structured data sources. Their IT budgets are limited. These enterprises must consider data integration tools based on their existing databases or they can use tools from Talend or Pentaho. Smaller enterprises must always ensure that their IT department has sufficient expertise to leverage these tools effectively.

Integration Use Cases

An enterprise must determine the reasons for which they need data integration. While determining this reason, several factors are needed to be considered, such as:

  • Is data integration required for analytics?
  • What about application consolidation?
  • How to synchronize data between on-premise systems and cloud applications?
  • Does data need to be captured and delivered for complex event processing or stream processing applications?

Hence, enterprises are suggested that they prepare a data integration use case to address these questions. The data integration use case is broken down into these subcategories, which enable data to be:

  • Integrated into a data warehouse or other analytical data store

In this use case, data is loaded into an enterprise data warehouse (EDW) using ETL.

  • Integrated into a BI data store

In this case, the primary integration task is to transform data sets from an EDW for use by specific business groups or areas of analysis. These data sets are then loaded into a special-purpose data store, such as a general store, an online analytical processing (OLAP) cube or columnar database. Data from other sources may also need to be added to enrich the information.

Data integration architectures are continuously shifting from physical bulk/batch movements to virtualized and real-time data delivery. Thus, data and analytics leaders are required to intertwine integration styles to match all requirements of business changes.

Data and analytics leaders must constantly focus on:

  • Understanding the data integration market landscape
  • Evaluating and selecting data integration tools that best suit their current and future use-case requirements
  • Monitoring the strengths and weaknesses of data integration tool that vendors against their use case requirements on the escalating diversity and complexity of internal and external data.

I hope that this analysis will prove helpful to you in selecting the right data integration tool.

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