Data integration is the process of consolidating data from various source systems to a target system where the business can have a holistic view of what’s happening to the organisation. To enable data integration in an organisation we need data integration tools that can perform the data integration task. With advancement in technology many tools are now available for various data integration use-cases ranging from big-data integration, batch and real-time integration, on-premise and cloud based tools, Open source and proprietary. Data integration tools are key in designing and development of data pipelines in an organisation. In this post we will discuss about factors to consider when selecting an ideal data integration tool and look at common data integration tools available.

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Factors to Consider When Selecting Data Integration Tools

Different data integration tools have different capabilities and the choice of a particular tool depends on the organisation/business datalandscape and business use-case. Below are general factors to consider when selecting an ideal data integration tool.

  1. Supported Data Sources. Pulling data from source system to target system is a key functionality of data integration tool. Different data integration tools support different data sources. While most tools support common data sources like major relational databases (Oracle, Microsoft SQL Server database, IBM DB2, MySQL and PostgreSQL among others), it’s important to consider new data sources such as those for unstructured data sources. An ideal data integration tool should support different and numerous data sources and have the capability of being extended to new future data sources. A good data integration tool grows with the organisation data strategy.
  2. Data Transformation Capabilities. Data transformation is at the core of an ideal data integration tool. A good data integration tool comes with many data transformation functions. In addition to providing numerous data transformation function it should be able to allow for adding custom transformation function.
  3. Scalability. Organisation data keeps growing hence the data integration tool should be able to scale with the growth in data. In this era of big data organisations are looking for a robust data integration tool that can big data for analytical purpose.
  4. Real time Integration. Many organisations want to leverage the power of analytics at real-time in order to make decisions faster and avert risk in time. Having tool that support both batch and real-time data integration and processing provides more value to the organisation.
  5. Security. For the integration of sensitive data organisation look for secure data integration tool.
  6. Cost. The choice of a data integration tool can be influenced by the cost of acquiring the tool or the skilled personnel required to maintain the data pipeline. The aspect of cost brings the perspective of choosing between open-source vs proprietary tools. This can be influenced by the ease of use of the tool. In addition to the cost factor different data integration architectures such as ELT and ETL requires different resources setting up the tool. ETL requires additional staging environment for data transformation while ELT does not hence making it cheaper.

Proprietary Data Integration Tools

There are tons of data integration tools in the market and the choice of the tool largely depends on the use-case and influenced by the above factors. Below are few common data integration tools to consider;

  1. Oracle Data Integrator. Oracle Data Integrator (ODI) is an enterprise-grade robust, secure, and scalable Extract, Load and Transform (ELT) data integration tool owned by Oracle. It supports diverse data sources ranging from simple flat files, big data, streaming data integration and other complex data sources through custom data adaptors. It Comes with numerous data transformation functions and gives ability to add custom functions.
  2. SQL Server Integration Services. SQL Server Integration Services is a platform for building enterprise-level data integration and data transformations solutions. SSIS is a Microsoft based Extract, Transform and Load data integration tool. SSIS comes with a wide range of data transformation functions and supports various data sources. It allows creation of custom extensions to perform different functionalities.
  3. IBM DataStage. IBM DataStage is a highly scalable data integration tool for designing, developing and running jobs that move and transform data on premises and in the cloud. IBM DataStage provides Extract, Transform and Load process.
  4. Informatica PowerCenter. Informatica PowerCenter provides Extract, Load (ETL) and Transform data integration tool from Informatica Company. It provides an enterprise level tool that enable connectivity to diverse data sources advanced data transformation and real time data integration capabilities.
  5. Pentaho. Pentaho Data Integration (PDI) is an Extract, Transform and Load tool. It provides a graphical user interface where user creates the ETL jobs. It enables the operationalisation of R, Python, Scala & Weka Machine learning models.

Open Source Data Integration Tools

  1. Talend. Talend is an open source robust Extract, Transform and Load data integration tool. It comes with connectors to different sources including but not limited to relational databases, Hadoop, Spark and NoSQL databases.
  2. Apache Airflow. Airflow is a platform created by the community to programmatically author, schedule and monitor workflows as directed acyclic graphs (DAGs). It’s a scalable tool for orchestrating Python based ETL jobs. It’s an open source tool enabling the user to customize any component to suit the business use-case. It offers support for diverse integrations including but not limited to; Oracle, Microsoft SQL Server (MSSQL), Google Cloud SQL, AWS Lambda, Amazon EMR, Druid, Google BigQuery, Spark, MySQL etc.
  3. Apache NiFi. Apache NiFi is a popular Open Source ETL tool written in java. Apache NiFi supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic. It provides a web-based interface for designing and monitoring of ETL jobs.
  4. Apache Kafka. Apache Kafka is an open-source distributed event streaming platform for executing high-performant data pipelines, streaming analytics, data integration, and mission-critical applications. It connect to tons of event sources and event sinks including Postgres, JMS, Elasticsearch, AWS S3 etc. It allows real-time data transformation and processing. Apache Kafka is the prime candidate for real-time workloads and analytics.
  5. Hevo Data. Hevo Data is a no-code tool that integrates data from different sources such as relational databases, SaaS application and streaming services to target systems. It comes with diverse connectors to different data sources and is easy to use.

Conclusion

Data Integration Tools plays a key role for implementing data infrastructure of an organisation. They enables the consolidating data from heterogeneous sources to the single target system. We have seen how various factors affects the choice of the data integration tool including supported data sources, data transformation capabilities, scalability, security and most important cost. We have listed few of the most commonly used 5 proprietary data integration tools and 5 Open Source tools. These tools are used to prepare data and load it to a central location (Data Warehouse) where business users and machine learning users can utilise. In the next post we will look at Introduction to Apache Airflow. You can visit our previous post to learn about data integration concepts.

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