As the data across all the sectors are continuously growing, all the organizations are interacting a lot with their partners, customers, suppliers, and stakeholders. Companies collect data from IoT, social media and other sources, also, creating and collecting data is another option but managing it is difficult. In most of the cases, the data silos contain vast data that tamper the productivity of any business. This problem can be resolved by including data integration.
Data integration usually takes data from multiple sources and transforms it into a meaningful one, usable for data scientists, business executives, data analysts, and many others. The needs of sharing the voluminous and variety of data increases, using commercial data integration platforms help in managing and simplifying the process.
What is exactly a Data integration platform?
Combination of data into a single and unified view from the various different sources is data integration. The cost and success of complex IT initiatives, from data warehousing and data migration to real-time business intelligence and master data management is widely dependent on how data integration processes are implemented. A data integration platform eases and even accelerates the deployment of all the initiatives giving you high-performance tools for filtering, processing, moving and collecting different types of data from the data sources.
A data integration platform is nothing but a group of technologies that help you create data pipelines. Further, a data pipeline must have a data ingestion framework so that it can connect to different systems and applications, database replication software for converting and copying data and objects of a database into data transport architectures, a graphical process designer and other formats.
Ease of use, flexibility, scalability, and low-impact on the data are some of the prime features to look for in a data integration platform. In modern business, data integration services are one fit solution to all the use cases. It leverages big data, creates data lakes & warehouses and simplifies business intelligence.
Evolution of ETL tools into data integration platforms
Earlier data integration focused on the key ETL functions to load the data marts, EDWs and BI data stores such as the columnar databases and online analytical processing cubes. Further, all these requirements of ETL data integration have expanded and include the mentioned tasks:
- B2B integration
- big data integration
- application and business process integration
- cloud integration
- data consolidation
- data migration
- master data management
- data cleansing and quality
And because of the above functions, below mentioned integration classifications emerged, targeting the specific technologies and uses:
EAI (Enterprise application integration)
This subcategory is simply called application integration, it supports interoperability between different applications and is enabled via data services or web. It is created using the industry standards and service-oriented architectures which include electronic data interchange. The most common architectural approach for implementing EAI functionality is an enterprise service bus.
Big data integration
The focus of this technology loading the data into large data platforms such as Hadoop, NoSQL databases, Spark, etc. Each category (key value, wide column, document, and graph) in NoSQL database features different use cases and interface integrations which can be accommodated via the integration tools. In Hadoop data integration, the processes interface with several Hadoop distribution constituents like MapReduce, HBase, Hive, Spark, Hadoop Distributed File System, Pig, and Sqoop. Several processing engines such as Spark are also widely used, besides Hadoop, with the correlating integration needs.
EMS (Enterprise messaging system)
EMS focuses on providing messaging among different applications via the use of structured formats like JSON and XML. To provide effective real-time updates of data from different sources of data, EMS tools offer low weight integration services.
EII (Enterprise information integration)
Initially called data federation shows the dissimilar data sources virtually but has limited integration capacity. Currently used, the data virtualization software, provides data services and data abstraction layers to several sources including semi-structured, structured, and unstructured data.
This integration was introduced to provide interoperability in real-time between the database and the cloud-based application. Deployed as a cloud service, these tools also offer EMS and EAI functionality.
Eventually, the vendors now offer fully developed integration suites with all the capabilities to suit all the tools, they also support data integration both in real-time and traditional batch mode via web services. It handles cloud data, on-premise, and less structured data i.e. text, systems, and big data.
Myths about data integration tools
Right usage of the data integration platforms improves productivity, scalability, integration flexibility, and expandability over manual coding. Below are some of the misconceptions as to why IT professionals prefer writing code manually than using data integration platform.
- Integration tools are quite expensive
- Requires highly experienced and skilled resources
- Cost-free coding
Data integration platforms in the market today
Data integration platforms provided companies like IBM, Talend, Informatica, Oracle, Information Builders, SAS Institute Inc. and SAP are on top in the market today. There are many other vendors who offer a variety of data integration tools known as data preparation tools, they are geared towards the data scientists and data analysts.
All these products deploy on-premise but integrate the data residing on the cloud or on-prem. Hitachi Vantara’sPentaho and Talend platforms offer both open sources and paid enterprise genres of their products. Further, Microsoft bundles the product with its database instead of selling the product separately.
Primarily, data integration is an IT-centric process which is based on the database, data and necessary technical expertise. IT teams responsible for data warehouse and BI systems manage integration with master data management, data quality, and data management programs. Few leading enterprises have developed centers for integration competency to handle their integration platforms.