In this post, we’ll take a look at what data integration is and why it’s important. We’ll also see some top data integration tools and how they work. We’ll learn key features to help assess the tools, supplemented by definitions of the various functions they’re expected to perform.
What is Data Integration?
Data integration is the process of consolidating data from different sources, providing a unified view, and enabling seamless data flow across various systems and platforms. It’s essential for organizations looking to harness data from diverse sources for analytics, business intelligence, and operational efficiency.
Key Features of Data Integration
Before looking at specific tools, here’s a summary of the key features they must support:
Data Sources
Simply put, integration involves extracting data from various sources such as databases, cloud services, applications, flat files, and more. Data integration tools must be able to connect to different data sources using connectors or APIs to fetch data. You can schedule the extraction process, or events in real time may trigger it. The tool must be capable of adding new input sources and increasing data volumes.
Data Integration Types
There are several ways you can integrate data. One of the most common is the ETL (extract, transform, load) process, a fundamental process in data integration. Others include ELT (extract, load, transform), where you load data in raw form before transforming it in some way. The various key concepts that tools need to support include:
- Extract—retrieving data from different sources
- Transformation—converting data into a suitable format or structure for the target system
- Load—inserting the transformed data into a target database or data warehouse
- Data quality—ensuring the accuracy, consistency, and reliability of data
- Schema mapping—aligning data from different sources to a common schema or data model
Other key concepts include:
- Scalability—Data integration is a moveable feast. Over time, other sources and input formats will be required, as will changes in data volumes. Therefore, a tool must be scaleable to different data sources using connectors or APIs to fetch data and detect changes in data volumes that are processed and stored.
- Real-time data processing—In some situations, integration must happen in real time as source data changes. A tool must be capable of monitoring input sources, detecting changes, and loading them, either immediately or according to a pre-defined schedule.
- Data quality and cleansing—It goes without saying that we must integrate data to high levels of quality—especially accuracy. A tool must support features to validate data form and content.
- Data security—Data must be held and processed with maximum security. While many tools and techniques lie outside data integration, an integration tool must protect data during the integration process itself and support encryption, access controls, and secure transmission protocols.
Criteria for Choosing the Right Tool
Having decided that you want to use data integration, the next step is to decide on the best tool. With a variety of tools available, organizations can choose the one that best fits their specific needs, whether they require batch processing, real-time data synchronization, cloud integration, or comprehensive data management. The selection process is pretty much standard.
The first stage is to define what you expect of your data integration implementation. You can narrow down this vital step into three levels: must have, nice to have, and wouldn’t it be great. Then, you can assess each tool against these criteria. The one you choose must meet all of the “must have” criteria and as many of the others as possible. You may find it easier to use a numeric ranking and scoring system.
Budget and operational considerations also come into play.
To help the selection process here are examples of market-leading tools to consider.
Workato
Workato is a powerful automation platform that enables businesses to integrate their applications and automate workflows seamlessly. It offers a range of features, including the ability to create “recipes,” which are sets of commands that automate tasks across different software applications based on specific triggers. The platform’s effectiveness lies in its robust API management capabilities, which include advanced security, efficient routing, and comprehensive monitoring tools. Additionally, Workato’s API governance ensures secure and compliant API usage, while its API gateway supports various authentication methods to maintain secure access.
Oracle Data Integrator
Oracle Data Integrator is a comprehensive data integration platform that covers all data integration requirements: from high-volume, high-performance batch loads, to event-driven, trickle-feed integration processes, to SOA-enabled data services.
Oracle Data Integrator (ODI) 12c, the latest version of Oracle’s strategic Data Integration offering, provides superior developer productivity and improved user experience with a redesigned flow-based declarative user interface and deeper integration with Oracle GoldenGate.
ODI12c further builds on its flexible and high-performance architecture with comprehensive big data support and added parallelism when executing data integration processes. It includes interoperability with Oracle Warehouse Builder (OWB) for a quick and simple migration for OWB customers to ODI12c. Additionally, ODI can be monitored from a single solution along with other Oracle technologies and applications through the integration with Oracle Enterprise Manager 12c
Informatica PowerCenter
PowerCenter provides an environment that allows you to load data into a centralized location, such as a data warehouse or operational data store (ODS). You can extract data from multiple sources, transform the data according to the business logic you build in the client application, and load the transformed data into file and relational targets. PowerCenter also provides the ability to view and analyze business information and browse and analyze metadata from disparate metadata repositories.
Talend
Talend is an open-source ETL tool that provides a wide range of connectors and components for data integration. It offers data quality, big data, and cloud integration solutions.
The tool is a low-code platform that combines data integration, data quality, and data governance in a single solution. It supports various data sources and architectures, and offers partnerships and integrations with top technology providers.
Microsoft Azure Data Factory—Integration Service
Azure Data Factory is a managed cloud service that’s built for complex hybrid extract-transform-load (ETL), extract-load-transform (ELT), and data integration projects offering hybrid data integration at an enterprise scale. It offers a visual interface with more than 90 built-in, maintenance-free connectors. It can deliver integrated data to Azure Synapse analytics.
Microsoft SSIS (SQL Server Integration Services)
Microsoft SQL Server Integration Services is a component of the Microsoft SQL Server database software that can be used to perform a broad range of data migration tasks. SSIS is a platform for data integration and workflow applications. It features a data warehousing tool used for data extraction, transformation, and loading.
Apache Nifi
Apache NiFi is a software platform that automates data pipelines and distribution for various use cases. It offers data provenance tracking, extensive configuration, low latency, dynamic prioritization, a browser-based interface, and secure communication.
It’s designed to automate the flow of data between software systems. Leveraging the concept of extract, transform, load, it’s based on the NiagaraFiles software previously developed by the U.S. National Security Agency, which is also the source of a part of its present name, NiFi.
It was open source as a part of NSA’s technology transfer program in 2014.
IBM InfoSphere DataStage
IBM InfoSphere DataStage is an ETL and ELT tool that is part of the IBM Information Platforms Solutions suite and IBM InfoSphere. It uses a graphical notation to construct data integration solutions and is available in various versions such as the Server Edition, the Enterprise Edition, and the MVS Edition. It uses a client-server architecture.
Conclusion
At the end of the day, data integration tools are crucial for managing and utilizing data effectively in today’s data-driven world. They streamline the process of collecting, transforming, and loading data from multiple sources, ensuring that organizations can leverage accurate and consistent data for decision-making and operational purposes.
This post was written by Iain Robertson. Iain operates as a freelance IT specialist through his own company. He provides onsite and remote global interim, contract and temporary support as a senior executive in general and ICT management.
