Spencer Nguyen - October 7, 2022
So much relies on data these days that it might be safe to say that most companies can’t thrive without it. Yet, gathering data is just the start — using it to make better company decisions or improve business processes is the end goal. Unfortunately, collecting large quantities of data for your business might seem futile if that data remains cut off from other systems. As such, it has become vital for developers and decision-makers to unify data for practical use. You can consistently compile relevant information from multiple systems through the power of data integration.
Understanding Data Integration
Data integration features a multi-level process that focuses on data at rest and combines information from disparate sources to form a unified view. Data managers begin by sourcing information through ingestion by obtaining and importing data for immediate use or storage within a database. Systems may ingest data through methods such as batch processing or micro batching.
Essentially, the user submits a data request to a designated server that pulls and collates the information from different systems. The master server extracts the significant details from each source before presenting them in data sets for clarity and optimization. In turn, master data management with the integrated process provides a consistent record of information for optimizing business and operational decision-making processes.
Unpacking the Techniques and Steps in Data Integration
Data integration involves a sum of parts, each refined and streamlined for quality business analysis. It is essential to check that each stage runs smoothly to provide your organization with consistent real-time data for accurate monitoring.
ETL
Extract, transform, and load (ETL) refers to a three-part process critical for the success of your data integration solutions. ETL begins with extracting essential information from a data source and transforming it into a format best suited for analytics. Many engineers consider structured query language (SQL) as one of the most popular methods for transforming and accessing information through data integration.
The final step of ETL involves sending converted data to a secure storage repository such as a data warehouse or lake for storage and future retrieval. Many businesses preserve the contents of a data warehouse or lake for the long-term management of business intelligence and holistic analytic reports.
ELT
Consider extract, load, and transform (ELT) as an advancement of standard ETL solutions, where administrators refine data for improved performance. ELT functions similarly to ETL, except for an increased data processing capability that directly leverages the processing power of source systems during transformation.
Data Quality
You might implement data quality as a standalone solution or as a part of the transformation process. Data quality provides a holding area for data as the system standardizes values, removes duplication, and implements an overall clean-up before finalized loading.
Change Data Capture
Change data capture provides your systems with real-time updates of changes within your integrated data stores, such as data lakes and data warehousing processes.
Data Mapping
Data mapping helps your data systems connect the dots by informing apps about the exact location and attributes of data during transformation. For instance, your application or ETL process might employ data mapping to match the fields from a source system to the same format and order requested by a destination system.
Data Replication
The data replication technique creates multiple copies of data and distributes them across different locations for optimized backup practices. You may store replicated data across environments — e.g., cloud-based and on-premise systems. Most leading data integration solutions, such as Microsoft SQL, offer out-of-the-box replication capabilities.
Data Streaming
Unlike most other integration processes, data streaming moves continuously in real time across source and destination systems. Data streaming has become more prevalent with cloud systems, APIs, data warehouses, and data lakes that utilize instantaneous analytics in data management.
Effective Use Cases of Data Integration
Data integration solutions offer myriad advantages depending on your project goals and priorities, with benefits varying according to the scale of your organizational network.
Optimizing Business Intelligence Decisions
Your organization can optimize business intelligence processes by accessing and applying unified data formats with data integration. Specifically, your team can look forward to collecting and interpreting data sets for actionable insights without sifting through large quantities of information across multiple sources.
Powering Data Lakes and Warehouses
Data integration technology supports comprehensive data warehouses and lakes, combining data from disparate sources to form a relational database.
Relational data stores with high-level information present all users with a uniform and accessible view of data for guiding critical business decisions. For example, quality data warehouses and lakes empower enterprise teams to consistently run queries, perform in-depth analytics, and generate detailed reports with updated information.
Additionally, an integrated data virtualization of data lakes and warehouses makes it possible to organize unstructured information, catalyzing technologies such as machine learning and big data initiatives. Ultimately, data virtualization eliminates the tedious requirement of loading data to new repositories for viewing purposes.
Leveraging the Advantages of Data Integration
Data integration allows you to conveniently refer to specific information and apply the right analytics tools to drive informed business decisions. While smaller organizations may not see an immediate priority in data integration, they may discover a growing need for adoption as they scale and expand.
An early setup of a data integration solution can help facilitate a smooth data transition as a company grows, preventing the inefficiencies and risks of data loss associated with poor management systems.
Optimize System Efficiency
Data integration streamlines data management to replace ineffective and tedious methods of manually evaluating and preparing individual information. An integrated data warehouse lets your team skip the lengthy process of forming data connections when engaged in complex projects such as application-building, which enhances your business processes.
You can expect quality data analytics with an integrated approach, where a centralized system scans and polishes data for maximum operational efficiency and predictive capabilities. Data integration helps you support queries in large-scale data sets, including customer data management.
Achieve Comprehensive Data Management
An integrated data infrastructure facilitates frictionless and reliable business data management across critical processes, such as KPI monitoring, financial risk metrics, regulatory compliance, and CRMs. The seamless data engineering process guides decision-makers toward improving business functions and raising overall customer experiences.
Improve Collaborations
Integrated data systems promote speedy and accessible connections across data stores. You can look forward to uninterrupted transfers between systems for data integrity and quality. Additionally, your data administrators can achieve more robust collaborations by eliminating data silos.
Minimize Data Errors
A data integration solution ensures that information remains updated across your systems. Specifically, you can avoid the risks of data inaccuracies by maintaining complete data sets.
A synchronized data integration platform with automated updates informs each authorized user about the latest changes in the operational system, keeping every collaborator on the same page without causing miscommunication.
Integrated data storage and management help you reduce IT costs while raising data quality. Additionally, a unified view of data instantly draws your attention to the business aspects that require priority while maintaining a steady flow of information throughout business areas.
Common Challenges for Data Integration Systems
While data integration brings many benefits, its complex processing components and tangle of data sources could pose challenges that require careful planning. These may include:
Facing challenges in internal and external data collation: You might face issues acquiring a consistent level of detail for external resources compared to internal data. These may include complications in vendor communications that result in delays and disruptions in data sharing.
Transitioning from legacy infrastructures: Accessing and transferring data from legacy systems may prove unwieldy and time-consuming without proper data integration tools.
Maintaining best practices: Setting up a data integration system is merely the first step in the overall process. Teams may actively collaborate with service providers to ensure that unified data systems continue running smoothly based on the latest industry and organizational standards.
Adapting to evolving business demands: The modern age presents new data sources that require customized adjustments to existing data integration infrastructures. These changes may include sourcing data from emerging sources like IoT devices, cloud management, and advanced sensors that require specific formatting.
Resolve Data Integration Woes with DreamFactory
DreamFactory’s advanced SaaS solution can help you significantly reduce operational costs while making the most out of your developmental hours. Our instant SQL server APIs run smoothly across private, public, and hybrid cloud infrastructures, making them suitable for all types of data integration environments.
With DreamFactory’s automation solutions, you can confidently make the leap from legacy systems (i.e., SOAP) to transformative REST API systems, boosting your data integration initiatives for optimal project outcomes. Visit DreamFactory for a 14-day trial in our hosted environment to optimize your data integration processes for quality results.
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As a seasoned content moderator with a keen eye for detail and a passion for upholding the highest standards of quality and integrity in all of their work, Spencer Nguyen brings a professional yet empathetic approach to every task.