All that data you collect for your business should make a big difference to your business through smarter decisions and impactful actions. However, outdated, labor-intensive integration processes are still a big hurdle in getting value from data. So, how can you overcome the bottlenecks and deliver analytics-ready data to all areas of your business in real time?
With more data pouring in from more sources than ever before, modern-day businesses need their siloed data, stored in disparate sources, to be easily accessible in a centralized data repository.
With data integration, you can combine information from several apps into a single unified source. When you implement the right data integration methods, you can streamline workflows, automate to easily access and extract valuable insights, and stay on top of your operations in real time to make better decisions faster.
In this blog, you’ll discover popular data integration techniques and technologies. Gain practical insights to create a robust data integration strategy and harness the full potential of your data to drive strategic decision-making.
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When your business needs to process data spread across disparate internal and external sources, you need to choose modern data integration techniques that efficiently cater to your unique business requirements. Depending on the complexity, disparity, and number of data sources to be integrated, you can choose from any of the following types of data integration methods.
With this method, you can formulate your data integration methods using hand-coding and custom code to organize and integrate siloed data. This technique is an excellent alternative for businesses that only need to integrate information from a few source systems and are rarely required to replicate data from apps to a target source.
The downside of this approach is that it needs human intervention, which often leaves it susceptible to errors. Also, this approach can get challenging if you want to add more data sources later and scale up.
This manual data integration approach that provides complete control and flexibility to manage your data at your ease and convenience. However, it is advisable to be careful about data quality and precision because errors in your code can result in substantial problems later.
This method ensures seamless data sharing and integration between different software applications. You can use web services, message queues, or APIs (application programming interfaces) to transfer directly from one app to another. The key advantage of integrating applications is that it helps eliminate data silos and guarantees all the apps access the same current and pertinent data. This ensures that your software tools collaborate flawlessly, enhancing your overall productivity.
This is a popular technique for integrating data from different systems. It involves using middleware applications that act as intermediaries between systems, translating and routing data as needed. Some of the standard middleware data integration types are,
This approach combines information from different sources to create a single data hub that data analysts can utilize for analytics and reporting. It also functions as a data supplier for subsequent applications. What sets this approach apart from other data integration types is its ability to swiftly collect data from diverse sources and seamlessly transfer it to the data repository. Minimizing this data latency makes the information fresher and more relevant for business intelligence and analytics tasks. In simple terms, you can access up-to-date information more quickly, essential for making well-informed decisions.
This data integration technique, also called data virtualization, creates a single virtual database with a unified data model from different data sets with different models. Data federation differs from real-time data integration, which continuously combines data into a centralized storage. Instead, in the case of Data Federation, when users request specific information, a federated virtual database delivers data on demand.
This data integration method involves moving data from a central enterprise data warehouse to various data marts, with necessary transformations being applied. As the data in the warehouse is continuously updated, these changes are disseminated to the original data mart either synchronously or asynchronously. Enterprise application integration (EAI) and enterprise data replication (EDI) are the two common data integration strategies used for data dissemination.
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The race toward cloud adoption has today resulted in the dispersion of systems across on-premises, hybrid, and cloud-based environments. Data integration emerges as a strategic solution to bridge these diverse systems, enabling businesses to analyze their data proficiently.
Determining the appropriate strategy for any given business entails a comprehensive understanding of the intricacies involved in system integration. A manual approach might suffice if the task involves integrating only a limited number of systems.
Conversely, enterprises needing to integrate disparate systems should opt for a multifaceted integration strategy. To provide clarity, we have described the optimal scenarios for each of these approaches. Here are some examples of different data integration approaches and when to use them:
|Data Integration Approach||When to Use It|
|Manual Data Integration||To combine data from a few sources for simple analysis.|
|Middleware Data Integration||To automate and translate communication between old and new systems.|
|Application-Based Integration||To automate and translate communication between systems, enabling more complex analysis.|
|Common Storage Integration||To present the data consistently, create and store a copy, and to perform the most sophisticated analysis tasks.|
|Data Propagation||To distribute data updates or changes across multiple systems or locations in near real-time|
|Data Federation||To access and query data from disparate sources without the need to physically move or consolidate the data|
|Data Consolidation||To bring together and centralize data from various sources into a single repository for comprehensive analysis|
You also need to consider the following aspects when choosing a data integration strategy.
Let’s explore the most popular and preferred data integration technologies commonly employed in businesses:
ETL is the go-to data integration technology for organizations worldwide. It covers everything from data extraction to transformation and loading into a data warehouse. Whether you need batch ETL for handling large data volumes or want incremental loading with near-real-time replication using Change Data Capture (CDC), ETL can help fulfill your needs. ETL also allows for various transformations like data cleansing, quality checks, aggregation, and reconciliation. Your engineering team can create a custom solution for one-time data replication or a limited number of data sources.
It offers on-demand data access to create a virtual layer or business view of relevant data sources. This provides business users with a simple interface to query data while the backend manages multiple connections to diverse sources with varying formats, interfaces, and semantics. Unlike traditional batch ETL, EII excels at handling real-time data integration, enabling business users to access updated data for analysis and reporting.
EDR functions as a near-real-time data consolidation strategy. It allows you to replicate complex data from various sources and load it into target destinations at near-real-time intervals or regular intervals. Unlike ETL, EDR does not involve data transformation or manipulation but focuses on data movement.
Analytics and reporting platforms offer straightforward data access for business intelligence. They come with built-in connections to common data sources, enabling quick data visualization through dashboards, reports, charts, and various formats. However, you may not always find the custom integration or reporting capabilities you require.
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A US-based fleet management enterprise running a diverse fleet of vehicles turned to Rishabh Software to streamline its data processing and reporting.
Learn more about how our Talend data integration efforts enabled the client to generate actionable business insights with real-time reports.
A product based MNC sought automation to streamline their extensive data processing needs, overcome data management challenges, and eliminate redundant and inaccurate data. Their primary goal was to obtain accurate sales reports and revenue calculations based on sales KPIs.
Our big data team orchestrated a Talend data integration solution, seamlessly integrating it with the client’s existing systems. We followed the concept of chained architecture to simplify functionality and facilitate the consolidation of outputs, involving the creation of 85+ Talend jobs. Here’s how we tackled it:
A dedicated team of 15+ big data engineers, including a testing and integration team, ensured the quality and accuracy of the output.
Learn more about how our Talend-based big data integration solution empowered the customer to achieve accurate and efficient global sales reporting.
Whether you need consulting, implementation or managed services for, we have hands-on expertise and experience to enable analytics at scale.
A: Data integration connects different data sources with different formats to enable unified analysis. Businesses often collect data from multiple sources, which results in data quality issues and redundancy. Breaking down data silos provides context and perspective, making the data more valuable. Effective data integration saves time, reduces errors, enhances data quality, and delivers valuable insights for analysis.
A: The ideal data integration approach would depend on your unique business and user needs. A two-tiered approach, combining staging and data warehouse layers, offers flexibility based on user needs and business requirements.
A: To implement a data integration solution that aligns with business goals, follow these best practices:
By following these best practices, your data integration initiative will ensure easy access for enhanced business operations.