Big Data Integration: 5 Best Practices and Examples

Big data is no longer a buzzword. It represents the reality of an amount of data so immense that traditional data processing methods simply can’t manage it. More than 328.77 exabytes (1 exabyte equals 1 billion gigabytes) of data are created each day, on average. Organizations worldwide depend on these vast oceans of information to uncover deep insights that drive strategic decision-making across all industries. Big data has transformed how business operates—optimizing processes, predicting trends, and enhancing customer experiences—and is a fundamental factor in decision-making for nearly every aspect of business.

This article will help you understand big data integration, why it’s important, and what the challenges are. We’ll also share some best practices and strategies to help smooth the process of integrating big data sources into information you can actually use.

What is big data integration?

Big data integration involves handling data volumes several orders of magnitude greater than what traditional data integration entails, but the concept is similar: You’re combining several different sources of big data—like social media, sensors, mobile devices, and transactional applications—to create a unified, comprehensive, and actionable view.

A few years ago, big data was defined by three ‘V’s: volume, variety, and velocity. Today, however, more metrics have been added, including veracity, value, and variability. Another aspect of big data to consider is visualization. While it’s not officially one of the ‘V’s, it probably should be. Big or small, data needs to be visualized in one way or another for it to be useful.

It’s not just the size that makes big data integration what it is. Compared to traditional data integration, there are significant differences:

• Volume is the obvious distinguishing factor. The amount of data that is integrated extends into petabytes (PBs) and even exabytes (EBs) rather than gigabytes (GBs) and terabytes (TBs) for smaller collections of data.
• Variety in data types and formats is also a differentiator, with big data integration including IoT (Internet of Things) devices, social media, online games, and video streams, to name a few. This data is often unstructured, like text and images, or semi-structured, like XML and JSON files.
• Velocity is the speed at which data is generated, processed, and analyzed. Big data integration needs to be capable of processing data in real-time or near-real-time, while traditional data integration is slower and often used in batch processing.
• Complexity is another important factor. Because the amount of data is so vast, big data integration needs complex data processing to accomplish advanced analytics. It’s just not possible to process it with more orthodox methods.
• Tools and technology used to manage big data integration are different, as well. While relational databases and conventional tools are suitable for managing smaller amounts of data, more advanced technologies are needed for big data, like NoSQL databases and data lakes.
• Data quality and governance, while important for any size data source, is exponentially more so with big data integration, exactly because of the differences described above.

Big data integration helps bring order to the chaos of massive, complex data sets to make it accessible and useful, regardless of where it is.

Big data integration process

The integration process isn’t much different from conventional data integration. You can apply the basic concept of ETL (extract, transform, load) processes, but since we’re talking about big data here, it’s a lot more sophisticated. Before you extract a single byte of data, your first step should be planning:

• Define the overarching purpose of what you want to achieve with the data that aligns with your business goals.
• Identify the source of the big data you need and select the appropriate technology you want to use.
• Establish strong data governance policies to ensure compliance, security, privacy, and data quality.
• Make sure that the infrastructure can scale to accommodate increasing volumes and complexity.
• Adopt the right tools and training to analyze the data to be sure you’re getting the most out of your big data integration.

Then you can get on with the actual process:

• Extract the big data from sources like social media, IoT devices, and organizational databases.
• Clean and prepare the data by removing errors, duplicates, and irrelevancies to improve quality and consistency.
• Transform the big data into a suitable format or structure for analysis, which may include normalization and aggregation.
• Load the big data from your sources into a cohesive set using techniques like ETL or data virtualization.
• Store the integrated data in a scalable and secure environment, such as data lakes or cloud storage systems.
• Analyze the integrated data using the appropriate tools and algorithms to extract insights and patterns.
• Visualize the analyzed data in your tool(s) of choice.

The importance of big data integration

Today’s modern organizations depend on data to keep their operations running smoothly. Big data integration reaps incredible benefits for data-driven organizations and can directly impact operations, strategies, and growth. Consolidating diverse data sources into a unified, accessible format ensures a comprehensive view of information, which is vital for effective analysis and informed decision-making.

Big data integration transforms raw data from vast sources into a consumable form that can be used to derive actionable intelligence, improve analytical accuracy, and gain deeper insights to support business intelligence, customer understanding, and operational efficiency.

The challenges of big data integration

The challenges of big data integration can be, well, big. As mentioned above, strategy determines success, and should be solidified before the work begins. Addressing these considerations will help smooth out the process of your big data integration project:

Experienced IT staff

Your IT team is likely responsible for creating and maintaining data lakes, data warehouses, and other sophisticated systems designed to store vast quantities of structured and unstructured data. They ensure that data sources are properly synced, which is a complex task given the diversity and volume of data involved. Their expertise is also vital in troubleshooting issues, optimizing performance, and ensuring that the systems are up to date with the latest technological advancements.

Tools and technologies

Traditional data integration tools may struggle with the demands of big data integration, which requires more advanced solutions. Big data integration often requires tools that can handle high volumes of data in varied formats, process data in real time, and manage the complexities of distributed systems. The right tools and technologies are critical to the success of your big data integration project, as they need to be both powerful and efficient to do the work without causing bottlenecks or data loss.

Capacity and scalability

Big data needs a lot of storage for initial extraction and housing, but it also needs to

allow expansion as data volume grows. Data requirements can increase rapidly, and the infrastructure must be able to keep up without causing disruptions or performance problems. This often requires a combination of on-premises and cloud-based solutions to balance cost, performance, and scalability needs.

Data quality and accuracy

The payoff of big data integration is the ability to use the data, so it must be trustworthy and safe. A lot of effort should be aimed at implementing stringent data validation, cleaning, and transformation processes. Big data integration efforts must also meet governance, security, and compliance requirements, ensuring that data is securely stored and accessed to maintain privacy and prevent data breaches.

5 best practices and strategies for big data integration

These challenges don’t need to get in the way of your big data integration project. Here are five best practices and strategies you can apply to ensure a safe, smooth, and synchronized process:

1. Implement solid data security and risk reduction practices

   Strong security protocols to protect data from unauthorized access and breaches are important for any amount of data, but the scale of the job makes it even more important. Security measures, like encryption, access controls, and regular security audits, are ever-present and must be rigorously applied. Risk reduction helps IT teams prepare for potential data losses or breaches with proactive measures such as regular backups, disaster recovery plans, and continuous monitoring for any security threats.

2. Perform regular and thorough data testing

   Frequent and comprehensive data testing, like validating data sources, testing integration processes, and verifying the accuracy of data transformations, ensures the integrity and reliability of big data systems. Consistent testing helps identify and resolve issues early, preventing data corruption and ensuring that the data is of high quality and reliable for decision-making.

3. Ensure ongoing data governance

   Effective data governance is essential for managing data assets efficiently and in compliance with regulations. Establish clear policies and procedures for data management, including data access, quality, privacy, and regulation compliance. Ongoing governance ensures consistent data handling across the organization, minimizes legal and regulatory risks, and helps maintain the overall quality of data.

4. Enable robust data quality controls

   Solid and consistent data quality controls are vital to maintain the accuracy, completeness, and consistency of big data. Implement processes that include continuous data cleaning, validation, and standardization. High-quality data ensures that organizations can rely on their data for accurate analytics and insights for informed strategic decision-making.

5. Maintain tool compatibility

   Organizations should regularly evaluate and update their tools and technologies to ensure they remain compatible with evolving data formats and integration requirements. This includes not only selecting the right tools for data collection, storage, and analysis but also making sure that the tools can interact with each other.

CData Sync: Connectivity without the headache

Big data doesn’t have to be a big problem. CData Sync provides robust low- and no-code tools to deliver comprehensive connectivity for any size integration project—on-premises and in the cloud. Easily access and analyze data using your favorite tools without overloading your IT team.