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Integrating Big Data Can Be A Challenge Featured

Integrating Big Data Can Be A Challenge John Barkiple

Big data integration is a critical step in any Big Data project. However, some challenges and issues must be taken into account while integrating data. With the growing number of data consumers, big data integration can become a problem that any company needs to respond to. Although it may sound easy, big data integration is not simple as it sounds because large data sets that are structured, unstructured and semi-structured are involved. All these diverse data sets are to be stored in a data warehouse for later retrieval. Some of the challenges encountered during data integration include uncertainty in the management of data, synchronization across data sources, availability of skills and getting the right insights. Despite these challenges, managing integrated big data makes decision-making accurate and ensures the decisions arrived at are insightful.

Big data integration tools

As big data continue being appreciated across different industries, the tools for integrating big data should continue being reevaluated to identify their abilities to process ever-increasing unstructured data. Data integration technologies should have a common platform that supports data quality and profiling.

Big data integration challenges

  1. Finding the personnel

With the rising adoption of big data, data scientists and analysts continue to be in high demand. There is a lack of individuals to fill the vacant positions in the big data research industry. While a typical big data expert must have experience with various big data integration tools and an understanding of data organization, coming across such people is never easy.

  1. Extracting data

The process of bringing in data that come from different sources is a massive challenge that needs to be addressed appropriately. With the many sources and diversity of data, the skills required to navigate the process of extraction are needed to analyze and process it to help in decision-making.

  1. Synchronizing data from different sources

After data from different sources has been extracted, it must be synchronized. This data uses different schedules and rates and can be desynchronized from the source. Synchronization provides consistency in systems while continually updating. With the traditional data management systems, extracting data migrating and transforming it promotes desynchronization. Therefore, synchronizing it will minimize variations in data.

  1. Choosing the right strategy

Big data integration mostly starts with the need for information to be shared. This can be followed by the interest in breaking down the existing data silos to allow data to be analyzed. The biggest challenge for many businesses is that they often jump from one project to another without laying down an organizational plan. Therefore, a true data integration plan must be developed complete with security and compliance to meet the goals that can sometimes be difficult to achieve.

  1. Security issues

Data is a new goldmine, and hackers know this quite well. Therefore, companies and data users must always ensure that big data integration is secure. Sadly, most organizations do not understand the sensitivity of data and the security challenges. Securing data can also face problems because the data sources are diverse, and data breaches can occur. Therefore, integrating data and storing them safely needs to be a key priority.

  1. Demand for skilled analysts

With the rising adoption of big data and analytics across industries, there has been a rising demand for top big data and analytics professionals across the globe. The scarcity of analysts and data engineers who are the key drivers of big data projects have made big data integration difficult. Therefore, companies that intend to deploy data integration must be aware of these key challenges and try as much as they can to address them for success in their projects.

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Scott Koegler

Scott Koegler is Executive Editor for Big Data & Analytics Tech Brief

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