Integrating large data can be complex because it involves data from different sources.

In the current scenario, companies use data and analytics. Pandemic-induced rapid digital change has emphasized the importance of big data for the growth of the company. Big data analytics today is a core business solution that can drive growth, agility, competitive advantage and give a 360-degree view of a company. Businesses take advantage big data analytics get useful insights and improve the customer experience.

What is Big Data Integration?

According to the term, big data integration is the process by which data is collected from different sources, combined, and processed to obtain valuable insights. This integration process is not simple because it sounds because it involves huge structured, unstructured, and semi-structured data sets. These data sets must also be stored data warehouses so that they can be retrieved later. The usual data integration process was based on extracting, converting, and uploading clean data to repositories. However, this cannot be used in the case of big data because it comes from heterogeneous sources. The four important characteristics of large data are quantity, speed, variability, and truthfulness. These characters make it challenging to integrate big data into business processes.

What are the challenges?

1. Different data formats and sources

Because large amounts of data are collected from a variety of sources, it can have heterogeneous forms and structures. Sorting them out of this complex step can be difficult. Data sets are extracted from a variety of applications and platforms, such as marketing applications, CRM, customer service teams, and more.

2. Connecting platforms and improving accessibility

Business intelligence tools used to identify and sort data should be able to combine different big data platforms. The growing number of data consumers may become a challenge in big data integration. The company needs to respond to growing demand and make data available to users in real time, which is becoming more difficult.

3. Data processing speed

The current business scenario requires real-time information insights and can pose a challenge to big data integration. Great date has been extracted from heterogeneous media and thus requires time to process and gain insights. Working with complex data structures makes it impossible to analyze them simultaneously.

4. Select the correct data management framework

There are various data management frameworks commonly referred to as a class called NoSQL. Different NoSQL approaches use different paradigms, including the key value storage concept that allows them to connect to data set entities. There are several NoSQL approaches that are said to evolve and have scalability and performance. The existence of such a wide range of tools makes information management systems uncertain. Choosing these information management landscapes according to the specific needs of the company can become a challenge.

5. Synchronization of data from different sources

Once the data has been extracted from different sources, it must be synchronized with the original system. Because they are from a variety of sources, by the time the data set is integrated, one may be left out of the sync schedule and thus call it old. This leads to variations in concepts of community, such as data definitions. Thus, the integration of large data causes variations in data management, decompression, and conversion problems in the synchronization of data sources.

6. Security challenges

Because large data is of great importance to the company and its users, ensuring security in the integration of large data should not be overlooked. Integrating large data has many security challenges because data sources are not always known and can also cause breaches. The integration of big data and its secure storage should be a priority.

7. Demand for experts

With the accelerated adoption of large data and analytics, the demand for talent in this field has been strong. The lack of analysts and data engineers can become a potential challenge to the big data integration process.

Companies planning to adopt big data integration should be aware of these major challenges. With the right data cleansing and processing, as well as new innovations such as data virtualization, big data integration can be less complex and more efficient.

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