Big Data is a broad term for large and complex datasets where traditional data processing applications are inadequate. The integration of this huge data sets is quite complex. There are several challenges one can face during this integration such as analysis, data curation, capture, sharing, search, visualization, information privacy and storage. The core elements of the big data platform is to handle the data in new ways as compared to the traditional relational database. Accuracy in managing big data will lead to more confident decision making. In this article, we discuss the integration of big data and six challenges that can be faced during the process.
Six Challenges in Big Data Integration:
The handling of big data is very complex. Some challenges faced during its integration include uncertainty of data Management, big data talent gap, getting data into a big data structure, syncing across data sources, getting useful information out of the big data, volume, skill availability, solution cost etc.
1. The Uncertainty of Data Management: One disruptive facet of big data management is the use of a wide range of innovative data management tools and frameworks whose designs are dedicated to supporting operational and analytical processing. The NoSQL (not only SQL) frameworks are used that differentiate it from traditional relational database management systems and are also largely designed to fulfill performance demands of big data applications such as managing a large amount of data and quick response times. There are a variety of NoSQL approaches such as hierarchical object representation (such as JSON, XML and BSON) and the concept of a key-value storage. The wide range of NoSQL tools, developers and the status of the market are creating uncertainty with the data management.
2. Talent Gap in Big Data: It is difficult to win the respect from media and analysts in tech without being bombarded with content touting the value of the analysis of big data and corresponding reliance on a wide range of disruptive technologies. The new tools evolved in this sector can range from traditional relational database tools with some alternative data layouts designed to maximize access speed while reducing the storage footprints, NoSQL data management frameworks, in-memory analytics, and as well as the broad Hadoop ecosystem. The reality is that there is a lack of skills available in the market for big data technologies. The typical expert has also gained experience through tool implementation and its use as a programming model, apart from the big data management aspects.
3. Getting Data into Big Data Structure: It might be obvious that the intent of a big data management involves analyzing and processing a large amount of data. There are many people who have raised expectations considering analyzing huge data sets for a big data platform. They also may not be aware of the complexity behind the transmission, access, and delivery of data and information from a wide range of resources and then loading these data into a big data platform. The intricate aspects of data transmission, access and loading are only part of the challenge. The requirement to navigate transformation and extraction is not limited to conventional relational data sets.
4. Syncing Across Data Sources: Once you import data into big data platforms you may also realize that data copies migrated from a wide range of sources on different rates and schedules can rapidly get out of the synchronization with the originating system. This implies that the data coming from one source is not out of date as compared to the data coming from another source. It also means the commonality of data definitions, concepts, metadata and the like. The traditional data management and data warehouses, the sequence of data transformation, extraction and migrations all arise the situation in which there are risks for data to become unsynchronized.
5. Extracting Information from the Data in Big Data Integration: The most practical use cases for big data involve the availability of data, augmenting existing storage of data as well as allowing access to end-user employing business intelligence tools for the purpose of the discovery of data. This business intelligence must be able to connect different big data platforms and also provide transparency of the data consumers to eliminate the requirement of custom coding. At the same time, if the number of data consumers grow, then one can provide a need to support an increasing collection of many simultaneous user accesses. This increment of demand may also spike at any time in reaction to different aspects of business process cycles. It also becomes a challenge in big data integration to ensure the right-time data availability to the data consumers.
6. Miscellaneous Challenges: Other challenges may occur while integrating big data. Some of the challenges include integration of data, skill availability, solution cost, the volume of data, the rate of transformation of data, veracity and validity of data. The ability to merge data that is not similar in source or structure and to do so at a reasonable cost and in time. It is also a challenge to process a large amount of data at a reasonable speed so that information is available for data consumers when they need it. The validation of data set is also fulfilled while transferring data from one source to another or to consumers as well.
This is all about the big data integration and some challenges that one can face during the implementation. These points must be considered and should be taken care of if you are going to manage any big data platform.