Big Data and Data Analytics
Putting Analytics to Work: Big Data and Data Analytics 



Big Data: The term Data Analytics is very vast. First of all Big data refers to the sets of data that are too large or complex to deal with a traditional data-processing method. In the present day, there are millions of data sources that generate the bulk of data at a very rapid rate. These sources of data are present across the world. Data with numerous fields offer greater statistical power, meanwhile, data with higher complexity may lead to higher false discovery rates too.

Big data analytics is the process used for examining data to gather meaningful information from a given data set such as hidden patterns, unknown correlations, market trends, and customer preferences which facilitates organizations make informed business decisions. Data analytics technologies give businesses a way to analyze data sets and gather new information.

Organizations use big data analytics to make data-driven decisions that improve business-related outcomes. They can use advanced analytics techniques such as data mining, text analytics, predictive analytics, machine learning, statistics, and natural language processing to gain new information from previously untapped data sources.

Data Analytics benefits may include more effective marketing, new revenue opportunities, customer personalization, and improved operational efficiency.


How big data analytics works?

Big data analytics collects, process, and analyze large data sets to help organization operations with their big data.


1) Collecting the Data

Data collection looks different for every organization. With different technology, they can gather both structured and unstructured data from various sources such as cloud storage, mobile applications, in-store IoT sensors, and many more. Some of the data will be stored in data warehouses, where business intelligence tools and solutions can be easily accessed on it. Raw data that is too complex for a warehouse may be assigned to metadata and stored in a data lake.


2) Processing the Data

Once the data is collected and stored, it must be processed properly to get accurate results especially when it is large and unstructured. One of the processing options is batch processing which looks at large data blocks. Batch processing is used when there is a longer turnaround time between collecting and analyzing data.

The other one is Stream processing. Stream processing looks at small batches of data at once. Stream processing is more complex and expensive as compared to Batch processing.

3) Cleaning the data

Big Data requires scrubbing to improve data quality and get meaningful and informational results. All data must be formatted correctly, and duplicate or irrelevant data should be eliminated. Unwanted data can obscure and mislead, creating or giving wrong data information.


4) Analyzing the Data

After filtering or cleaning raw data, advanced analytics processes can turn big data into useful information. Some of these big data analysis methods are:


Data mining sorts large data sets to identify patterns and relationships by identifying irregularities and creating data clusters.

Predictive analytics is used in an organization to analyze historical data to make predictions about the future to identify upcoming risks and opportunities in the future.

Deep learning follows human learning patterns by using artificial intelligence (AI) and machine learning to find patterns and algorithms in complex and abstract data.


Tools and Technology Used in Big Data Analytics


Hadoop is an open-source framework that uses distributed storage and parallel processing to store and manage big datasets on clusters of commodity hardware. This framework is free and can handle large amounts of structured and unstructured data, making it valuable for any big data operation. It is one of the most common software used by data analysts to handle big data.



Apache Spark is an open-source, distributed processing framework used for big data workloads. Spark utilizes in-memory caching and optimizes query execution for a faster query against data of any size. Spark is one of the fast and general engines used for large-scale data processing. Spark can handle both batch and stream processing for fast processing or calculation of data.



Tableau is a powerful end-to-end data visualization tool used in the Business Intelligence Industry that allows users to collect, analyze, collaborate, and share their big data analyzed information. Tableau helps to create data that can be easily understood by professionals at any level of an organization. Moreover, it allows non-technical background users to create a customized dashboard.



MapReduce is an essential component of the Hadoop framework which is used to perform the distributed processing in parallel in a Hadoop cluster that makes Hadoop work fast since when dealing with Big Data, serial processing is not in any use. The first one is mapping, which filters the data to various nodes within the cluster and the second one is reducing, which organizes and reduces the results from each node to reply to a query.



MongoDB is a NoSQL document Oriented database that stores JSON-like documents with dynamic schemas and then Data are stored in documents in the form of key-value. On the backend, MongoDB converts JSON data into a binary format which is known as Bison. MongoDB additionally provides provisions for nested data. Its approach is focused on integrating the important capabilities of relational databases with the innovations of non-relational databases like NoSQL.


Apache Drill

Apache drill is another distributed system tool that is used for interactive analysis of big data. It is more flexible to support many types of data formats, query languages, and data sources. It is specially designed to exploit nested data.

Moreover, it has an objective to scale up on ten thousand servers or more and reaches the capability to process trillions of data in seconds. Apache Drill uses HDFS architecture for the storage of data.


Challenges of Big Data Analytics


1) Data Accessibility

With a larger number of data, storage and processing becomes more complicated. Big data should be maintained and stored properly to ensure that it can be used by less experienced analysts and data scientists.


2) Security of Data

Securing these huge sets of data is one of the most daunting challenges of Big Data. As the amount of data increases, privacy and security concerns increases too. The complexity of big data systems presents different security challenges. Properly addressing security concerns within such a complicated big data ecosystem can be a complex process.


3) Maintaining quality of Data

As a higher number of data is continuously coming from a variety of sources and in different formats, data quality management of big data requires significant time, effort, and resources to maintain them properly. Nowadays, organizations are spending more time scrubbing duplicates, errors, absences, conflicts, and inconsistencies in data.



4) Choosing the right tools

New technologies are used for processing and analyzing big data. Organizations often get confused while selecting the tool for big data analysis and storage. Therefore, they must find the right technology that aligns with their needs and infrastructure to work within their established ecosystems and address their needs. Often, the right solution is also a flexible solution that can make future infrastructure changes in an organization.



In today’s technological world, big data is rapidly growing, and different tools and techniques are used to analyze the data to get valid information. Organizations use these techniques to manage and analyze their data. Along with the benefits, there are different set of challenges that comes with big data analysis. For better analysis of data, we must ensure proper accessibility of data with appropriate security and should maintain the quality of data, and most importantly must have good tools for big data analysis.

Using big data analytics assists in risk management, product development, and innovation, which analyzes how efficient the engine designs are and if there is any need for improvements on the product or infrastructure. It facilitates in better and quicker decision-making within organizations and further helps to improve the customer experience.