Big Data Analysis: A Comprehensive Tutorial for Beginners

What is Big Data Analysis?

Big data analytics describes the process of uncovering trends, patterns, and correlations in large amounts of raw data to help make data-informed decisions. These processes use familiar statistical analysis techniques—like clustering and regression—and apply them to more extensive datasets with the help of newer tools.

How big data analytics works?

Big data analytics refers to collecting, processing, cleaning, and analyzing large datasets to help organizations operationalize their big data.

1. Collect Data

Data collection looks different for every organization. With today’s technology, organizations can gather both structured and unstructured data from a variety of sources — from cloud storage to mobile applications to in-store IoT sensors and beyond. Some data will be stored in data warehouses where business intelligence tools and solutions can access it easily. Raw or unstructured data that is too diverse or complex for a warehouse may be assigned metadata and stored in a data lake.

2. Process Data

Once data is collected and stored, it must be organized properly to get accurate results on analytical queries, especially when it’s large and unstructured. Available data is growing exponentially, making data processing a challenge for organizations. One processing option is batch processing, which looks at large data blocks over time. Batch processing is useful when there is a longer turnaround time between collecting and analyzing data. Stream processing looks at small batches of data at once, shortening the delay time between collection and analysis for quicker decision-making. Stream processing is more complex and often more expensive.

3. Clean Data

Data big or small requires scrubbing to improve data quality and get stronger results; all data must be formatted correctly, and any duplicative or irrelevant data must be eliminated or accounted for. Dirty data can obscure and mislead, creating flawed insights.

4. Analyze Data

Getting big data into a usable state takes time. Once it’s ready, advanced analytics processes can turn big data into big insights. Some of these big data analysis methods include:

Data mining sorts through large datasets to identify patterns and relationships by identifying anomalies and creating data clusters.
Predictive analytics uses an organization’s historical data to make predictions about the future, identifying upcoming risks and opportunities.
Deep learning imitates human learning patterns by using artificial intelligence and machine learning to layer algorithms and find patterns in the most complex and abstract data.

Big data analytics tools and technology

Big data analytics cannot be narrowed down to a single tool or technology. Instead, several types of tools work together to help you collect, process, cleanse, and analyze big data. Some of the major players in big data ecosystems are listed below.

Hadoop is an open-source framework that efficiently stores and processes big datasets on clusters of commodity hardware. This framework is free and can handle large amounts of structured and unstructured data, making it a valuable mainstay for any big data operation.
NoSQL databases are non-relational data management systems that do not require a fixed scheme, making them a great option for big, raw, unstructured data. NoSQL stands for “not only SQL,” and these databases can handle a variety of data models.
MapReduce is an essential component to the Hadoop framework serving two functions. The first is mapping, which filters data to various nodes within the cluster. The second is reducing, which organizes and reduces the results from each node to answer a query.
YARN stands for “Yet Another Resource Negotiator.” It is another component of second-generation Hadoop. The cluster management technology helps with job scheduling and resource management in the cluster.
Spark is an open source cluster computing framework that uses implicit data parallelism and fault tolerance to provide an interface for programming entire clusters. Spark can handle both batch and stream processing for fast computation.
Tableau is an end-to-end data analytics platform that allows you to prep, analyze, collaborate, and share your big data insights. Tableau excels in self-service visual analysis, allowing people to ask new questions of governed big data and easily share those insights across the organization.