Purpose Of Big Data Analytics – Big data is very popular, and for good reason. But understanding big data and how to analyze it is still not clear. In fact, this word means more than the amount of information created. Big data refers not only to ever-growing volumes of enormous data in various formats, but also to the processes, tools, and approaches used to extract insights from that data. And this is the most important thing: big data analysis helps companies solve business problems that cannot be solved using traditional approaches and tools.
This post provides a complete picture of what big data analytics is and how it works. In addition, we introduce popular tools for big data analysis and existing use cases.
Purpose Of Big Data Analytics
Before we get into a detailed explanation of big data analytics, let’s first define what big data is and what it does, briefly.
What Is Big Data Analytics?
Big data is a term that describes large collections of heterogeneous data – structured, unstructured and semi-structured – that are constantly being generated at high speeds and in high volumes. More and more companies are using this data to provide meaningful insights and improve decision making, but cannot store and process it using traditional data storage and processing tools.
Knowing the key features, it can be understood that not all information can be taken for big data.
Big data analysis is the process of discovering patterns, trends, and relationships in huge data sets that cannot be detected by traditional data processing methods and tools.
The best way to understand the idea of big data analytics is to compare it to conventional data analytics.
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Data from a variety of sources, including sensors, log files, and social media, can be used in addition to existing marketing data for both individual and multi-organization companies. Additionally, not only business users and analysts can use this data for advanced analytics, but data science teams can use big data to build predictive machine learning projects.
You can check out our post on the Analytics Maturity Model where we explain the above types in more detail. For now, let’s move on to explaining the processes behind big data analytics and what tools can do it all.
The concept of big data is not new: it is the culmination of decades of software and hardware development that have enabled businesses to handle vast amounts of complex data. In addition, new technologies for storing and processing large data are constantly being developed, which means that computer engineers can find better ways to integrate and use this data.
Big Data Analytics involves the process of collecting, processing, filtering/cleaning and analyzing huge data sets so that organizations can develop, develop and produce better products. Let’s look at these processes in more detail.
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The process of identifying sources and then obtaining big data varies from company to company. However, it should be noted that data collection usually occurs in real-time or real-time to ensure prompt processing. Modern technologies allow collecting both structured (mostly data in tabular formats) and unstructured data (all types of data formats) from various sources such as websites, mobile applications, databases, flat files, customer relationship management (CRM), IoT. . Sensors and etc.
Raw data must go through processes of extraction, transformation, and loading, so ETL or ELT data pipelines are created to deliver data from sources to central repositories for further storage and processing. In the ETL approach, data transformation occurs before it reaches the target storage, such as a data warehouse, whereas ELT allows data to be transformed after it is loaded into the target system.
Depending on the complexity of the data, it can be moved to storage such as cloud storage or to data lakes where business intelligence tools can access it as needed. There are very few modern cloud solutions, which typically include storage, computing, and client infrastructure components. Storage tiers allow data from different sources to be divided into partitions for further optimization and compression. Computational layers are sets of processing engines used to perform any mathematical operation on data. There are also client layers where all data processing activities are performed.
Once the data is stored, it needs to be transformed into a more digestible form in order to obtain actionable results for analytical searches. There are different data processing options for this. The choice of the right approach can depend on the company’s data processing and analysis functions as well as available resources.
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According to the requirements for using one or more machines, the process areas are centralized and divided into:
Before an in-depth analysis, data – whether small or large – must be properly cleaned to ensure the best quality and provide accurate results. In short, the data cleaning process ensures that the data is useful and necessary for analysis by cleaning any errors, duplications, inconsistencies, duplications, bad formats, etc. Any irrelevant or incorrect data should be removed or taken into account. Several data quality tools can detect and clean up any errors in data sets.
That’s when big data becomes valuable insight that, among other things, can stimulate companies’ growth and competitiveness. There are many methods and practices for understanding large amounts of data. We have listed some of them below.
It is important to note that there is no universal tool or technology that can be used to work with big data analysis. In most cases, you need to integrate multiple solutions to collect, process and analyze data. We tell you about the key players who deserve attention.
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Apache Hadoop is an open source software package developed in 2006 by the Apache Software Foundation for storing, processing and managing large data.
As you can see, the Hadoop ecosystem consists of many components. The three most important ones include Hadoop Distributed File System (HDFS), Hadoop MapReduce, and Hadoop YARN.
NoSQL databases, also known as non-relational or non-tabular databases, use different data models to access and manipulate data. The “NoSQL” part here stands for “not SQL” and “not just SQL”. Unlike traditional relational databases that store all data in tables and use SQL (Structured Query Language) syntax, NoSQL databases use different methods to store data. Depending on the type of database, there may be storage options such as columns, documents, key-value pairs, and graphs. With flexible schemas and excellent scalability, NoSQL databases are well-suited for large amounts of raw unstructured data and high user loads. Below are some examples of such databases.
Spark is another software in the Apache family that manages large amounts of heterogeneous data in a distributed manner, either as a stand-alone tool or in combination with other data tools. It basically does the same job as MapReduce. One of the key players in distributed big data processing, Apache Spark is developer-friendly because it provides connections to the most popular programming languages used in data analysis, such as R and Python. Spark supports machine learning (MLlib), SQL, graph processing (GraphX).
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Talend is an open source data integration and management platform that enables users to work with data on their own. Talend is considered one of the most effective and user-friendly data integration tools for big data.
Kafka is an open-source, extensible, fault-tolerant software platform for gathering large amounts of data from multiple sources. The platform is specifically designed by the Apache Software Foundation to process data in real-time at high speeds. Kafka has an event-driven architecture, which means that the system does not need to search for new information as it responds to events as they occur in real time. In this way, the complexity of big data will be more manageable.
Most organizations today work with big data, but few know what to do with it and how to make it work to their advantage. Below are real examples of companies using Big Data.
Startup Ginger.io, founded by researchers at the Massachusetts Institute of Technology, uses machine learning and big data generated by smartphones to remotely predict symptoms for patients with mental health problems.
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Ginger.io is a mobile app that not only provides real-time communication with professional therapists and coaches, but also allows therapists to collect and analyze vast amounts of patient behavior data for more effective treatment. The application monitors and collects information such as the frequency of messages and phone calls, sleep patterns and sports activities, which can indicate a person’s mental health. For example, when people experience depressive episodes, they often isolate themselves from other people, call and write less. Conversely, increased phone calls and text messages may be a sign of a manic episode in patients with bipolar disorder.
It’s no surprise that e-commerce and technology giant Amazon collects and analyzes tons of data about each of its customers.
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