The expression Big Data refers to data sets that are so large in volume and so complex that the software and traditional computer architectures are not able to capture, manage and process them in a reasonable time.
If a traditional database can manage tables perhaps composed of millions of rows, but on tens or a few hundred columns, big data requires tools that can manage the same number of records, but with thousands of columns.
In addition, often the data are not even available in a structured form, easily pigeonholed in rows and columns, but are present in the form of documents, metadata, geographical locations, values detected by IoT sensors, and numerous other forms, from semi-structured to completely deconstructed.
The quantity and complexity that make it possible to define a set of data as “Big Data” is a debated topic. Many take the petabyte (1,000 terabytes) as a threshold, and several projects operate in the field of exabytes (1,000 petabytes). However, considering only the size of the database is considered by many to be a mistake that can be misleading for companies that, although they do not have such large archives, can still benefit from the use of big data technologies and approaches, for example for extract value from unstructured data, or that must be processed in a very short time (an approach sometimes called “Little Data” ).
We, therefore, tend to define the contours of a Big Data project by analyzing it for three different aspects, which are referred to as the “three Vs of Big Data” :
- The Volume of Data
- The great variety in data types
- The speed with which the data must be acquired or analyzed
The data that make up Big Data archives can come from heterogeneous sources, such as navigation data from websites, social media, desktop, and mobile applications, scientific experiments, and – increasingly – sensors and Internet of Things devices.
The concept of Big Data brings with it several elements and components that allow companies and organizations to leverage data to solve numerous business problems practically. The different components to consider are:
- IT infrastructure for Big Data;
- The organization and storage structure of the data ;
- The analytical tools for Big Data;
- The skills techniques and mathematical;
- Last but not least, a real business case in which Big Data can bring value.
What allows us to extract a useful value from the data are the interpretative analyzes that can be applied to it. Without analytics, it is just data that is worthless and indeed carries a significant storage cost.
By applying analysis methods and tools to data, companies can find benefits such as increased sales, better customer satisfaction, greater efficiency, and more generally an increase in competitiveness.
The analytical practice involves examining data sets, deriving homogeneous groups from them to obtain useful information otherwise hidden, and drawing conclusions and forecasts about future activities. By analyzing the data, companies can make more informed business decisions, for example about when and where to run a certain marketing campaign, or identify a need that can be met by a new product or service.
Big Data analyzes can be done with generic business intelligence applications, or with more specific tools, even developed ad hoc using programming languages. Among the most advanced methods of analytics, we find data mining, where analysts process large sets of data to identify relationships, patterns, and trends.
A widely used technique involves making an initial exploratory analysis, perhaps on a reduced set of data, to identify patterns and relationships between the data, and then perform a confirmation analysis to verify whether the assumptions extracted from the first analysis are verified. In fact.
Another major distinction is that between quantitative data analysis (or analysis of numerical data expressing quantifiable values), and qualitative analysis, which focuses on non-numerical data, such as images, videos, sounds, and unstructured text.
The IT infrastructure for Big Data
For a Big Data project to be successful, companies need to dedicate an adequate and often very specific infrastructure to this workload, capable of collecting, storing, and processing data to present it in a useful form. All while ensuring the security of information while it is stored and in transit
At the highest level, this includes storage systems and servers designed for big data, software frameworks, databases, tools, analytics software, and integrations between big data and other applications. Very often, this infrastructure is present on-premises, or in any case in the form of hardware machines located in a remote data center. Cloud and virtualization, rightly considered two practical and efficient IT architectures, are often not the best choice for dealing with Big Data, especially as regards the data processing phase.
Among the techniques used to speed up analytical processing on Big Data, there are the use of in-memory databases and accelerated graphics cards (GPUs), which must continuously exchange data with the disks. It is easy to guess that if the computer and memory component is far from the storage component, the network connection will be at the expense. In cloud and virtualized environments, the volume of data and the required processing speed risk generating bottlenecks in the networking component.
For this reason, we tend to prefer an architecture composed of clusters of numerous physical servers, even at low cost but equipped with a lot of RAM, one or more GPUs, and fast hard drives, all centralized on the same motherboard. All are combined with software tools designed to divide the workload on the individual servers that make up the clusters.
The general rule has its exceptions: batch processes that do not need real-time responses, and that perhaps need to be performed only occasionally (for example financial reports, account statements, or billing of service performed monthly) can be performed profitably. on a cloud service that is turned on only for the hours or days required for processing, and then turned off to reduce costs.
Even simple data collection can lead to complexity and obstacles. While some data is static and always available, such as data from files, logs, and social media, others must be collected at high speed and immediately recorded without delay, which can pose challenges in terms of storage and connectivity performance. Examples of dynamic data that must be acquired in “streaming” mode include signals collected by sensors, economic and financial transactions, and all data generated by the proliferation of IoT sensors.
The increasing penetration of Internet of Things solutions, with companies adding sensors and connectivity to all sorts of products, from gadgets to cars, is growing a new generation of Big Data solutions specifically designed for the IoT world.
Among the storage options most used in Big Data are traditional data warehouses, data lakes, and cloud storage.
Data warehouse
The traditional systems on which business applications record their data, from ERP to CRM, can be one of the sources from which Big Data applications draw information.
Data lakes
Data lakes are repositories of information capable of holding extremely large volumes of data in their native format, at least until the time when it is necessary to carry out processing and obtain information for business applications. In that case, and only at that point, the Big Data systems will take care of extracting the information requested from that data. The Internet of Things and digital transformation initiatives, with the collection of insights into individual customers, are increasingly fueling data lakes.
Cloud Storage for Big Data
More and more business data is stored in the cloud, sometimes in object-storage mode, and it is often necessary to feed this data into Big Data applications.
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Specific Technologies For Big Data
In addition to the generic IT infrastructure just described, some specific technologies are essential to the success of any Big Data project.
The Hadoop Ecosystem
The Hadoop software library, an open-source project of the Apache Foundation, is a framework that allows the management of large data sets, structured and unstructured, and their distributed processing on clusters of computers using very simple programming models. It is designed to scale from a single server up to thousands, each made up of the compute and storage components.
The framework includes several modules:
Hadoop Common
The basic utilities that support other Hadoop modules
Hadoop Distributed File System
Provides high-speed access to structured and unstructured data. It allows you to “mount” any data source reachable with a URL.
Hadoop YARN
A framework for job scheduling and cluster resource management
Hadoop MapReduce
A YARN-based system for parallel processing of large data sets
Apache Spark
Also part of the Hadoop ecosystem, Apache Spark is an open-source framework for clustered computing that serves as the engine for managing Big Data in the context of Hadoop. Spark has become one of the leading frameworks of this type and can be used in many different ways. It offers native links to various programming languages such as Java, Scala, Python (especially the Python Anaconda distribution), and R, and supports SQL, data streaming, machine learning, and graph database processing.
NoSQL Databases
Traditional SQL databases are designed for reliable transactions and to answer ad-hoc queries on well-structured data. However, this rigidity represents an obstacle for some types of applications. NoSQL databases overcome these obstacles, storing and managing data in ways that allow great flexibility and operational speed. Unlike traditional relational databases, many NoSQL databases can scale horizontally across hundreds or thousands of servers. Here you can find the SQL injection cheat sheet.
In-Memory Database
An in-memory database (IMDB, not to be confused with the Internet Movie DataBase) is a DBMS that primarily uses RAM, and not a hard disk, to store data. This allows a much higher execution speed, which makes real-time analytics applications on Big Data possible, otherwise unthinkable.
Skills For Big Data
The technical, theoretical, and practical difficulties for the design and execution of Big Data applications require specific skills, which are not always present in the IT departments of companies that have been trained on technologies different from today’s ones.
Many of these skills relate to specific Big Data tools, such as Hadoop, Spark, NoSQL, in-memory databases, and analytical software. Other skills are related to disciplines such as data science, statistics, data mining, quantitative analysis, data visualization, programming in general and for specific languages (Python, R, Scala), data structuring, and algorithms.
For a Big Data project to be successful, managerial skills are also required, particularly in the areas of resource planning and planning, and account management, which risks growing out of control as the volume of data grows.
Nowadays, many of the figures we have indicated in the previous lines are among the most requested in the market. If you have a degree in mathematics or statistics but lack computer skills, now is the right time to fill them with courses and training specific to Big Data. There are huge job opportunities.
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Use Cases For Big Data
Big Data can be used to solve numerous business problems or to open up new opportunities. Here are some examples.
Customer Analytics
Companies can analyze consumer behavior from a multichannel marketing perspective to improve customer experience, increase conversion rates, collateral sales, offer services and increase loyalty.
Operational Analytics
Improving operational performance and making better use of corporate assets is the goal of many organizations. Big data can help businesses find new ways to operate more efficiently.
Fraud And Crime Prevention
Companies and governments can identify suspicious activity by recognizing patterns that may indicate fraudulent behavior, preventing its occurrence, or identifying the culprit.
Price Optimization
Businesses can use data to optimize prices for products and services, expanding their market or increasing revenues.