What is Big Data and Data Analytics?
Big Data and Data Analytics are two important concepts that express the processes of collecting, processing, analyzing large volumes of data from various sources and extracting meaningful information from these data. Here are detailed explanations of both concepts:
Big Data:
Big data describes large volumes and high diversity of data that cannot be processed effectively by traditional databases or data processing methods. This data is usually summarized as “3V”:
Volume: Big data typically refers to large amounts of data. These data can be large data volumes such as terabyte (TB), petabyte (PB), exabyte (EB).
Variety: Big data includes different types of data, which can include text, audio, images, video, social media data, sensor data and more.
Velocity: Big data includes data that is produced and needs to be processed quickly. For example, constant data streams from IoT devices.
Additionally, in some definitions, additional dimensions such as “value” and “veracity” can be included in the concept of big data.
Data Analytics:
Data analytics refers to the process of transforming big data into more meaningful and useful information. This process consists of the following stages:
Data Collection: The first step is to collect data. This involves pulling data from a variety of sources, these sources can include websites, sensors, databases, mobile applications and more.
Data Cleaning and Editing: Data cleaning and editing are performed to improve the quality of the collected data. This means correcting incomplete or inaccurate data and resolving inconsistencies.
Data Analysis: Cleaned data is analyzed. Using techniques such as statistical methods, data mining, machine learning and deep learning, data is examined and meaningful patterns, relationships and trends are revealed.
Data Visualization: Analysis results are visualized using graphs, tables, or visual tools. This supports easier understanding of data and decision-making processes.
Application of Results: Analysis results are applied to business strategies in ways that help improve decisions or solve problems.
Continuous Improvement: The data analytics process is constantly reviewed and improved. It is updated with new data and the results are made better.
Big Data and Data Analytics are powerful tools that help businesses gain competitive advantage, make better decisions, improve customer experience and optimize business processes. Therefore, they are of great importance in many industries and sectors.
Sources of Big Data
Sources of big data are various sources that produce or provide large volumes of data. These resources can be found in a wide variety of areas, including various industries, devices, platforms, and user interactions. Here are some common sources of big data:
Social Media Data: Social media platforms (e.g. Facebook, Twitter, Instagram, LinkedIn) generate text, photo, video and interaction data that users share. These platforms are popular sources that generate large amounts of data.
Web Traffic Data: Websites collect data such as users’ browsing habits, clicking behavior, and demographic information. This data is used for web analytics.
Internet of Things (IoT): IoT devices can generate all kinds of data. Smart home devices, industrial sensors, smart city systems and more form a huge ecosystem that constantly generates data.
Mobile Applications: Mobile applications provide user interactions, location data, and other information. This is used to improve personal user experience and location-based services.
E-Commerce Data: E-commerce platforms generate customer shopping habits, product reviews, sales data, and more. This data is used to direct marketing strategies and product offerings.
Banking and Finance Data: Banks generate huge amounts of data such as financial transactions, credit card usage, stock trading and more. This data is used for risk analysis, fraud detection and market forecasts.
Health Industry Data: Hospitals, clinics, and healthcare organizations generate large amounts of data such as patient records, medical images, drug interactions, and treatment outcomes. These data are used to improve healthcare and predict disease.
Research and Scientific Data: Data produced by scientific research, laboratories, and observatories includes large amounts of data in fields ranging from astronomy to biology.
Weather and Climate Data: Meteorological stations and satellite data generate big data for weather forecasts, climate change analysis and natural disaster forecasting.
Government and Public Data: Generates massive amounts of data for utilities, censuses, traffic data, urban planning, and more. These data are used for the development of public policies and city management.
These are just a few examples of the potential sources of big data. Big data comes from a wide variety of sources of data that are produced at an ever-increasing rate and are of great importance in different industries. The processing and analysis of this data has great potential to improve decision-making processes, identify trends, find new opportunities and solve problems.
Big Data Processing Technologies
Big Data processing technologies are tools, platforms and methods used to process, store and analyze large data sets quickly and effectively. Big data processing technologies have been developed and evolved due to the difficulty of applying traditional data processing methods (for example, relational databases) to big data. Here are some of the big data processing technologies:
Hadoop: Hadoop is an open source platform for big data processing. Hadoop performs parallel processing by distributing data over a large cluster. It basically uses Hadoop Distributed File System (HDFS) and MapReduce processing model. Also, within the Hadoop ecosystem there are many additional tools and components (eg Hive, Pig, Spark) and are used for big data analytics.
Apache Spark: Apache Spark is an open source data processing framework used for big data processing beyond Hadoop. Spark includes a number of components such as fast and broad processing capabilities, dataflows, query language (Spark SQL), intelligent data storage (Spark DataFrames), machine learning (MLlib), and graph processing (GraphX).
NoSQL Databases: NoSQL (Not Only SQL) databases are used as an alternative to traditional SQL-based databases for big data processing. NoSQL databases have advantages such as horizontal scalability, fast data writing and reading, multiple data models (document, column-based, key-value, etc.). Examples include MongoDB, Cassandra, HBase, and Redis.
Data Storage and Databases: There are data storage systems and databases specifically designed for big data processing. These systems are optimized for storing large volumes of rapidly changing data. For example, cloud-based databases such as Amazon DynamoDB, Google Bigtable, and Microsoft Azure Cosmos DB fall into this category.
Data Stream Processing: Tools and platforms used to process large data streams allow real-time analysis of data and instant decisions. Solutions such as Apache Kafka, Apache Flink and Apache Storm are used in this area.
GPU Accelerated Computing: Graphics processing units (GPUs) are used as accelerators for intensive computational operations such as big data processing and deep learning. It is especially popular in artificial intelligence and machine learning applications.
Data Visualization and Analytics Tools: Tools used to transform big data into more meaningful information include data visualization platforms (e.g. Tableau, Power BI), statistical analysis tools (R, Python), commercial software designed for big data analytics (SAS, IBM SPSS) takes place.
Cloud Computing Services: Cloud-based services (for example, Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP)) are offered for big data processing. These platforms quickly provide the resources necessary for big data processing.
These technologies enable big data processing processes to be more efficient and scalable, while analyzing big data and obtaining valuable information. By using these technologies, businesses work to extract more value from big data and gain competitive advantage.
Data Collection and Storage
Data collection and storage refers to the processes used to collect, organize and securely store information and data from various sources. This process is a fundamental step for the subsequent processing, analysis and use of data when necessary. Here are detailed descriptions of the data collection and storage processes:
Data collecting:
Data collection includes a set of methods used to collect data from various sources. These data sources can produce data in different types and formats. The data collection process includes these steps:
Identifying: The first step is to identify the goals and objectives of data collection. What data will be collected, why it is collected and for what purpose it will be used should be clearly defined.
Defining Data Sources: The sources from which data will be collected are defined. These sources can include websites, databases, sensors, surveys, social media, log files, machine sensors and more.
Selection of Data Collection Methods: Depending on the data sources, appropriate data collection methods and tools are selected. These methods can include manual data entry, automatic data retrieval, API usage, data mining, and more.
Data Collection Process: The data collection process is carried out according to the selected methods. At this stage, data is pulled from data sources, edited and moved to a data store.
Data Storage:
Data storage is an area where collected data is stored in a secure and orderly manner. Here are the basic stages of data storage:
Data Warehouse Design: A data warehouse is designed according to data storage requirements. It is determined how the data will be organized and which database or storage technology will be used.
Selection of Database or Storage Technology: A suitable database or storage technology is selected for data storage. There are various options such as relational databases, NoSQL databases, big data storage systems.
Storage of Data: The collected data is saved in the selected database or storage technology. This process is organized and indexed according to the structure and format of the data.
Data Security: Data security is of great importance. Security measures such as access control, encryption and data backup are taken.
Data Management: Data needs to be managed throughout its lifecycle. This includes organizing, updating, archiving and deleting data when necessary.
Backup and Recovery: To prevent data loss, regular data backups are taken and data recovery plans are created.
Data collection and storage processes form the basis of many applications such as big data analytics, business intelligence, reporting and decision support systems. Accurate data collection and secure data storage enable data to be used effectively and organizations to make informed decisions.
Data Analytics Algorithms
Data analytics algorithms are mathematical and statistical methods, calculations and software used to analyze data and transform it into meaningful information. These algorithms perform a variety of tasks, such as detecting patterns, relationships, and trends in data, making predictions, classifying, or clustering. Here are some common data analytics algorithms:
Regression Analysis: Regression analysis is used to model the relationship between dependent and independent variables. This analysis is suitable for situations where the dependent variable is continuous and is used to predict future values.
Logistic Regression: Logistic regression is used for situations where a dependent variable is categorical (for example, yes/no or classes). It is especially widely used for classification problems.
Decision Trees: Decision trees are used for classification and regression problems. It classifies data step by step or follows a set of decision rules when estimating values.
K-Means Clustering: K-Means clustering is used to group similar data points. Assigns data points to a specific number of clusters or clusters.
Support Vector Machines (SVM): SVM is a learning algorithm used for classification and regression problems. It is especially designed to make the best distinction between classes.
Naive Bayes Classifier: Naive Bayes is a probability-based classification algorithm used for classification problems. It is especially widely used for text classification (such as spam filtering).
K-Nearest Neighbor (KNN): KNN is an algorithm used for classification and regression problems. It classifies or predicts data points based on their surrounding neighbors.
Artificial Neural Networks: Artificial neural networks are a machine learning model containing artificial neurons and layers designed to be used in complex problems. It is especially used in areas such as image and voice recognition.
Decision Support Systems (DSS): DSS is a computer-based system that includes a set of algorithms and models used to support data analysis and decision-making processes.
Temporal Data Analysis: Temporal data analysis is used to identify trends, seasonality, and other patterns on time series data. This analysis is important in fields such as finance, weather forecasting and medical data.
Deep Learning: Deep learning refers to a field that performs complex learning tasks using multi-layer artificial neural networks. It is especially used for identifying complex patterns on large data sets.
Stacked Generalization Model (Ensemble Learning): Ensemble learning is used to obtain better results by combining multiple models. Examples are Random Forest and Gradient Boosting algorithms.
These data analytics algorithms are used in a wide variety of applications to understand data, discover patterns, and make data-based decisions. Which algorithm to use may vary depending on the type of data analyzed, the problem, and the goals.
Data Visualization and Reporting
Data visualization and reporting refers to the process of presenting large data sets, complex data analysis, trends, and key information in a more understandable and visual way. This process helps businesses and professionals better understand data, improve decision-making, and share information. Here are the main components and importance of data visualization and reporting:
Data Visualization:
Data visualization is the process of representing numerical data with graphs, charts, maps, charts, and other visual elements. This makes data clearer and helps us better understand relationships and patterns between visuals and data. Here are the importance of data visualization and the tools used:
Visual Communication: Presenting data visually through graphs and graphs makes information easier to understand. Visual communication is an important way to convey complex data simply and effectively.
Identifying Trends and Trends: Visual elements such as charts and time series charts help us quickly identify trends, seasonal patterns, and variations in data.
Decision Making Support: Business leaders and analysts can make more informed decisions by visualizing data. Seeing data visually makes it easier to evaluate different scenarios.
Rapid Problem Detection: Visual analysis is used to quickly detect anomalies or problems. For example, seeing an unexpected decline in a sales chart provides early recognition of a problem.
Customer Experience Improvement: By analyzing customer data, we can better understand customer behavior and preferences. This helps us create personalized marketing strategies.
Data visualization tools are used to create these visual representations. Popular data visualization tools include Tableau, Power BI, Google Data Studio, Python-based Matplotlib and Seaborn libraries, and JavaScript libraries in R such as ggplot2 and D3.js.
Reporting:
Reporting refers to the process of presenting data analysis results in written or electronic reports. Reports often provide important information that helps businesses, managers, and stakeholders make data-driven decisions. Here is the importance of reporting and some commonly used reporting tools:
Decision Making Support: Reports support organizations in making information-based decisions. Business leaders can create their strategies based on data from the reports.
Data Communication: Reports enable data to be shared in written or electronic formats. This makes it easier to share data with internal or external stakeholders.
Performance Monitoring: Reports help organizations monitor their performance and evaluate whether they are meeting their goals.
Quick Access and Analysis: Summary reports summarize large data sets and help analysts or managers quickly access important information.
Reporting tools are used to create, customize, and share these reports. These tools include software-based tools such as Microsoft Excel, Google Sheets, Crystal Reports, SSRS (SQL Server Reporting Services) and business intelligence platforms such as Tableau, Power BI, QlikView. These tools help users present data analysis results effectively.
