BIG-DATA.AE1
Big Data: Concepts, Technology, and Architecture
Learn how to manage big data for extracting meaningful insights by exploring the fundamentals, challenges and technology.
- Practice in 28 Laboratorios prácticos — nothing to install
- 10 Lecciones interactivas y 110 topics mapped to the official exam objectives
- 157 Preguntas del examen de práctica
Beginner A tu propio ritmo · 1 año de acceso 4.5/5 (39 Revisar)
28 LiveLabs prácticos
Practice real IT tasks in guided environments.
- Entornos reales
- Calificación automática
- Sin instalación
10Lecciones interactivas
110Topics
28Laboratorio en vivo
157Preguntas del examen de práctica
142Tarjetas didácticas
142Glosario de términos
01 / Habilidades que obtendrás
What you will be able to do
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No se requiere tarjeta de crédito
This Big Data training course offers an in-depth analysis of the concept, its challenges, and the tool and technology required to manage it. Explore the 3Vs of Big Data course - volume, velocity, and variety for handling huge data sets that are usually unmanageable by traditional database systems. Learn the architecture and components of Big Data systems, focusing on the Hadoop ecosystem, which includes HDFS, MapReduce, YARN, Spark, Hive, Pig, and HBase. Discover different types of NoSQL databases and its application for various data models. By the end of this online training, you’ll be able to design, implement, manage, store, process, and analyze big data architecture solutions.
- Understanding the 3Vs - volume, velocity, and variety
- Identifying challenges with traditional database systems
- Gain expertise in the component and architecture of Big Data systems
- Using NoSQL database for diverse data types
- Implementing effective data processing strategies
- Utilizing cloud computing for scalable solutions
- Managing large datasets for extracting useful insights
- Building a solid foundation in data analysis and preparing for further specialization
Course Highlights
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10 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
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28 LiveLabs prácticos Escenarios interactivos guiados con evaluación instantánea
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157 Preguntas de práctica Pruebas de evaluación con justificaciones de respuesta detalladas
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1 año de acceso completo Aprendizaje a tu propio ritmo, accesible en cualquier momento y en todos los dispositivos
02 / Lecciones y laboratorios
See exactly what you will learn and practice
Plan de estudios
10 Lecciones interactivas · 110 topics01 Introduction to the World of Big Data 11 topics · 2 Laboratorio en vivo +
- Understanding Big Data
- Evolution of Big Data
- Failure of Traditional Database in Handling Big Data
- 3 Vs of Big Data
- Sources of Big Data
- Different Types of Data
- Big Data Infrastructure
- Big Data Life Cycle
- Big Data Technology
- Big Data Applications
- Big Data Use Cases
2 Laboratorio en vivo in this lesson — see the labs panel →
02 Big Data Storage Concepts 5 topics · 1 Laboratorio en vivo +
- Cluster Computing
- Distribution Models
- Distributed File System
- Relational and Non‐Relational Databases
- Scaling Up and Scaling Out Storage
1 Laboratorio en vivo in this lesson — see the labs panel →
03 NoSQL Database 8 topics · 1 Laboratorio en vivo +
- Introduction to NoSQL
- Why NoSQL
- CAP Theorem
- ACID
- BASE
- Schemaless Databases
- NoSQL (Not Only SQL)
- Migrating from RDBMS to NoSQL
1 Laboratorio en vivo in this lesson — see the labs panel →
04 Big Data Processing, Management, and Cloud Computing 15 topics · 3 Laboratorio en vivo +
- Part I: Big Data Processing and Management Conce...essing, Management Concepts, and Cloud Computing
- Data Processing
- Shared Everything Architecture
- Shared‐Nothing Architecture
- Batch Processing
- Real‐Time Data Processing
- Parallel Computing
- Distributed Computing
- Big Data Virtualization
- Part II: Managing and Processing Big Data in Clo...essing, Management Concepts, and Cloud Computing
- Introduction
- Cloud Computing Types
- Cloud Services
- Cloud Storage
- Cloud Architecture
3 Laboratorio en vivo in this lesson — see the labs panel →
05 Driving Big Data with Hadoop Tools and Technologies 15 topics · 2 Laboratorio en vivo +
- Apache Hadoop
- Hadoop Storage
- Hadoop Computation
- Hadoop 2.0
- HBASE
- Apache Cassandra
- SQOOP
- Flume
- Apache Avro
- Apache Pig
- Apache Mahout
- Apache Oozie
- Apache Hive
- Hive Architecture
- Hadoop Distributions
2 Laboratorio en vivo in this lesson — see the labs panel →
06 Big Data Analytics 9 topics · 1 Laboratorio en vivo +
- Terminology of Big Data Analytics
- Big Data Analytics
- Data Analytics Life Cycle
- Big Data Analytics Techniques
- Semantic Analysis
- Visual analysis
- Big Data Business Intelligence
- Big Data Real‐Time Analytics Processing
- Enterprise Data Warehouse
1 Laboratorio en vivo in this lesson — see the labs panel →
07 Big Data Analytics with Machine Learning 3 topics · 1 Laboratorio en vivo +
- Introduction to Machine Learning
- Machine Learning Use Cases
- Types of Machine Learning
1 Laboratorio en vivo in this lesson — see the labs panel →
08 Mining Data Streams and Frequent Itemset 16 topics · 4 Laboratorio en vivo +
- Itemset Mining
- Association Rules
- Frequent Itemset Generation
- Itemset Mining Algorithms
- Maximal and Closed Frequent Itemset
- Mining Maximal Frequent Itemsets: the GenMax Algorithm
- Mining Closed Frequent Itemsets: the Charm Algorithm
- CHARM Algorithm Implementation
- Data Mining Methods
- Prediction
- Important Terms Used in Bayesian Network
- Density-Based Clustering Algorithm
- DBSCAN
- Kernel Density Estimation
- Mining Data Streams
- Time Series Forecasting
4 Laboratorio en vivo in this lesson — see the labs panel →
09 Cluster Analysis 13 topics · 1 Laboratorio en vivo +
- Clustering
- Distance Measurement Techniques
- Hierarchical Clustering
- Analysis of Protein Patterns in the Human Cancer‐Associated Liver
- Recognition Using Biometrics of Hands
- Expectation Maximization Clustering Algorithm
- Representative‐Based Clustering
- Methods of Determining the Number of Clusters
- Optimization Algorithm
- Choosing the Number of Clusters
- Bayesian Analysis of Mixtures
- Fuzzy Clustering
- Fuzzy C‐Means Clustering
1 Laboratorio en vivo in this lesson — see the labs panel →
10 Big Data Visualization 15 topics · 12 Laboratorio en vivo +
- Big Data Visualization
- Conventional Data Visualization Techniques
- Tableau
- Bar Chart in Tableau
- Line Chart
- Pie Chart
- Bubble Chart
- Box Plot
- Tableau Use Cases
- Installing R and Getting Ready
- Data Structures in R
- Importing Data from a File
- Importing Data from a Delimited Text File
- Control Structures in R
- Basic Graphs in R
12 Laboratorio en vivo in this lesson — see the labs panel →
Laboratorios prácticos Our edge
28 Laboratorio en vivos- Discussing Big Data Characteristics
- Discussing Big Data
- Discussing Big Data Storage
- Discussing the NoSQL Database
- Implementing the Data Processing Cycle
- Discussing Big Data Processing and Management Concepts - Part I
- Discussing Big Data Processing and Management Concepts - Part II
- Discussing Components of Hadoop
- Discussing Big Data Using Hadoop Tools and Technologies
- Discussing Big Data Analytics
- Discussing Machine Learning
- Implementing Frequent Itemset Mining Using R
- Determining the Support Count and Confidence Count
- Implementing the Eclat Algorithm Using R
- Implementing Apriori Algorithm Using R
- Implementing K-Means Clustering
- Creating a Connection in a New Workbook
- Creating a Bar Chart
- Creating a Line Chart
- Creating a Pie Chart
- Creating a Bubble Chart
- Creating a Box Plot
- Assigning Value to a Variable
- Using the length(), mean(), and median() Functions
- Using the matrix() Function
- Using the if-else Statement
- Using the for Loop
- Using the while Loop
Los laboratorios se ejecutan en tu navegador; no hay nada que instalar.
03 / Preguntas frecuentes
Preguntas antes de empezar
What is the focus of this Big Data course?+
This course focuses on the Big Data fundamentals, architecture, and analysis for handling huge data sets that cannot be managed by the traditional ways. The core concepts revolve around building Big Data systems, and extract insights
How will this Big Data course benefit me?+
This course will improve your problem solving skills as you learn to handle complex data challenges with a data-driven approach. Most importantly, you will have a competitive edge as you explore the job opportunities in the big data landscape.
What is Big Data Architecture?+
It is designed to handle massive datasets that are otherwise unmanageable for traditional database systems. It focuses on the ingestion, storage, processing, and analysis of data characterized by the 3Vs - volume, velocity, and variety.
How many layers are there in a Big Data Architecture?+
Big Data concepts are applicable to all businesses handling huge amounts of data. It’s used to access and analyze data for making important decisions on various aspects like production, customer feedback and returns, anticipating future demands to reduce production outages, etc.
What are the Big Data Technologies I’ll be learning?+
The Big Data: concepts, technology, and architecture field is very vast and you’ll be learning numerous technologies including Hadoop Ecosystem, NoSQL Database, Data Warehousing and ETL tools, Cloud Platforms (AWS, Azure, and GCP), Data Processing Engines (Apache Flink, Apache Storm), and Data Visualization Tools (Tableau, Power BI, Looker).
What is the role of Big Data Architecture in businesses?+
Big Data concepts are applicable to all businesses handling huge amounts of data. It’s used to access and analyze data for making important decisions on various aspects like production, customer feedback and returns, anticipating future demands to reduce production outages, etc.
How is Big Data Stored?+
It can be stored in several ways depending on data volumes, types, access patterns, cost and requirements. It can be stored in the cloud, data warehouses, or both. Considering the cost effectiveness and scalability of cloud solutions many big businesses are now moving towards cloud storage.
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- 1 año de acceso completo
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No se requiere tarjeta de crédito