Big Data Introduction Training

Stratebi; Madrid; Presencial; EUR 30.000 - 50.000; Jornada completa

Descripción de la vacante

Una empresa innovadora busca profesionales apasionados por Big Data para diseñar soluciones personalizadas que optimicen las oportunidades de negocio. Este curso ofrece una visión integral del manejo de grandes volúmenes de datos, utilizando tecnologías como Hadoop y NoSQL para enfrentar los desafíos de captura, almacenamiento y análisis. Los participantes aprenderán a implementar arquitecturas de Big Data y a utilizar herramientas de análisis y visualización para transformar datos en información valiosa. Si te entusiasma el análisis de datos y quieres marcar la diferencia, esta es tu oportunidad de unirte a un entorno dinámico y en constante evolución.

Formación

• Experiencia en arquitecturas de Big Data y herramientas de análisis de datos.
• Conocimientos en Hadoop, NoSQL, y herramientas de visualización.

Responsabilidades

• Diseñar e implementar soluciones de Big Data adaptadas a las necesidades del cliente.
• Analizar y procesar grandes volúmenes de datos utilizando herramientas como Hadoop y NoSQL.

Conocimientos

Big Data

Hadoop

NoSQL

Data Analysis

Data Visualization

SQL

Pentaho

Spark

Educación

Bachelor's Degree in Computer Science

Master's Degree in Data Science

Herramientas

Pentaho Data Integration

Sqoop

Flume

Hive

Pig

Descripción del empleo

Before organising a course or seminar, we listen to the real needs and objectives of each client, in order to adapt the training and get the most out of it. We tailor each course to your needs.

We are also specialists in 'in company' trainings adapted to the needs of each organisation, where the benefit for several attendees from the same company is much greater. If this is your case, contact us.

Goal

This course is designed to introduce and explain the main concepts and technologies in the Big Data field.

The aim of this course is to provide a holistic view of Big Data, relying on its ability to generate business opportunities and optimize existing ones.

Examples of architectures already implemented in the market and use cases in which it will analyze Big Data will be decisive.

Big Data (or handling large volumes of information) refers to datasets that grow so large that they become awkward to manage with traditional database tools. Difficulties include capture, storage, search, sharing, analysis, and visualization.

If this trend continues, due to the benefits of working with larger sets of data that allow analysts to "spot business trends, prevent diseases, combat crime," new technologies like NoSQL and Hadoop will be needed to support these efforts.

Big Data utilizes heterogeneous technologies that are complementary to achieve these goals (Hadoop, NoSQL, Column-oriented DB, SQL Databases, etc.), along with powerful visualization tools, many of which are open source.

In Stratebi (contact us for a presentation), we use the power of business intelligence with Big Data to provide Big Data Analytics solutions in areas such as:

• Solutions for Social Media (Twitter, Facebook, etc.)
• Web analytics solutions, analysis of logs, e-commerce, etc.
• Smart City Solutions / Open Data
• Fraud detection, auditing, and performance level systems
• Security solutions and financial analysis for Retail, Telco, Banking, and Insurance
• Advanced customer segmentation solutions, leads, and automation of commercial activities
• Analysis solutions for utilities and sensors (energy, water, pollution, light, etc.)
• Detection solutions for buying patterns, recommendations, etc.
• Joint solutions for unstructured and structured bulk loading and analysis of information previously unaddressed

Thanks to Open Source technologies, the implementation of these solutions represents significant cost savings over proprietary solutions (e.g., Big Data Analytics with Pentaho).

1. Big Data Architectures

• Introduction and classification of different architectures and Big Data systems available on the market
• In-depth study of the Hadoop environment: HDFS, MapReduce, YARN, stack analysis tools available on HDFS and MapReduce (Hive, Pig, etc.), introduction to Hadoop distributions, etc.
• Study of the main NoSQL solutions: Cassandra, MongoDB, etc.
• Introduction to analytical databases: HP Vertica and MonetDB
• Considerations for choosing a Big Data architecture
• Practical examples and future vision on these databases

Installing a Hadoop distribution on a single node for testing

• Introduction to Hadoop cluster management
• Introduction to the HDFS file system

2. Obtaining and Data Movement in Big Data

• Study of the main types of existing data sources

Structured, semi-structured, and unstructured data

• Batch and streaming
• Analysis of the main tools available for acquisition and data movement:

Pentaho Data Integration: Loading, processing, and data extraction of any kind from data sources to HDFS and vice versa.

• Sqoop: Loading and relational data extraction (RDBMS -> HDFS, HDFS -> RDBMS) in batch

• Flume: Loading and processing real-time data
• Exercises with the above tools based on a case study to obtain data from logs, social networks, etc.

3. Processing of Big Data

• Analysis of temporal requirements (opportunity analysis)
• Introduction to the main tools for processing and analysis of Big Data

Tools on MapReduce: Pig, Hive

• Tools that do not use MapReduce: Spark, Spark Streaming, Storm, etc.
• Exercise based on a case study for processing log data, social networks, etc.
• Case analysis of market research: Amazon recommendation system, analysis of sensor data from transport companies, analysis of clicks on web pages, etc.
• Analysis of case studies based on our extensive experience in the development of Big Data projects

Schedule: 16:00H - 21:00H (CEST - Madrid)

Schedule: 9:00H - 14:00H and 15:30H - 18:30H