MindMap Gallery Big data application development process
There needs to be a modeling process, the amount of data and processing efficiency need to be considered, and the reliability and maintainability of the data need to be considered.
Edited at 2022-02-21 15:11:44Ce calendrier annuel, créé avec EdrawMax, présente une disposition claire et organisée des mois de janvier à décembre. Chaque mois est affiché dans un cadre distinct, montrant les jours de la semaine et les dates correspondantes. Les weekends (samedis et dimanches) sont mis en évidence pour une meilleure visibilité. Ce format est idéal pour la planification et l'organisation des activités tout au long de l'année, offrant une vue d'ensemble rapide et facile à consulter.
This quarterly calendar overview for 2026, created with EdrawMax, presents a structured and colorful layout of the entire year divided into four quarters. Each quarter is displayed in a separate column, showcasing the months within that quarter in a clear grid format. The days of the week are labeled, and each date is marked within its respective cell, allowing for easy identification of dates across the year. This calendar is an excellent tool for long-term planning, providing a comprehensive view of the year at a glance.
This weekly calendar for 2026 is designed using EdrawMax to provide a detailed and organized view of each week, starting from January. The left side features a mini monthly calendar for quick reference, highlighting the current week in yellow. Below it, there's a section for weekly goals to help prioritize tasks. The main area is a time-grid from 6:00 AM to 12:00 AM, divided into half-hour slots, allowing for precise scheduling of daily activities throughout the week. This layout is ideal for managing a busy schedule efficiently.
Ce calendrier annuel, créé avec EdrawMax, présente une disposition claire et organisée des mois de janvier à décembre. Chaque mois est affiché dans un cadre distinct, montrant les jours de la semaine et les dates correspondantes. Les weekends (samedis et dimanches) sont mis en évidence pour une meilleure visibilité. Ce format est idéal pour la planification et l'organisation des activités tout au long de l'année, offrant une vue d'ensemble rapide et facile à consulter.
This quarterly calendar overview for 2026, created with EdrawMax, presents a structured and colorful layout of the entire year divided into four quarters. Each quarter is displayed in a separate column, showcasing the months within that quarter in a clear grid format. The days of the week are labeled, and each date is marked within its respective cell, allowing for easy identification of dates across the year. This calendar is an excellent tool for long-term planning, providing a comprehensive view of the year at a glance.
This weekly calendar for 2026 is designed using EdrawMax to provide a detailed and organized view of each week, starting from January. The left side features a mini monthly calendar for quick reference, highlighting the current week in yellow. Below it, there's a section for weekly goals to help prioritize tasks. The main area is a time-grid from 6:00 AM to 12:00 AM, divided into half-hour slots, allowing for precise scheduling of daily activities throughout the week. This layout is ideal for managing a busy schedule efficiently.
Big data application development process
Data collection and preprocessing
Obtaining initial data
Data cleaning
Data integration and fusion
data transformation
data reduction
Example
Internal data of enterprise organization
social media data
Big data storage and management
storage media
Memory
disk
tape
Data organization and management
database
Relational Database
Document storage
Column storage
key value storage
graph database
Distributed database
Memory Database
Big data computing framework
type of data
static data
dynamic data
computing framework
Batch processing
specific technology
MapReduce
Spark
data mining
Classification analysis
Cluster analysis
Basic framework of data visualization
data visualization process
Data visualization design
problem characterization layer
abstraction layer
coding layer
algorithm layer
Requires strong expressiveness and effectiveness