- Compare a traditional database with an analytical database and a NoSQL database.
| Feature |
Traditional Database |
Analytical Database |
NoSQL Database |
| Data model |
Relational |
Columnar |
Document, key-value, graph, wide-column store |
| Schema |
Fixed |
Dynamic |
Dynamic |
| Query language |
SQL |
SQL, MDX, HiveQL, etc. |
NoSQL-specific query languages |
| Scalability |
Vertical (adding more resources to a single server) |
Horizontal (clustering multiple servers) |
Horizontal |
| Use cases |
Online transaction processing (OLTP), customer relationship management (CRM), enterprise resource planning (ERP) |
Online analytical processing (OLAP), data warehousing, data mining |
Web applications, social media, IoT, big data |
drive_spreadsheetExport to Sheets
Traditional databases are relational databases, which means that they store data in tables that are related to each other through foreign keys. Traditional databases are well-suited for applications that require complex transactions and ACID compliance (atomicity, consistency, isolation, and durability). However, traditional databases can struggle to scale horizontally to handle large volumes of data.
Analytical databases are columnar databases, which means that they store data in columns rather than tables. This makes analytical databases very efficient for running complex queries on large datasets. Analytical databases are often used for data warehousing and data mining applications.
NoSQL databases are non-relational databases, which means that they do not use the traditional relational data model. NoSQL databases are available in a variety of different data models, such as document, key-value, graph, and wide-column store. NoSQL databases are very scalable and can handle large volumes of data with ease. However, NoSQL databases typically do not offer the same level of ACID compliance as traditional databases.
- Compare three examples:
- Traditional database: PostgreSQL
- Analytical database: Hadoop
- Statistics package: R
PostgreSQL is a popular open-source relational database management system (RDBMS). It is known for its reliability, performance, and scalability. PostgreSQL is used in a wide variety of applications, including OLTP, CRM, and ERP systems.
Hadoop is an open-source software framework for distributed storage and processing of large datasets. It is a popular choice for building data warehouses and big data analytics systems. Hadoop is known for its scalability and ability to handle large volumes of data.
R is a popular open-source programming language and software environment for statistical computing and graphics. It is widely used by statisticians and data scientists to analyze data and generate insights.
How are these three tools related and how is each used as part of an overall analytics system?
PostgreSQL, Hadoop, and R can be used together to create a powerful analytics system. PostgreSQL can be used to store and manage the data, Hadoop can be used to distribute and process the data, and R can be used to analyze the data and generate insights.
Here is an example of how these three tools could be used together to build an analytics system:
- Data is collected from various sources and stored in PostgreSQL.
- The data is then exported from PostgreSQL and loaded into Hadoop.
- Hadoop is used to distribute and process the data.
- R is used to analyze the data and generate insights.
- The insights are then exported from R and imported into a visualization tool to create reports and dashboards.
This is just one example of how PostgreSQL, Hadoop, and R can be used together to build an analytics system. The specific tools and technologies used will vary depending on the specific needs of the organization.
Here is a more detailed discussion of how each tool is used as part of an overall analytics system:
PostgreSQL: PostgreSQL is used to store and manage the data. It provides a reliable and scalable platform for storing large datasets. PostgreSQL also provides a variety of features that make it well-suited for analytics applications, such as complex queries, ACID compliance, and support for foreign keys.
Hadoop: Hadoop is used to distribute and process the data. It can scale horizontally to handle large volumes of data. Hadoop also provides a variety of tools for processing and analyzing data, such as Hive, Pig, and Spark.
R: R is used to analyze the data and generate insights. It provides a wide range of statistical and machine learning algorithms. R is also very good at data visualization.
Overall, PostgreSQL, Hadoop, and R are a powerful combination for building analytics systems. PostgreSQL provides a reliable and scalable platform for storing data, Hadoop provides a way to distribute and process large datasets, and R provides a wide range of tools for analyzing data and generating insights.
