Content:
• Introduction to SQL and NoSQL databases
• Understanding data modeling and normalization
• Querying and indexing data
• Data migration and management
• Introduction to data warehousing and data lakes
Databases and Data Management:
Databases are used to store and manage data in a structured way. SQL and NoSQL are the two main categories of databases.
Introduction to SQL and NoSQL databases:
SQL databases are based on the relational model, which means that data is organized into tables with predefined relationships between them. Oracle, MySQL, and PostgreSQL are a few examples of SQL databases.
On the other hand, NoSQL databases are built to manage unstructured and semi-structured data. They are typically used in applications that require high scalability and performance. The NoSQL databases MongoDB, Cassandra, and Redis are a few examples.
Understanding data modeling and normalization:
The process of creating a database's structure is known as data modelling.
A database must be divided into smaller, more manageable
tables, and relationships must be established between them. The goal of data
modeling is to create a database that is efficient, scalable, and easy to
maintain.
Normalization is a technique used in data modeling to eliminate data redundancy and improve data integrity. A database must be divided into smaller, more manageable tables, and relationships must be established between them.
Querying and indexing data:
Querying data involves retrieving specific data from a database using SQL or other query languages. Indexing is a technique used to optimize the speed of queries by creating indexes on columns that are frequently used in queries.
Data migration and management:
The process of moving data from one database to another is known as data migration. It is typically done when migrating from one type of database to another or when upgrading to a newer version of a database.
Data management involves tasks such as backup and recovery, performance tuning, and data security. It is important to manage data effectively to ensure that it is available and secure.
Introduction to data warehousing and data lakes:
Data warehousing is a technique used to store and manage large amounts of data for analysis and reporting. It involves creating a separate database for storing data that is optimized for reporting and analysis.
Data lakes are similar to data warehouses but are designed to handle unstructured and semi-structured data. They are typically used in big data applications where data is collected from a variety of sources and needs to be analyzed in real-time.
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