Cloud Computing for Beginners - Database Technologies

Explore core database terms such as database, database management system, instance, and schema, compare operational and analytics data, explain transactions, data warehouse, data lake, ETL versus ELT, and database scalability.

Understand how a database stores and retrieves data, separating application code from the data, using a database management system, database instances, and schemas to organize tables, columns, and constraints.

Learn how operational data powers real-time decision making with OLTP databases that handle atomic, consistent, isolation, durable transactions at low latency.

Explore analytics data as input for business intelligence, compare OLTP and OLAP architectures, and learn how data warehouses and multidimensional cubes enable fast, complex queries.

Data warehouses consolidate data from multiple sources for OLAP analytics and store historical data at scale. Data lakes keep raw data intact for flexible data science and AI use cases.

Compare ETL and ELT for moving data from source systems to data warehouses or data lakes, detailing extract, transform, load steps and the trade-offs of transformation order and storage.

Compare batch processing and stream processing for moving data between systems. The Lambda architecture combines both to enable real-time analytics while preserving batch consolidation.

Balance performance and resource use by applying vertical scaling (scaling up) and horizontal scaling (scaling out) to database bottlenecks.

Explore the main types of database technologies, from relational SQL and NoSQL to distributed SQL, in-memory, and time series databases, and learn where they fit best and why.

Explore traditional relational databases (SQL) with table schemas and keys; see how SQL queries handle customers, products, and orders. Understand scalability limits that push web apps toward non-relational databases.

Explore non-relational databases, or NoSQL, designed for scale-out and high availability with simple data models. Learn about key-value, document, wide-column, and graph types, and eventual consistency.

Explore distributed SQL databases (NewSQL) that combine SQL relational data, ACID transactions, and global scalability for OLTP workloads, addressing NoSQL trade-offs with high availability.

Explore how in-memory databases store data in memory rather than on disk, delivering fast, predictable performance. Note the higher memory cost and smaller datasets for caching and web sessions.

Discover time series databases, optimized for timestamped data and high ingestion, storing recent data in memory and historical data on disk with millisecond-level queries.

Explore the four types of NoSQL databases—key value database, documents database, white column database, and graph database—and learn how each is tuned to specific use cases.

Explore key-value databases, the simplest NoSQL model that stores data as key-value pairs, enabling fast lookups with put, get, and delete, ideal for caching and shopping carts at scale.

Explore document databases that group key value pairs into documents with unique IDs and no fixed schema. Learn how a flexible json or xml based catalog handles diverse product attributes.

Explore wide column, column oriented databases that store data by columns and column families, enabling efficient OLAP queries, compression, scalability, and big data warehousing, with trade-offs for OLTP.

Explore graph databases that model data as nodes and edges to capture complex relationships, enabling fast queries for recommendations, fraud detection, and cyber security detection.

Explore the db engines ranking overview, highlighting Oracle, MySQL, Microsoft SQL, PostgreSQL, and MongoDB while comparing relational, NoSQL, and time series databases, plus open source versus commercial trends.

Explore on-premises installation, infrastructure as a service, and database as a service options. Learn how cloud providers handle management, updates, security, replication, monitoring, and auto scaling.

Explore the business challenges of traditional databases amid data explosions, rising costs, and manual management, and why fully managed cloud database services offer scalable solutions.

Explore database as a service, a cloud model that lets users rent and auto-manage databases via a self-service, on-demand platform while providers handle backups, security, upgrades, and availability.

Database as a service enables on-demand provisioning, reduces administrative overhead, boosts developer productivity, offers cost savings, scalable on demand, and increases high availability across multiple data centers.

Explore Azure database as a service options, including relational databases such as Azure SQL, PostgreSQL, MySQL, and MariaDB, and Cosmos DB NoSQL with real-time IoT telemetry use cases.

Explore Amazon's broad database services across relational, key-value, in-memory, document, wide-column, and time-series types. Explore Aurora, DocumentDB, DynamoDB, Timestream, Redshift, and rds with use cases and learning resources.

Assess Google Cloud database options from Cloud SQL and Cloud Spanner to Cloud Bigtable, Firestore, and Memorystore, comparing relational, distributed SQL, and NoSQL approaches for scalable applications.

Recaps database fundamentals—the management system, a database instance, and a database schema—then contrasts operational (oltp) and analytics (olap) data, data movement, etl/elt, and sql vs nosql trends.