Data Management and Database Technologies

Databases

store data generated by business apps, sensors, operations, and transaction processing systems (TPS)

Data Warehouses

integrate data from multiple databases and data silos across the organization

Data Marts

are small-scale data warehouses that support a single function or one department

Business Intelligence

are tools and techniques process data and do statistical analysis for insight and discovery

Database Management Systems

are software used to manage the additions, updates, and deletions of data as transactions occur, and to support data queries and reporting. They are online transaction-processing (OLTP) systems

Data Filtering

Identifies errors (e.g., missing values, duplicates).

Integrity Checks

Ensures consistency (e.g., validating email formats).

Synchronization

Combines data from disparate sources (e.g., CRM + ERP systems)

Latency

Delay between data creation and availability (critical for real-time apps)

Query Response

Pre-staging data (pre-calculating results) speeds up analysis

Consistency

Immediate vs. eventual updates (e.g., stock market feeds require real-time accuracy)

Relational Management Systems

provide access to data using a declarative language—structured query language (SQL)

Structured Query Language

is a standardized query language for accessing databases

Query

are ad hoc (unplanned) user requests for specific data

Declarative Languages

simplify data access by requiring that users only specify what data they want to access without defining how access will be achieved. The format of a basic SQL statement is

Data Management

oversees the end-to-end lifecycle of data

Centralized Databases

• Easier security (single point of control).

• Better data consistency (all updates are centralized).

Distributed Databases

• Fault tolerance (survives node failures).

• Faster local queries (data is stored closer to users).

Dirty Data