Data Engineering & ETL Tools

• Apache Spark, Hadoop
• Airflow, dbt (data build tool)
• Talend, Informatica, AWS Glue

Data Warehousing & Cloud Solutions

• Snowflake, Amazon Redshift, Google BigQuery
• Azure Synapse Analytics, Databricks

Programming Languages

• Python (Pandas, NumPy, PySpark)
• R for statistical analysis
• Scala for big data processing

Data Visualization & Analytics

• Tableau, Power BI, Looker
• Jupyter Notebooks for exploration

Security & Standards

• GDPR (General Data Protection Regulation)
• ISO 27001 (Information Security Management)
• Encryption protocols (TLS/SSL, AES)
• Role-based access control (RBAC)

Networking & APIs

• RESTful APIs, GraphQL for data integration
• Kafka for real-time data streaming

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Relational Databases & SQL

Relational databases

• MySQL – Open-source, widely used for web applications (e.g., WordPress, e-commerce).
• PostgreSQL – Advanced, enterprise-grade with support for JSON, GIS (spatial data).
• SQL Server – Microsoft's solution, often found in corporate environments.
• Oracle Database – Robust, widely used in finance, banking, and enterprise systems.
• MariaDB – A MySQL fork, optimized for high performance and scalability.

SQL is used for:

• Writing queries (SELECT, INSERT, UPDATE, DELETE).
• Creating tables (CREATE TABLE).
• Indexing data for faster access (CREATE INDEX).
• Managing relationships (JOIN, FOREIGN KEY constraints).
• Running aggregate functions (SUM, AVG, COUNT).

NoSQL Databases
NoSQL databases store data in flexible, schema-less formats, suitable for handling large-scale applications and unstructured data. Examples include:

• MongoDB – Document-oriented; stores data in JSON-like format, great for applications with changing schemas.
• Cassandra – Distributed, high-speed key-value store used for handling big data.
• Redis – An in-memory key-value store optimized for caching and fast data retrieval.
• DynamoDB – AWS’s managed NoSQL solution, offering seamless scalability.

These databases often use different query methods:

• MongoDB Query Language (MQL) – JSON-like syntax (db.collection.find({ "name": "Alice" })).
• Cassandra Query Language (CQL) – SQL-like syntax (SELECT * FROM users WHERE id = 1).
• Key-based Lookups – Used in Redis and DynamoDB for fast data retrieval (GET user:1234).

Graph Databases

• Graph databases are optimized for storing complex relationships between entities.
• Neo4j – Uses Cypher Query Language (MATCH (a:User)-[:FRIEND]->(b) RETURN b).
• Amazon Neptune – Managed graph database supporting Gremlin queries.
• ArangoDB – Multi-model database combining key-value, document, and graph features.

Graph queries are excellent for:

• Social networks (friendship graphs).

Recommendation systems (connecting products/users).

• Fraud detection (linking financial transactions).
• Columnar & Time-Series Databases
• ClickHouse – Open-source columnar database, lightning-fast for analytical workloads.
• Google Bigtable – Column-family database designed for massive scalability.
• InfluxDB – Optimized for storing time-series data (IoT, server monitoring).
• TimescaleDB – PostgreSQL-based solution for time-series data.
• Columnar databases are used in analytical environments where reading large datasets efficiently is required.

In-Memory Databases & Search Engines

• ElasticSearch – Text search and analytics database using Elasticsearch Query DSL.
• Memcached – Simple in-memory caching system.
• Apache Solr – Powerful search platform supporting structured queries.