Data Technologies
Overview

Data Technologies

Modern data technologies form the backbone of data engineering and analytics systems. This section covers the tools, platforms, and frameworks that enable organizations to process, store, and analyze data at scale.

Categories

Databases

  • Relational Databases: PostgreSQL, MySQL, SQL Server
  • NoSQL Databases: MongoDB, Cassandra, DynamoDB
  • Graph Databases: Neo4j, Amazon Neptune
  • Time Series Databases: InfluxDB, TimescaleDB

Data Processing Engines

  • Batch Processing: Apache Spark, Apache Hadoop
  • Stream Processing: Apache Kafka, Apache Flink, Apache Storm
  • Query Engines: Apache Drill, Presto, Apache Impala

Cloud Platforms

  • AWS: S3, Redshift, EMR, Kinesis, Glue
  • Google Cloud: BigQuery, Dataflow, Pub/Sub, Cloud Storage
  • Azure: Synapse Analytics, Data Factory, Event Hubs

Data Storage

  • Data Warehouses: Snowflake, Amazon Redshift, Google BigQuery
  • Data Lakes: Apache Hadoop, Amazon S3, Azure Data Lake
  • Object Storage: Amazon S3, Google Cloud Storage, Azure Blob

Orchestration Tools

  • Workflow Management: Apache Airflow, Prefect, Dagster
  • Container Orchestration: Kubernetes, Docker Swarm

Monitoring & Observability

  • Data Quality: Great Expectations, Deequ
  • Monitoring: Prometheus, Grafana, DataDog
Monitoring & ObservabilityDatabases