Common Tech Stacks in Data Analytics

The following is a brief description of the tools and technologies within each layer of a modern data stack used in data analytics:

1. Data Integration Layer

• Purpose: Collects and takes data from various sources.

  
  

  Examples:

i. Daton: A data integration platform for managing and automating data pipelines.

ii. AWS Kinesis: A real-time data streaming service for collecting and processing large streams of data.

iii. Logstash: An open-source tool for managing events and logs, transforming and forwarding them to different systems.

2. Data Storage Layer

• Purpose: Stores data in structured or unstructured formats.

  Examples:

  
  

  • MySQL/PostgreSQL: Relational databases for structured data, using SQL for queries.

  • MongoDB/Cassandra: NoSQL databases for storing unstructured or semi-structured data at scale.

  • AWS S3/Google Cloud Storage: Cloud-based object storage for large volumes of data, both structured and unstructured.

    
    

3. Data Processing Layer

• Purpose: Cleans and transforms data.

  Examples:

  • Apache Spark: A fast, in-memory data processing engine for big data analytics.

  • Hadoop: A distributed framework for processing large datasets in parallel across multiple servers.

  • AWS Glue: A serverless ETL service for preparing and loading data.

    
    

4. Data Analysis Layer

• Purpose: Analyses and extracts insights from data.

  Examples:

  • TensorFlow/Py Torch: Machine learning frameworks for building and training models.

  • R/Python: Programming languages for statistical analysis and machine learning.

  • SQL: A query language for relational databases to extract, analyse, and manipulate data.

    
    

5. Data Visualisation Layer

• Purpose: Displays data in an easy-to-understand format.

  Examples:

  • Power BI: A business intelligence tool for creating interactive dashboards and reports.

  • Tableau: A data visualisation platform for creating insightful, interactive visualisations.

  • Looker: A BI tool for exploring and analysing data with powerful visualisation capabilities.

    
    

6. Data Governance and Management Layer

• Purpose: Manages and governs data quality, security, and privacy.

  Examples:

  • Collibra: A platform for data governance and management, including data cataloging and compliance.

  • Informatica: Provides data integration, quality, and governance tools.

  • Alation: A data cataloging tool for organising and finding data across an organisation.

    
    

Each layer of the data stack is essential for ensuring the smooth flow of data from integration to storage, processing, analysis, visualisation, and governance, supporting data-driven decision-making.