Data Sources

These are the places `where` your data originates. It can be anything from databases, SaaS applications, in-house applications, IoT devices, third-party APIs, etc.

Data Ingestion

This involves extracting data from the sources and loading it into the data pipeline. Tools for this could include Apache Kafka, Logstash, or custom-built scripts.

Data Storage (Data Lake or Data Warehouse)

After ingestion, data is stored in a data warehouse or data lake. A data warehouse, such as Google BigQuery or Amazon Redshift, is used for structured, processed data. A data lake, such as Amazon S3 or Google Cloud Storage, can store raw, unprocessed data in its native format.

Data Processing and Transformation (ETL)

1 This is where raw data is cleaned, transformed, and structured for analysis. 2 This process is also known as ETL (Extract, Transform, Load). Tools for this could include Apache Beam, Apache Airflow, or Talend.

Data Analysis

Once data is processed and structured, it can be analyzed using various data analysis tools like SQL, Python, R, or even Excel.

Data Visualization and BI Tools

The results of the data analysis are often visualized using tools like Tableau, Looker, or PowerBI. These tools help non-technical users to understand and interpret the data.

Machine Learning and Advanced Analytics

    For more advanced use cases, data can be further processed and analyzed using machine learning algorithms and tools like
  • TensorFlow,
  • PyTorch, or
  • Scikit-learn.