Data science
Video
✨From Raw Data to Real Insights:
Understanding the Journey of a Modern Data
Pipeline
A data pipeline generally consists of a sequence of stages or components that transfer data from its origin to a destination for analysis and utilization.
Here's an overview of the common stages and components in a data pipeline.
1. Collect
- Purpose: Gather raw data from various sources. This data can be generated by applications, sensors, devices, databases, or user interactions.
Components:
- Data Store: Holds operational data, often a database (e.g., relational databases, NoSQL stores).
- Data Stream: Handles real-time data feeds, often using sources like IoT devices, transactional systems, or event logs.
- Application Data: Collects data directly from applications, APIs, or web services.
2. Ingest
Purpose: Move collected data into the pipeline, transforming and consolidating it for further use.
Components:
Data Load: Transfers data from data stores and applications into the processing system.
Event Queue: Manages the flow of data, particularly streaming data, using tools like Apache Kafka or AWS Kinesis.
- Outcome: Data enters the processing layer, often in a more structured format, with consistent formats and time-stamping.
3. Store
Purpose: Persist data so it can be easily
accessed and processed.
- Components:
- Data Lake: A centralized storage repository for large amounts of structured,
semi-structured, and unstructured data.
- Data Warehouse: Structured storage for processed data, optimized for querying and
reporting.
- Lakehouse: Combines elements of data lakes and data warehouses to provide both raw and processed data storage.
- Outcome: Data is stored in various formats (raw, transformed, aggregated) and is accessible for compute and analysis.
4. Compute
Purpose: Process data to prepare it for
analysis and use.
- Components:
- Batch Processing: Periodic processing of large datasets, using frameworks like Apache
Spark or Hadoop.
- Stream Processing: Real-time processing of data streams, often using Apache Flink, Apache Kafka Streams, or AWS Kinesis Data Analytics.
Outcome: Data is processed into usable forms, such as aggregated tables, machine learning features, or transformed datasets.
5. Consume
- Purpose: Deliver data insights and enable its
use across various applications and user groups.
Components:
Data Science, Business Analysis, ML
Understanding the Journey of a Modern Data
Pipeline
A data pipeline generally consists of a sequence of stages or components that transfer data from its origin to a destination for analysis and utilization.
Here's an overview of the common stages and components in a data pipeline.
1. Collect
- Purpose: Gather raw data from various sources. This data can be generated by applications, sensors, devices, databases, or user interactions.
Components:
- Data Store: Holds operational data, often a database (e.g., relational databases, NoSQL stores).
- Data Stream: Handles real-time data feeds, often using sources like IoT devices, transactional systems, or event logs.
- Application Data: Collects data directly from applications, APIs, or web services.
2. Ingest
Purpose: Move collected data into the pipeline, transforming and consolidating it for further use.
Components:
Data Load: Transfers data from data stores and applications into the processing system.
Event Queue: Manages the flow of data, particularly streaming data, using tools like Apache Kafka or AWS Kinesis.
- Outcome: Data enters the processing layer, often in a more structured format, with consistent formats and time-stamping.
3. Store
Purpose: Persist data so it can be easily
accessed and processed.
- Components:
- Data Lake: A centralized storage repository for large amounts of structured,
semi-structured, and unstructured data.
- Data Warehouse: Structured storage for processed data, optimized for querying and
reporting.
- Lakehouse: Combines elements of data lakes and data warehouses to provide both raw and processed data storage.
- Outcome: Data is stored in various formats (raw, transformed, aggregated) and is accessible for compute and analysis.
4. Compute
Purpose: Process data to prepare it for
analysis and use.
- Components:
- Batch Processing: Periodic processing of large datasets, using frameworks like Apache
Spark or Hadoop.
- Stream Processing: Real-time processing of data streams, often using Apache Flink, Apache Kafka Streams, or AWS Kinesis Data Analytics.
Outcome: Data is processed into usable forms, such as aggregated tables, machine learning features, or transformed datasets.
5. Consume
- Purpose: Deliver data insights and enable its
use across various applications and user groups.
Components:
Data Science, Business Analysis, ML