Data Engineering Lifecycle

1. Why Do We Need a Lifecycle?

• Data cannot simply be taken from one source and given to a Data Science team.
• There must be a step-by-step approach to ensure the data pipeline serves a meaningful purpose.
• This structured approach is called the Data Engineering Lifecycle.

2. The Data Engineering Lifecycle Stages

1. Data Generation

• Data is generated from multiple sources such as:
  • APIs (e.g., fetching data from online services).
  • Databases (RDBMS) (e.g., transactional data).
  • Sensors (e.g., IoT devices, vehicle trackers).
  • Analytics Tools (e.g., Google Analytics, log data).

2. Data Ingestion

• Once data is generated, it must be collected and ingested into the system.

• This involves setting up connections to:

  • APIs
  • Databases (RDBMS)
  • Sensors and real-time data sources

• Purpose of Ingestion: Ensures that whenever new data is created, it is automatically collected for processing.

3. Data Storage

• After ingestion, data must be stored properly.
• Storage options include:
  • Relational Databases (RDBMS) – PostgreSQL, MySQL, Microsoft SQL Server
  • NoSQL Databases – MongoDB, Cassandra
  • Data Warehouses – Snowflake, Amazon Redshift
  • Data Lakes – Amazon S3, Google Cloud Storage

4. Data Transformation

• Raw data is often messy and inconsistent.

• Data transformation involves:

  • Cleaning, filtering, and formatting the data.
  • Converting different formats (e.g., changing date formats).
  • Removing duplicates and handling missing values.
  • Combining data from multiple sources.

• Example:

  • API data might have a date format as YYYY-MM-DD.
  • Database data might store the date as MM-DD-YYYY.
  • During transformation, dates should be converted into a consistent format.

• Common transformation tools:

  • Python (Pandas, PySpark)
  • SQL (for filtering and aggregating data)
  • Hadoop, Spark (for large-scale processing)

5. Data Serving

• Once data is transformed and cleaned, it is sent to different teams for use:

  • Data Science & Machine Learning Teams – Use data for predictions and AI models.
  • Business Intelligence (BI) Teams – Use data for reports and dashboards.
  • Data Analysts – Use data for insights and decision-making.

• The goal is to ensure that transformed data is accessible and useful for different business needs.

---

3. Understanding Data Transformation in Detail

• Transformation is the heart of Data Engineering.

• It includes:

  • Formatting Data (e.g., converting date formats).
  • Data Cleaning (e.g., removing duplicates and handling null values).
  • Data Aggregation (e.g., calculating total sales per month).
  • Joining Data (e.g., merging product data with order data).

• Example:

  • Data from an API may store product purchase dates differently from a database.
  • To analyze purchases over time, these dates must be converted to a common format.
  • This process is handled in the transformation layer before data is used.

---

4. Why is Data Engineering Important?

• Without clean and structured data, businesses cannot make informed decisions.
• Data Engineers ensure that data is:
  • Accurate (free from errors).
  • Well-organized (easy to access and analyze).
  • Useful (can be used for reports, AI models, and dashboards).

5. Summary of the Data Engineering Lifecycle

Step	Purpose	Example Tools
Data Generation	Collecting raw data from various sources.	APIs, Sensors, RDBMS
Data Ingestion	Bringing data into a central system.	Kafka, Airflow
Data Storage	Storing data for further processing.	PostgreSQL, Snowflake
Data Transformation	Cleaning and structuring the data.	Pandas, Spark, SQL
Data Serving	Making the data accessible to users.	BI Tools, Dashboards