How data engineering is used to handle Bigdata?
- Volume:
- Scalable Storage Solutions: Data engineers develop high-performance data center products like data lakes, data warehouses, and distributed file systems to accommodate large data volumes.
- Partitioning and Sharding: They implement partitioning or sharding methods to distribute data across multiple storage nodes for effective management and access.
- Velocity:
- Real-Time Data Processing: Data engineers use stream-oriented platforms such as Apache Kafka and Apache Flink for processing real-time data, enabling rapid data ingestion and processing.
- Buffering and Queuing: They implement data buffering and queuing mechanisms to ensure smooth data flow and manage high-speed data streams effectively.
- Variety:
- Data Transformation: Employing an ETL (extract, transform, load) process, data engineers harmonize different data types into a single, structured format for analysis.
- Schema Management: They oversee schema evolution and maintain data integrity between different databases to ensure consistency across multiple data formats.
- Veracity:
- Data Quality Assurance: Data engineers establish data validation, cleansing, and enrichment procedures to ensure data accuracy and credibility.
- Metadata Management: They build data asset repositories and data catalogs to track data lineage and quality at the data pipeline level.
- Value:
- Data Pipeline Optimization: Data engineers optimize data pipelines to accelerate data intake for faster insights and analysis.
- Collaboration with Data Scientists and Analysts: They work closely with data scientists and analysts to ensure that data sources are cleaned and formatted correctly, facilitating valuable insights and business outcomes.
By addressing these aspects, data engineers play a critical role in enabling organizations to effectively manage and derive insights from Big Data.
What does data engineering mean in the context of big data?
Data engineering is the domain that formulates, designs and implements systems and pipelines that can efficiently converge, divide, and map out vast quantities of data. In this article, we will learn about data engineering in the context of big data.