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Collecting, storing, and processing large datasets Data engineers are also responsible for collecting, storing, and processing large volumes of data. This involves working with various data storage technologies, such as databases and datawarehouses, and ensuring that the data is easily accessible and can be analyzed efficiently.
Key Takeaways Data Engineering is vital for transforming raw data into actionable insights. Key components include data modelling, warehousing, pipelines, and integration. Effective datagovernance enhances quality and security throughout the data lifecycle. What is Data Engineering?
Data Warehousing Solutions Tools like Amazon Redshift, Google BigQuery, and Snowflake enable organisations to store and analyse large volumes of data efficiently. Students should learn about the architecture of datawarehouses and how they differ from traditional databases.
ETL (Extract, Transform, Load) Processes Apache NiFi can streamline ETL processes by extracting data from multiple sources, transforming it into the desired format, and loading it into target systems such as datawarehouses or databases. Its visual interface allows users to design complex ETL workflows with ease.
DataGovernance and Security Hadoop clusters often handle sensitive data, making datagovernance and security a significant concern. Ensuring compliance with regulations such as GDPR or HIPAA requires implementing robust security measures, including data encryption, access controls, and auditing capabilities.
Technologies like ApacheKafka, often used in modern CDPs, use log-based approaches to stream customer events between systems in real-time. Here’s how a composable CDP might incorporate the modeling approaches we’ve discussed: Data Storage and Processing : This is your foundation.
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