Dataconomy

FEBRUARY 23, 2023

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 data warehouses, and ensuring that the data is easily accessible and can be analyzed efficiently.

Big Data 91

Big Data Big Data Data Engineering Data Engineering 91

Pickl AI

NOVEMBER 4, 2024

Key Takeaways Data Engineering is vital for transforming raw data into actionable insights. Key components include data modelling, warehousing, pipelines, and integration. Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering?

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Pickl AI

AUGUST 9, 2024

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 data warehouses and how they differ from traditional databases.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

JULY 30, 2024

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 data warehouses or databases. Its visual interface allows users to design complex ETL workflows with ease.

ETL 52

ETL Data Lakes Big Data Big Data 52

Pickl AI

JULY 29, 2024

Data Governance and Security Hadoop clusters often handle sensitive data, making data governance 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.

Hadoop 52

Hadoop Clustering Big Data Big Data 52

phData

SEPTEMBER 27, 2024

Technologies like Apache Kafka, 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.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52