Dataconomy

FEBRUARY 23, 2023

Creating data pipelines and workflows Data engineers create data pipelines and workflows that enable data to be collected, processed, and analyzed efficiently. By creating efficient data pipelines and workflows, data engineers enable organizations to make data-driven decisions quickly and accurately.

Big Data 91

Big Data Big Data Data Engineer 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 Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Pickl AI

JULY 30, 2024

It can ingest data in real-time or batch mode, making it an ideal solution for organizations looking to centralize their data collection processes. Its visual interface allows users to design complex ETL workflows with ease. Apache NiFi is used for automating the flow of data between systems.

ETL 52

ETL Data Lakes Big Data Big Data 52

Pickl AI

AUGUST 9, 2024

Data Integration Tools Technologies such as Apache NiFi and Talend help in the seamless integration of data from various sources into a unified system for analysis. Understanding ETL (Extract, Transform, Load) processes is vital for students. Once data is collected, it needs to be stored efficiently.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

DagsHub

APRIL 7, 2024

Flexibility: Its use cases are wider than just machine learning; for example, we can use it to set up ETL pipelines. Also, while it is not a streaming solution, we can still use it for such a purpose if combined with systems such as Apache Kafka. Miscellaneous Workflows are created as directed acyclic graphs (DAGs).

Machine Learning 59

Machine Learning Machine Learning ML ML 59

DagsHub

OCTOBER 23, 2024

Data Processing Tools These tools are essential for handling large volumes of unstructured data. They assist in efficiently managing and processing data from multiple sources, ensuring smooth integration and analysis across diverse formats. It allows unstructured data to be moved and processed easily between systems.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 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. If the event log is your customer’s diary, think of persistent staging as their scrapbook – a place where raw customer data is collected, organized, and kept for future reference.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52