Women in Big Data

OCTOBER 9, 2024

Big data pipelines operate similarly to traditional ETL (Extract, Transform, Load) pipelines but are designed to handle much larger data volumes. Data Ingestion: Data is collected and funneled into the pipeline using batch or real-time methods, leveraging tools like Apache Kafka, AWS Kinesis, or custom ETL scripts.

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IBM Journey to AI blog

AUGUST 29, 2024

The unique advantages of Apache Flink Apache Flink augments event streaming technologies like Apache Kafka to enable businesses to respond to events more effectively in real time. Integration: Integrates seamlessly with other data systems and platforms, including Apache Kafka, Spark, Hadoop and various databases.

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Pickl AI

NOVEMBER 4, 2024

Key components of data warehousing include: ETL Processes: ETL stands for Extract, Transform, Load. ETL is vital for ensuring data quality and integrity. Among these tools, Apache Hadoop, Apache Spark, and Apache Kafka stand out for their unique capabilities and widespread usage.

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Mlearning.ai

MAY 16, 2023

ETL Design Pattern The ETL (Extract, Transform, Load) design pattern is a commonly used pattern in data engineering. ETL Design Pattern Here is an example of how the ETL design pattern can be used in a real-world scenario: A healthcare organization wants to analyze patient data to improve patient outcomes and operational efficiency.

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Pickl AI

AUGUST 9, 2024

Some of the most notable technologies include: Hadoop An open-source framework that allows for distributed storage and processing of large datasets across clusters of computers. It is built on the Hadoop Distributed File System (HDFS) and utilises MapReduce for data processing. Once data is collected, it needs to be stored efficiently.

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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.

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Dataconomy

FEBRUARY 23, 2023

This involves working with various tools and technologies, such as ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes, to move data from its source to its destination. By creating efficient data pipelines and workflows, data engineers enable organizations to make data-driven decisions quickly and accurately.

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