Smart Data Collective

AUGUST 17, 2022

You can safely use an Apache Kafka cluster for seamless data movement from the on-premise hardware solution to the data lake using various cloud services like Amazon’s S3 and others. 5 Key Comparisons in Different Apache Kafka Architectures. 5 Key Comparisons in Different Apache Kafka Architectures.

Apache Kafka 130

Apache Kafka ETL Data Lakes AWS 130

phData

MAY 3, 2024

How Snowflake Helps Achieve Real-Time Analytics Snowflake is the ideal platform to achieve real-time analytics for several reasons, but two of the biggest are its ability to manage concurrency due to the multi-cluster architecture of Snowflake and its robust connections to 3rd party tools like Kafka. Looking for additional help?

Analytics 52

Analytics Apache Kafka Analytics ETL 52

AWS Machine Learning Blog

OCTOBER 9, 2024

To use this feature, you can write rules or analyzers and then turn on anomaly detection in AWS Glue ETL. To learn more about the beta offering, see Anomaly detection in streaming time series data with online learning using Amazon Managed Service for Apache Flink. How do I delete my Amazon Lookout for Metrics resources? Choose Delete.

AWS 96

AWS ML ML Data Quality 96

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.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Pickl AI

JULY 30, 2024

Scalability : NiFi can be deployed in a clustered environment, enabling organizations to scale their data processing capabilities as their data needs grow. 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

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.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

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. 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.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52