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

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.

Big Data 59

Big Data Big Data Apache Kafka Data Pipeline 59

AWS Machine Learning Blog

OCTOBER 9, 2024

The service, which was launched in March 2021, predates several popular AWS offerings that have anomaly detection, such as Amazon OpenSearch , Amazon CloudWatch , AWS Glue Data Quality , Amazon Redshift ML , and Amazon QuickSight. To use this feature, you can write rules or analyzers and then turn on anomaly detection in AWS Glue ETL.

AWS 91

AWS ML ML Data Quality 91

AWS Machine Learning Blog

JANUARY 6, 2023

TR wanted to take advantage of AWS managed services where possible to simplify operations and reduce undifferentiated heavy lifting. TR used AWS Glue DataBrew and AWS Batch jobs to perform the extract, transform, and load (ETL) jobs in the ML pipelines, and SageMaker along with Amazon Personalize to tailor the recommendations.

AWS 86

AWS Data Warehouse ML ML 86

Pickl AI

JULY 25, 2024

Apache Kafka An open-source platform designed for real-time data streaming. AWS Glue A fully managed ETL service that makes it easy to prepare and load data for analytics. Data Ingestion Tools To facilitate the process, various tools and technologies are available. It supports both batch and real-time processing.

Apache Kafka 52

Apache Kafka Data Lakes Data Warehouse Data Quality 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 Engineer Data Engineering Data Engineering 52

The MLOps Blog

MARCH 15, 2023

Typical examples include: Airbyte Talend Apache Kafka Apache Beam Apache Nifi While getting control over the process is an ideal position an organization wants to be in, the time and effort needed to build such systems are immense and frequently exceeds the license fee of a commercial offering.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59