Dataconomy

JULY 29, 2024

Talks and insights Mikhail Epikhin: Navigating the processor landscape for Apache Kafka Mikhail Epikhin began the session by sharing his team’s research on optimizing Managed Service for Apache Kafka. His presentation focused on the performance and efficiency of different instance types and processor architectures.

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Apache Kafka Data Pipeline Data Warehouse ETL 91

Women in Big Data

OCTOBER 9, 2024

A traditional data pipeline is a structured process that begins with gathering data from various sources and loading it into a data warehouse or data lake. Once ingested, the data is prepared through filtering, error correction, and restructuring for ease of use.

Big Data 59

Big Data Big Data Apache Kafka Data Pipeline 59

Pickl AI

NOVEMBER 4, 2024

Role of Data Engineers in the Data Ecosystem Data Engineers play a crucial role in the data ecosystem by bridging the gap between raw data and actionable insights. They are responsible for building and maintaining data architectures, which include databases, data warehouses, and data lakes.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Pickl AI

JULY 25, 2024

In this blog, we’ll delve into the intricacies of data ingestion, exploring its challenges, best practices, and the tools that can help you harness the full potential of your data. Batch Processing In this method, data is collected over a period and then processed in groups or batches. The post What is Data Ingestion?

Apache Kafka 52

Apache Kafka Data Lakes Data Warehouse Data Quality 52

Mlearning.ai

MAY 16, 2023

This article discusses five commonly used architectural design patterns in data engineering and their use cases. ETL Design Pattern The ETL (Extract, Transform, Load) design pattern is a commonly used pattern in data engineering. Finally, the transformed data is loaded into the target system.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

AWS Machine Learning Blog

OCTOBER 9, 2024

Using Amazon Redshift ML for anomaly detection Amazon Redshift ML makes it easy to create, train, and apply machine learning models using familiar SQL commands in Amazon Redshift data warehouses. To capture unanticipated, less obvious data patterns, you can enable anomaly detection.

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AWS ML ML Data Quality 81

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