Data Science Dojo

JULY 24, 2023

Different algorithms and techniques are employed to achieve eventual consistency. Unlike traditional batch processing, where data is processed in fixed intervals, stream processing enables organizations to gain insights and respond to events as they happen in real-time. They use redundancy and replication to ensure data availability.

Big Data 195

Big Data Big Data Data Engineering Data Engineering 195

AWS Machine Learning Blog

SEPTEMBER 11, 2024

The application, once deployed, constructs an ML model using the Random Cut Forest (RCF) algorithm. It initially sources input time series data from Amazon Managed Streaming for Apache Kafka (Amazon MSK) using this live stream for model training. You can follow him on linkedin, syedfurqhan Nirmal Kumar is Sr.

AWS 104

AWS ML ML Apache Kafka 104

AWS Machine Learning Blog

APRIL 19, 2023

We use Amazon SageMaker to train a model using the built-in XGBoost algorithm on aggregated features created from historical transactions. An event time feature is also required, which enables the feature store to track the history of feature values over time. The feature groups for our use case are shown in the following table.

ML 89

ML ML Apache Kafka SQL 89

phData

SEPTEMBER 27, 2024

In this guide, we will explore concepts like transitional modeling for customer profiles, the power of event logs for customer behavior, persistent staging for raw customer data, real-time customer data capture, and much more. Rich Context: Each event carries with it a wealth of contextual information. What is Activity Schema Modeling?

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52

Pickl AI

AUGUST 9, 2024

Data Streaming Learning about real-time data collection methods using tools like Apache Kafka and Amazon Kinesis. Students should understand the concepts of event-driven architecture and stream processing. Students should learn how to leverage Machine Learning algorithms to extract insights from large datasets.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

JULY 20, 2023

Diagnostic Analytics Projects: Diagnostic analytics seeks to determine the reasons behind specific events or patterns observed in the data. 3. Predictive Analytics Projects: Predictive analytics involves using historical data to predict future events or outcomes. Root cause analysis is a typical diagnostic analytics task.

Analytics 40

Analytics Analytics Big Data Big Data 40

DagsHub

OCTOBER 23, 2024

Apache Kafka Apache Kafka is a distributed event streaming platform for real-time data pipelines and stream processing. Kafka is highly scalable and ideal for high-throughput and low-latency data pipeline applications. It allows unstructured data to be moved and processed easily between systems.

Machine Learning 52

Machine Learning Machine Learning AI AI 52

DagsHub

APRIL 7, 2024

Image generated with Midjourney In today’s fast-paced world of data science, building impactful machine learning models relies on much more than selecting the best algorithm for the job. Flexibility: Airflow was designed with batch workflows in mind; it was not meant for permanently running event-based workflows.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

The MLOps Blog

MARCH 15, 2023

Data Ingestion : Involves raw data collection from origin and storage using architectures such as batch, streaming or event-driven. The logical flow of running upstream and downstream tasks is decided using an algorithm commonly known as a Directed Acyclic Graph (DAG). Pricing Up to a million events/month on the free plan.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59