Remove Apache Kafka Remove Big Data Analytics Remove Blog
article thumbnail

Apache Kafka use cases: Driving innovation across diverse industries

IBM Journey to AI blog

Apache Kafka is an open-source , distributed streaming platform that allows developers to build real-time, event-driven applications. With Apache Kafka, developers can build applications that continuously use streaming data records and deliver real-time experiences to users. How does Apache Kafka work?

article thumbnail

Big Data – Lambda or Kappa Architecture?

Data Science Blog

Big Data Analytics stands apart from conventional data processing in its fundamental nature. In the realm of Big Data, there are two prominent architectural concepts that perplex companies embarking on the construction or restructuring of their Big Data platform: Lambda architecture or Kappa architecture.

Big Data 130
professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

How Netflix Applies Big Data Across Business Verticals: Insights and Strategies

Pickl AI

Introduction Netflix has transformed the entertainment landscape, not just through its vast library of content but also by leveraging Big Data across various business verticals. Data at Rest This includes storage solutions such as S3 Data Warehouse and Cassandra. How Does Netflix Ensure Security Against Fraud?

article thumbnail

Use streaming ingestion with Amazon SageMaker Feature Store and Amazon MSK to make ML-backed decisions in near-real time

AWS Machine Learning Blog

Streaming ingestion – An Amazon Kinesis Data Analytics for Apache Flink application backed by Apache Kafka topics in Amazon Managed Streaming for Apache Kafka (MSK) (Amazon MSK) calculates aggregated features from a transaction stream, and an AWS Lambda function updates the online feature store.

ML 87
article thumbnail

Big Data Syllabus: A Comprehensive Overview

Pickl AI

A well-structured syllabus for Big Data encompasses various aspects, including foundational concepts, technologies, data processing techniques, and real-world applications. This blog aims to provide a comprehensive overview of a typical Big Data syllabus, covering essential topics that aspiring data professionals should master.

article thumbnail

ML Pipeline Architecture Design Patterns (With 10 Real-World Examples)

The MLOps Blog

This blog will answer these questions by exploring the following: 1 What is pipeline architecture and design consideration, and what are the advantages of understanding it? 1 Data Ingestion (e.g., Apache Kafka, Amazon Kinesis) 2 Data Preprocessing (e.g., pandas, NumPy) 3 Feature Engineering and Selection (e.g.,

ML 52
article thumbnail

Predicting the Future of Data Science

Pickl AI

This explosive growth is driven by the increasing volume of data generated daily, with estimates suggesting that by 2025, there will be around 181 zettabytes of data created globally. Real-Time Data Processing The demand for real-time analytics is growing as businesses seek immediate insights to drive decision-making.