AWS Machine Learning Blog

MARCH 10, 2023

We show how you can apply this approach to various data streaming and event-driven architectures, depending on the desired outcome and actions to take to prevent fraud (such as alert the user about the fraud or flag the transaction for additional review). The user can then choose to take action to prevent further abuse.

Machine Learning 144

Machine Learning Machine Learning AWS Apache Kafka 144

Data Science Dojo

JULY 24, 2023

Its characteristics can be summarized as follows: Volume : Big Data involves datasets that are too large to be processed by traditional database management systems. databases), semi-structured data (e.g., These datasets can range from terabytes to petabytes and beyond. XML, JSON), and unstructured data (e.g., text, images, videos).

Big Data 195

Big Data Big Data Data Engineering Data Engineering 195

Precisely

DECEMBER 28, 2023

Streaming data pipelines, by extension, offer an architecture capable of handling large volumes of data, accommodating millions of events in near real time. One very popular platform is Apache Kafka , a powerful open-source tool used by thousands of companies. You need a separate tool to do that.

Data Pipeline 91

Data Pipeline Apache Kafka Big Data Big Data 91

IBM Journey to AI blog

AUGUST 29, 2024

Event-driven businesses across all industries thrive on real-time data, enabling companies to act on events as they happen rather than after the fact. This is where Apache Flink shines, offering a powerful solution to harness the full potential of an event-driven business model through efficient computing and processing capabilities.

Apache Kafka 45

Apache Kafka Hadoop ETL Data Pipeline 45

ODSC - Open Data Science

JUNE 1, 2023

To understand what it means, we should start by thinking of the world in terms of events, where an event is a thing that happens. And we are going to take those events, become aware of them, and understand them. Stores events in a durable manner so that downstream components can process them.

Analytics 98

Analytics Analytics Apache Kafka Data Science 98

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. p8 -pubout -out C:tmpnew_rsa_key_v1.pub

Apache Kafka 52

Apache Kafka Analytics Analytics ETL 52

AWS Machine Learning Blog

NOVEMBER 3, 2023

This process comprises two key components: event data and optical tracking data. Event data collection entails gathering the fundamental building blocks of the game. For the precision needed in shot speed calculations, we must ensure that the ball’s position aligns precisely with the moment of the event.

AWS 128

AWS Apache Kafka Data Scientist Data Science 128

AWS Machine Learning Blog

SEPTEMBER 11, 2024

It initially sources input time series data from Amazon Managed Streaming for Apache Kafka (Amazon MSK) using this live stream for model training. Conclusion This post demonstrated how to build a robust real-time anomaly detection solution for streaming time series data using Managed Service for Apache Flink and other AWS services.

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AWS ML ML Apache Kafka 119

AWS Machine Learning Blog

MARCH 30, 2023

How Keeper Efficiency is implemented This Bundesliga Match Fact consumes both event and positional data. Event data consists of hand-labelled event descriptions with useful attributes, such as shot on target. This frame is used to synchronize the event data with the positional data.

Machine Learning 104

Machine Learning Machine Learning AWS ML 104

Pickl AI

SEPTEMBER 18, 2024

It utilises Amazon Web Services (AWS) as its main data lake, processing over 550 billion events daily—equivalent to approximately 1.3 Data in Motion Technologies like Apache Kafka facilitate real-time processing of events and data, allowing Netflix to respond swiftly to user interactions and operational needs.

Big Data 52

Big Data Big Data Apache Kafka Big Data Analytics 52

The MLOps Blog

FEBRUARY 5, 2023

Streaming data is a continuous flow of information and a foundation of event-driven architecture software model” – RedHat Enterprises around the world are becoming dependent on data more than ever. A streaming data pipeline is an enhanced version which is able to handle millions of events in real-time at scale.

Machine Learning 72

Machine Learning Machine Learning Data Pipeline Apache Kafka 72

Mlearning.ai

MAY 16, 2023

It is used to extract data from various sources, transform the data to fit a specific data model or schema, and then load the transformed data into a target system such as a data warehouse or a database. The company can use the Pub/Sub pattern to process customer events such as product views, add to cart, and checkout.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Pickl AI

JULY 30, 2024

Guaranteed Delivery : NiFi ensures that data delivered reliably, even in the event of failures. It maintains a write-ahead log to ensure that the state of FlowFiles preserved, even in the event of a failure. Provenance Repository : This repository records all provenance events related to FlowFiles.

ETL 52

ETL Data Lakes Big Data Big Data 52

Pickl AI

NOVEMBER 4, 2024

They are responsible for building and maintaining data architectures, which include databases, data warehouses, and data lakes. Data Modelling Data modelling is creating a visual representation of a system or database. Physical Models: These models specify how data will be physically stored in databases.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

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. It often involves specialized databases designed to handle this kind of atomic, temporal data.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52

DagsHub

OCTOBER 23, 2024

Data can come from different sources, such as databases or directly from users, with additional sources, including platforms like GitHub, Notion, or S3 buckets. Vector Databases Vector databases help store unstructured data by storing the actual data and its vector representation. mp4,webm, etc.), and audio files (.wav,mp3,acc,

Machine Learning 52

Machine Learning Machine Learning AI Data Lakes 52

Pickl AI

AUGUST 9, 2024

Variety It encompasses the different types of data, including structured data (like databases), semi-structured data (like XML), and unstructured formats (such as text, images, and videos). Understanding the differences between SQL and NoSQL databases is crucial for students. Once data is collected, it needs to be stored efficiently.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

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. Data Pipeline Tool Key Features Apache Airflow Flexible, customizable, and supports complex business logic. Relational database connectors are available. Talend Free to use.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

AWS Machine Learning Blog

APRIL 19, 2023

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 98

ML ML Apache Kafka SQL 98

The MLOps Blog

AUGUST 11, 2023

Apache Kafka, Amazon Kinesis) 2 Data Preprocessing (e.g., The exploration of common machine learning pipeline architecture and patterns starts with a pattern found in not just machine learning systems but also database systems, streaming platforms, web applications, and modern computing infrastructure. 1 Data Ingestion (e.g.,

ML 52

ML ML Machine Learning Machine Learning 52

Pickl AI

MARCH 19, 2025

Python, SQL, and Apache Spark are essential for data engineering workflows. Real-time data processing with Apache Kafka enables faster decision-making. A data engineer creates and manages the pipelines that transfer data from different sources to databases or cloud storage. What Does a Data Engineer Do?

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Pickl AI

FEBRUARY 23, 2025

Best Big Data Tools Popular tools such as Apache Hadoop, Apache Spark, Apache Kafka, and Apache Storm enable businesses to store, process, and analyse data efficiently. Real-Time Data Analysis: Connects seamlessly with various databases for live analysis.

Big Data 52

Big Data Big Data Apache Hadoop Apache Kafka 52

AWS Machine Learning Blog

FEBRUARY 7, 2025

However, it lacked essential services required for machine learning (ML) applications, such as frontend and backend infrastructure, DNS, load balancers, scaling, blob storage, and managed databases. At that time, the application was deployed as a single monolithic container, which included Kafka and a database.

Analytics 70

Analytics Analytics AWS Clustering 70