Mlearning.ai

MAY 16, 2023

Data engineering is a rapidly growing field that designs and develops systems that process and manage large amounts of data. There are various architectural design patterns in data engineering that are used to solve different data-related problems.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

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.

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The MLOps Blog

FEBRUARY 5, 2023

In the later part of this article, we will discuss its importance and how we can use machine learning for streaming data analysis with the help of a hands-on example. What is streaming data? This will also help us observe the importance of stream data. It can be used to collect, store, and process streaming data in real-time.

Machine Learning 72

Machine Learning Machine Learning Data Pipeline Apache Kafka 72

DagsHub

OCTOBER 23, 2024

General Purpose Tools These tools help manage the unstructured data pipeline to varying degrees, with some encompassing data collection, storage, processing, analysis, and visualization. DagsHub's Data Engine DagsHub's Data Engine is a centralized platform for teams to manage and use their datasets effectively.

Machine Learning 52

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DagsHub

JANUARY 14, 2025

Similar Audio: Audio recordings of the same event or sound but with different microphone placements or background noise. Design diagram of PDDMAL DagsHub makes active learning easier by combining tools like Data Engine, MLflow, and Label Studio. Tools like Apache Kafka and Apache Flink can be configured for this purpose.

Machine Learning 52

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Data Science Dojo

MARCH 9, 2023

It requires minimal operational maintenance and allows for rapid development, resulting in significant cost savings and reduced development time for data-focused developers and engineers. Memphis has a simple UI, CLI, and SDKs, and offers features like automatic message retransmitting, storage tiering, and data-level observability.

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Apache Kafka Azure Data Science Data Pipeline 323

phData

SEPTEMBER 27, 2024

Methods that allow our customer data models to be as dynamic and flexible as the customers they represent. 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.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52

The MLOps Blog

AUGUST 11, 2023

1 Data Ingestion (e.g., Apache Kafka, Amazon Kinesis) 2 Data Preprocessing (e.g., pandas, NumPy) 3 Feature Engineering and Selection (e.g., 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

ML ML Machine Learning Machine Learning 52

AWS Machine Learning Blog

APRIL 18, 2025

Solution overview: Build a generative AI stock price analyzer with RAG For this post, we implement a RAG architecture with Amazon Bedrock Knowledge Bases using a custom connector and topics built with Amazon Managed Streaming for Apache Kafka (Amazon MSK) for a user who may be interested to understand stock price trends.

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Apache Kafka AWS Clustering Database 94

Pickl AI

MARCH 19, 2025

Summary: Data engineering tools streamline data collection, storage, and processing. Tools like Python, SQL, Apache Spark, and Snowflake help engineers automate workflows and improve efficiency. Learning these tools is crucial for building scalable data pipelines. Thats where data engineering tools come in!

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Pickl AI

MARCH 13, 2025

RabbitMQ ensures reliable, structured message delivery, while Kafka excels in real-time, high-volume data streaming. Choosing between them depends on your systems needsRabbitMQ is best for workflows, while Kafka is ideal for event-driven architectures and big data processing. Thats where message brokers come in.

Apache Kafka 52

Apache Kafka Big Data Big Data Data Pipeline 52