Flipboard

NOVEMBER 21, 2023

In recent years, MathWorks has brought many product offerings into the cloud, especially on Amazon Web Services (AWS). Here is a quick guide on how to run MATLAB on AWS. Installation of AWS Command-Line Interface (AWS CLI) , AWS Configure , and Python3. Set up AWS Configure to interact with AWS resources.

Machine Learning 165

Machine Learning Machine Learning AWS Decision Trees 165

AWS Machine Learning Blog

SEPTEMBER 11, 2024

In this post, we demonstrate how to build a robust real-time anomaly detection solution for streaming time series data using Amazon Managed Service for Apache Flink and other AWS managed services. It offers an AWS CloudFormation template for straightforward deployment in an AWS account.

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AWS ML ML Apache Kafka 108

AWS Machine Learning Blog

OCTOBER 9, 2024

The service, which was launched in March 2021, predates several popular AWS offerings that have anomaly detection, such as Amazon OpenSearch , Amazon CloudWatch , AWS Glue Data Quality , Amazon Redshift ML , and Amazon QuickSight. To use this feature, you can write rules or analyzers and then turn on anomaly detection in AWS Glue ETL.

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AWS ML ML Data Quality 90

AWS Machine Learning Blog

NOVEMBER 3, 2023

m How it’s implemented In our quest to accurately determine shot speed during live matches, we’ve implemented a cutting-edge solution using Amazon Managed Streaming for Apache Kafka (Amazon MSK). We’ve implemented an AWS Lambda function with the specific task of retrieving the calculated shot speed from the relevant Kafka topic.

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AWS Apache Kafka Data Scientist Data Science 119

AWS Machine Learning Blog

OCTOBER 9, 2024

Additionally, you will learn how to configure the Amazon Q Business application and enable user authentication through AWS IAM Identity Center , which is a recommended service for managing a workforce’s access to AWS applications. Permission to access your AWS Secrets Manager secret to authenticate your data source connector instance.

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AWS Apache Kafka Data Scientist Database Administration 126

Women in Big Data

OCTOBER 9, 2024

Data Ingestion: Data is collected and funneled into the pipeline using batch or real-time methods, leveraging tools like Apache Kafka, AWS Kinesis, or custom ETL scripts. This phase ensures quality and consistency using frameworks like Apache Spark or AWS Glue.

Big Data 59

Big Data Big Data Apache Kafka Data Pipeline 59

Data Science Dojo

JULY 24, 2023

Amazon S3: Amazon Simple Storage Service (S3) is a scalable object storage service provided by Amazon Web Services (AWS). It provides fault tolerance and high throughput for Big Data storage and processing. It allows organizations to store and retrieve any amount of data, making it popular for storing and managing Big Data in the cloud.

Big Data 195

Big Data Big Data Data Engineering Data Engineering 195

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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ML ML Apache Kafka SQL 95

AWS Machine Learning Blog

JANUARY 6, 2023

TR wanted to take advantage of AWS managed services where possible to simplify operations and reduce undifferentiated heavy lifting. TR used AWS Glue DataBrew and AWS Batch jobs to perform the extract, transform, and load (ETL) jobs in the ML pipelines, and SageMaker along with Amazon Personalize to tailor the recommendations.

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AWS Data Warehouse ML ML 85

ODSC - Open Data Science

FEBRUARY 6, 2024

Apache Kafka For data engineers dealing with real-time data, Apache Kafka is a game-changer. Spark offers a versatile range of functionalities, from batch processing to stream processing, making it a comprehensive solution for complex data challenges.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

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. petabytes of data.

Big Data 52

Big Data Big Data Apache Kafka Big Data Analytics 52

Pickl AI

JULY 25, 2024

Apache Kafka An open-source platform designed for real-time data streaming. AWS Glue A fully managed ETL service that makes it easy to prepare and load data for analytics. Data Ingestion Tools To facilitate the process, various tools and technologies are available. It provides a user-friendly interface for designing data flows.

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Apache Kafka Data Lakes Data Warehouse Data Quality 52

Pickl AI

NOVEMBER 4, 2024

Among these tools, Apache Hadoop, Apache Spark, and Apache Kafka stand out for their unique capabilities and widespread usage. Apache Hadoop Hadoop is a powerful framework that enables distributed storage and processing of large data sets across clusters of computers.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

DagsHub

APRIL 7, 2024

Also, while it is not a streaming solution, we can still use it for such a purpose if combined with systems such as Apache Kafka. Integration: It can work alongside other workflow orchestration tools (Airflow cluster or AWS SageMaker Pipelines, etc.) Miscellaneous Workflows are created as directed acyclic graphs (DAGs).

Machine Learning 59

Machine Learning Machine Learning ML ML 59

DagsHub

OCTOBER 23, 2024

Apache Kafka Apache Kafka is a distributed event streaming platform for real-time data pipelines and stream processing. Tooling : Apache Tika , ElasticSearch , Databricks , and AWS Glue for metadata extraction and management. It allows unstructured data to be moved and processed easily between systems.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Pickl AI

JULY 20, 2023

Real-time Data Stream Analysis: Use Python with libraries like Apache Kafka and Apache Spark to process and analyze real-time data streams from sources like Twitter, sensors, or website logs. Implement real-time analytics to monitor trends or anomalies in the data.

Analytics 40

Analytics Analytics Big Data Big Data 40

The MLOps Blog

MARCH 15, 2023

Typical examples include: Airbyte Talend Apache Kafka Apache Beam Apache Nifi While getting control over the process is an ideal position an organization wants to be in, the time and effort needed to build such systems are immense and frequently exceeds the license fee of a commercial offering.

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Data Pipeline ETL SQL Data Quality 59

The MLOps Blog

AUGUST 11, 2023

Apache Kafka, Amazon Kinesis) 2 Data Preprocessing (e.g., As usage increased, the system had to be scaled vertically, approaching AWS instance-type limits. Today different stages exist within ML pipelines built to meet technical, industrial, and business requirements. 1 Data Ingestion (e.g.,

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ML ML Machine Learning Machine Learning 52

Pickl AI

DECEMBER 4, 2024

Apache Kafka), organisations can now analyse vast amounts of data as it is generated. Understanding real-time data processing frameworks, such as Apache Kafka, will also enhance your ability to handle dynamic analytics. AWS or Azure) will be increasingly important as more organisations migrate their operations online.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

AWS Machine Learning Blog

FEBRUARY 7, 2025

In this post, we dive deep into how CONXAI hosts the state-of-the-art OneFormer segmentation model on AWS using Amazon Simple Storage Service (Amazon S3), Amazon Elastic Kubernetes Service (Amazon EKS), KServe, and NVIDIA Triton. Our journey to AWS Initially, CONXAI started with a small cloud provider specializing in offering affordable GPUs.

Analytics 66

Analytics Analytics AWS Clustering 66

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. Apache Spark Apache Spark is a powerful data processing framework that efficiently handles Big Data. Which cloud-based data engineering tools are most popular?

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 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. Statistics : According to AWS reports, EMR reduces the time required for Big Data processing tasks by up to 90% compared to traditional methods.

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Big Data Big Data Apache Hadoop Apache Kafka 52