ODSC - Open Data Science

JUNE 1, 2023

We’re going to assume that the pizza service already captures orders in Apache Kafka and is also keeping a record of its customers and the products that they sell in MySQL. Apache Pinot is a real-time OLAP database built at LinkedIn to deliver scalable real-time analytics with low latency. He tweets at @markhneedham.

Analytics 98

Analytics Analytics Apache Kafka Data Science 98

IBM Journey to AI blog

NOVEMBER 29, 2023

IBM Event Automation is a fully composable solution, built on open technologies, with capabilities for: Event streaming : Collect and distribute raw streams of real-time business events with enterprise-grade Apache Kafka. Event endpoint management : Describe and document events easily according to the Async API specification.

Artificial Intelligence 105

Artificial Intelligence Artificial Intelligence Apache Kafka AI 105

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 95

ML ML Apache Kafka SQL 95

IBM Journey to AI blog

AUGUST 22, 2024

In recognizing the benefits of event-driven architectures, many companies have turned to Apache Kafka for their event streaming needs. Apache Kafka enables scalable, fault-tolerant and real-time processing of streams of data—but how do you manage and properly utilize the sheer amount of data your business ingests every second?

EDA 40

EDA Apache Kafka Clustering 40

IBM Journey to AI blog

JULY 12, 2024

IBM® Event Automation’s event endpoint management capability makes it easy to describe and document your Kafka topics (event sources) according to the open source AsyncAPI Specification. Why is this important? AsyncAPI already fuels clarity, standardization, interoperability, real-time responsiveness and beyond.

Apache Kafka 40

Apache Kafka Clustering 40

Alation

AUGUST 4, 2022

Spark, Tensorflow, Apache Kafka, et cetera, are all out found in cloud databases,” points out Jones. In the cloud], Graph databases, document stores, file stores, relational stores all now exist, each addressing different challenges.” [You can] see that it works before going all-in.”.

Big Data 95

Big Data Big Data Apache Kafka Data Lakes 95

DagsHub

OCTOBER 23, 2024

For instance, if the collected data was a text document in the form of a PDF, the data preprocessing—or preparation stage —can extract tables from this document. The pipeline in this stage can convert the document into CSV files, and you can then analyze it using a tool like Pandas. Unstructured.io

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 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. With the advent of technologies like edge computing and stream processing frameworks (e.g.,

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

DagsHub

APRIL 7, 2024

This also means that it comes with a large community and comprehensive documentation. 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. Also, it is a bit more difficult to find resources online other than the official documentation.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Pickl AI

DECEMBER 2, 2024

This includes structured data (like databases), semi-structured data (like XML files), and unstructured data (like text documents and videos). Variety Variety indicates the different types of data being generated. For instance, Netflix uses diverse data types—from user viewing habits to movie metadata—to provide personalised recommendations.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

Pickl AI

NOVEMBER 25, 2024

This includes structured data (like databases), semi-structured data (like XML files), and unstructured data (like text documents and videos). Variety Variety indicates the different types of data being generated. For instance, Netflix uses diverse data types—from user viewing habits to movie metadata—to provide personalised recommendations.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

DagsHub

JANUARY 14, 2025

Marketing materials, news stories, and business documents are frequently repeated or reposted, producing exact replicas. N-Gram based matching: It compares text documents by breaking them down into overlapping sequences of N contiguous words (N-Grams). For more information please refer to the source document.

Machine Learning 52

Machine Learning Machine Learning Clustering Algorithm 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. MongoDB MongoDB is a NoSQL database that stores data in flexible, JSON-like documents. Cloud-based tools like Snowflake and BigQuery enhance scalability and performance.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Pickl AI

FEBRUARY 23, 2025

Evaluate Community Support and Documentation A strong community around a tool often indicates reliability and ongoing development. Evaluate the availability of resources such as documentation, tutorials, forums, and user communities that can assist you in troubleshooting issues or learning how to maximize tool functionality.

Big Data 52

Big Data Big Data Apache Hadoop Apache Kafka 52