DagsHub

OCTOBER 23, 2024

Managing unstructured data is essential for the success of machine learning (ML) projects. Popular data lake solutions include Amazon S3 , Azure Data Lake , and Hadoop. Apache Kafka Apache Kafka is a distributed event streaming platform for real-time data pipelines and stream processing.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Alation

AUGUST 4, 2022

Big data got“ more leaders and people in the organization to use data, analytics, and machine learning in their decision making,” says former CIO Isaac Sacolick. “Setting up Hadoop on-premises was a huge undertaking. Spark, Tensorflow, Apache Kafka, et cetera, are all out found in cloud databases,” points out Jones.

Big Data 95

Big Data Big Data Apache Kafka Data Lakes 95

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.

Analytics 98

Analytics Analytics Apache Kafka Data Science 98

Pickl AI

AUGUST 9, 2024

Some of the most notable technologies include: Hadoop An open-source framework that allows for distributed storage and processing of large datasets across clusters of computers. It is built on the Hadoop Distributed File System (HDFS) and utilises MapReduce for data processing. Once data is collected, it needs to be stored efficiently.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

NOVEMBER 4, 2024

On the other hand, Data Science involves extracting insights and knowledge from data using Statistical Analysis, Machine Learning, and other techniques. Among these tools, Apache Hadoop, Apache Spark, and Apache Kafka stand out for their unique capabilities and widespread usage.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Pickl AI

DECEMBER 4, 2024

Summary: The future of Data Science is shaped by emerging trends such as advanced AI and Machine Learning, augmented analytics, and automated processes. Continuous learning and adaptation will be essential for data professionals. Automated Machine Learning (AutoML) will democratize access to Data Science tools and techniques.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Pickl AI

DECEMBER 2, 2024

Processing frameworks like Hadoop enable efficient data analysis across clusters. Distributed File Systems: Technologies such as Hadoop Distributed File System (HDFS) distribute data across multiple machines to ensure fault tolerance and scalability. Data lakes and cloud storage provide scalable solutions for large datasets.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

Pickl AI

NOVEMBER 25, 2024

Processing frameworks like Hadoop enable efficient data analysis across clusters. Distributed File Systems: Technologies such as Hadoop Distributed File System (HDFS) distribute data across multiple machines to ensure fault tolerance and scalability. Data lakes and cloud storage provide scalable solutions for large datasets.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

Dataconomy

FEBRUARY 23, 2023

Some of these solutions include: Distributed computing: Distributed computing systems, such as Hadoop and Spark, can help distribute the processing of data across multiple nodes in a cluster. Solutions for managing and processing large volumes of data Data engineers can use various solutions to manage and process large volumes of data.

Big Data 91

Big Data Big Data Data Engineering Data Engineering 91

Mlearning.ai

MAY 16, 2023

The events can be published to a message broker such as Apache Kafka or Google Cloud Pub/Sub. The message broker can then distribute the events to various subscribers such as data processing pipelines, machine learning models, and real-time analytics dashboards.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Pickl AI

JULY 20, 2023

Techniques like regression analysis, time series forecasting, and machine learning algorithms are used to predict customer behavior, sales trends, equipment failure, and more. Use machine learning algorithms to build a fraud detection model and identify potentially fraudulent transactions.

Analytics 40

Analytics Analytics Big Data Big Data 40