Big Data Tools Deep Learning Utilities 
Knowledge Hut
NOVEMBER 2, 2023
Data Aggregation Working with a sample of big data allows you to investigate real-time data processing, big data project design, and data flow. Learn how to aggregate real-time data using several big data tools like Kafka, Zookeeper, Spark, HBase, and Hadoop.
WeCloudData
OCTOBER 19, 2021
With these data tools in place, the WeCloudData team was able to: Process the raw user clickstream data with Python & Spark to develop an array of recommender models. Generate user article recommendations and write the recommendations back to a NoSQL database.
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WeCloudData
OCTOBER 19, 2021
With these data tools in place, the WeCloudData team was able to: Process the raw user clickstream data with Python & Spark to develop an array of recommender models. Generate user article recommendations and write the recommendations back to a NoSQL database.
Knowledge Hut
JUNE 20, 2023
Skills A data engineer should have good programming and analytical skills with big data knowledge. A machine learning engineer should know deep learning, scaling on the cloud, working with APIs, etc. Examples Pull daily tweets from the data warehouse hive spreading in multiple clusters.
ProjectPro
OCTOBER 5, 2018
(Source - [link] ) Master Hadoop Skills by working on interesting Hadoop Projects LinkedIn open-sources a tool to run TensorFlow on Hadoop.Infoworld.com, September 13, 2018. is looking to churn more data in place and share BI analytics of the data within and outside the organization.To Source - [link] ) PREVIOUS <
ProjectPro
APRIL 20, 2017
Problem Statement In this Hadoop project, you can utilize the Yelp Dataset and implement data engineering techniques in Hadoop to learn storage, processing, and fetching datasets. Apply what you have learned, explore a variety of hands-on example projects for data engineers. What is Data Engineering?
ProjectPro
FEBRUARY 25, 2022
The Need for MLOps: Understanding a Data Science Project’s Workflow A data science project involves the below-mentioned steps that you should follow in sequential order. These steps are: Cleaning the data and handling different file formats. Experience with using Big Data tools for a data science project deployment.
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