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Seamless Data Analytics Workflow: From Dockerized JupyterLab and MinIO to Insights with Spark SQL

Towards Data Science

Photo by Ian Taylor on Unsplash This tutorial guides you through an analytics use case, analyzing semi-structured data with Spark SQL. We’ll start with the data engineering process, pulling data from an API and finally loading the transformed data into a data lake (represented by MinIO ).

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Building a Machine Learning Application With Cloudera Data Science Workbench And Operational Database, Part 3: Productionization of ML models

Cloudera

to make a classification model based off of training data stored in both Cloudera’s Operational Database (powered by Apache HBase) and Apache HDFS. Using PySpark and Apache HBase, Part 1 and Using PySpark and Apache HBase, Part 2. One big use case is with sensor data. Training Data in HBase and HDFS.

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Data News — Week 24.12

Christophe Blefari

Friday routine ( credits ) It's Friday and it's Data News. I don't go into too much detail about the magic of Data News, but every Friday is the same. Exploration, Friday morning I read the last 7 days of 2 Twitter lists ( MDS , Data voices ) and I open interesting stuff in tabs. on April 10.

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A Beginner’s Guide to Learning PySpark for Big Data Processing

ProjectPro

Did you know that, according to Linkedin, over 24,000 Big Data jobs in the US list Apache Spark as a required skill? Learning Spark has become more of a necessity to enter the Big Data industry. Python is one of the most extensively used programming languages for Data Analysis, Machine Learning , and data science tasks.

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Python for Data Engineering

Ascend.io

The rise of data-intensive operations has positioned data engineering at the core of today’s organizations. As the demand to efficiently collect, process, and store data increases, data engineers have started to rely on Python to meet this escalating demand. Why Python for Data Engineering?

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Top 8 Hadoop Projects to Work in 2024

Knowledge Hut

Imagine having a framework capable of handling large amounts of data with reliability, scalability, and cost-effectiveness. In this blog, we'll talk about intriguing and real-time sample Hadoop projects with source codes that can help you take your data analysis to the next level. Why Are Hadoop Projects So Important?

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The Art of Using Pyspark Joins For Data Analysis By Example

ProjectPro

Learn PySpark Joins in a single go! From the various type of PySpark joins to their syntax and PySpark join example, this blog has it all for you. Table of Contents Why are PySpark Joins Important for Data Analytics? Why are PySpark Joins Important for Data Analytics?