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Remote Data Science Jobs: 5 High-Demand Roles for Career Growth

Data Science Dojo

Strong analytical skills and the ability to work with large datasets are critical, as is familiarity with data modeling and ETL processes. Programming Questions Data science roles typically require knowledge of Python, SQL, R, or Hadoop. Prepare to discuss your experience and problem-solving abilities with these languages.

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How Rocket Companies modernized their data science solution on AWS

AWS Machine Learning Blog

Rockets legacy data science environment challenges Rockets previous data science solution was built around Apache Spark and combined the use of a legacy version of the Hadoop environment and vendor-provided Data Science Experience development tools. This also led to a backlog of data that needed to be ingested.

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Spark Vs. Hadoop – All You Need to Know

Pickl AI

Summary: This article compares Spark vs Hadoop, highlighting Spark’s fast, in-memory processing and Hadoop’s disk-based, batch processing model. Introduction Apache Spark and Hadoop are potent frameworks for big data processing and distributed computing. What is Apache Hadoop? What is Apache Spark?

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A Guide to Choose the Best Data Science Bootcamp

Data Science Dojo

Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud. Data Engineering : Building and maintaining data pipelines, ETL (Extract, Transform, Load) processes, and data warehousing.

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The Backbone of Data Engineering: 5 Key Architectural Patterns Explained

Mlearning.ai

ETL Design Pattern The ETL (Extract, Transform, Load) design pattern is a commonly used pattern in data engineering. ETL Design Pattern Here is an example of how the ETL design pattern can be used in a real-world scenario: A healthcare organization wants to analyze patient data to improve patient outcomes and operational efficiency.

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Apache Flink for all: Making Flink consumable across all areas of your business

IBM Journey to AI blog

Event-driven businesses across all industries thrive on real-time data, enabling companies to act on events as they happen rather than after the fact. This is where Apache Flink shines, offering a powerful solution to harness the full potential of an event-driven business model through efficient computing and processing capabilities.

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Data Version Control for Data Lakes: Handling the Changes in Large Scale

ODSC - Open Data Science

Cost-Efficiency By leveraging cost-effective storage solutions like the Hadoop Distributed File System (HDFS) or cloud-based storage, data lakes can handle large-scale data without incurring prohibitive costs. Interested in attending an ODSC event? Learn more about our upcoming events here.